C. Polle, S. Bosse, A.S. Herrmann, Damage Location Determination with Data Augmentation of Guided Ultrasonic Wave Features and Explainable Neural Network Approach for Integrated Sensor Systems, Computers 2024, 13, 32.
https://doi.org/10.3390/computers13020032 PublisherPDF
[j24.2]
S. Bosse, D. Lehmhus, S. Kumar, Automated Porosity Characterization for Aluminum Die Casting Materials Using X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning, Sensors 2024, 24, 2933.
https://doi.org/10.3390/s24092933 PublisherPDF
[j24.3]
S. Bosse, A Virtual Machine Platform Providing Machine Learning as a Programmable and Distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization, Algorithms. 2024; 17(8):356.
https://doi.org/10.3390/a17080356 PublisherPDF
[c24.1]
S. Bosse, Data-driven Parameterizable Generative Adversarial Networks for Synthetic Data Augmentation of Guided Ultrasonic Wave Sensor Signals, EWSHM 2024, 11 th European Workshop on Structural Health Monitoring, 10-13.6.2024, Potsdam,
Germany PublisherPDF
[c24.2]
C. Polle, S. Bosse, A Study on XANNs for Analyzing Failures in Guided Ultrasonic Wave-based Damage Localization, EWSHM 2024, 11 th European Workshop on Structural Health Monitoring, 10-13.6.2024, Potsdam,
Germany PublisherPDF
[c24.3]
Franck P. Vidal et al., CT simulations with gVXR as a useful tool for education, set-up of CT scans and scanner development, 18-22 August 2024, SPIE Optics + Photonics, San Diego,
USA PublisherPDF
Publications 2023
[b23.1]
Maurizio Valle, Dirk Lehmhus, Christian Gianoglio, Edoardo Ragusa, Lucia Seminara, Stefan Bosse, Ali Ibrahim, Klaus-Dieter Thoben, Advances in System-Integrated Intelligence, Proceedings of the 6th International Conference on System-Integrated Intelligence (SysInt 2022), September 7-9, 2022, Genova, Italy, Springer
2023 Publisher
[r23.1]
Stefan Bosse, Sarah Borneman, Björn Lüssem, Virtualization of low-resource Embedded Systems with a robust real-time capable and extensible Stack Virtual Machine REXAVM supporting Material-integrated Intelligent Systems and Tiny Machine Learning, arXiv:2302.09002,
2023 Paper PDFPublisher
[r23.2]
Stefan Bosse, Rule-based High-level Hardware-RTL Synthesis of Algorithms, Virtualizing Machines, and Communication Protocols with FPGAs based on Concurrent Communicating Sequential Processes and the ConPro Synthesis Framework, arXiv:2302.02959,
2023 Paper PDFPublisher
[r23.3]
Stefan Bosse, Dirk Lehmhus, Automated Detection of hidden Damages and Impurities in Aluminum Die Casting Materials and Fibre-Metal Laminates using Low-quality X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning, arXiv:2311.12041 [cs.CV],
2023 Paper PDFPublisher
[c23.1]
Stefan Bosse, Peter Krämer, Ereignisbasierte Verteilte Zustandsüberwachung und Schadenserkennung in großskaligen und komplexen Konstruktionen mit hybrider Multisensorfusion, DGZfP Schall Conference, 21-22.3.2023, Wetzlar,
2023 Paper PDFSlides PDF
[c23.2]
Stefan Bosse, Christoph Polle, Tiny Machine Learning Virtualization for IoT and Edge Computing using the REXA VM, The 10th International Conference on Future Internet of Things and Cloud (FiCloud 2023), Marrakesh, Marroco, IEEE Catalog Number: CFP23FIC-ART, ISBN: 979-8-3503-1635-3
2023 Paper PDFSlides PDF
[c23.3]
Stefan Bosse, Dirk Lehmhus, Detection of hidden Damages in Fibre Laminates using low-quality Transmission X-ray Imaging, X-ray Data Augmentation by Simulation, and Machine Learning,FEMS EUROMAT 2023, the 17 European Congress and Exhibition on Advanced Materials and Processes, on 03 - 07 September 2023 in Frankfurt am Main (Germany).
2023 Slides PDFAbstract PDFVideoPlay
Detection and characterisation of hidden damages in layered composites like Fibre laminates, e.g., Fibre Metal Laminates (FML), is still a challenge. Commonly, Guided Ultrasonic Waves (GUW) or X-ray imaging are used to detect hidden damages. X-ray imaging can be divided into two-dimensional transmission or reflection and three-dimensional tomography imaging using reconstruction algorithms to compute a three-dimensional view from slice images. Damages or defects can be classified roughly in layer delaminations, extended cracks, micro cracks (fibres and solid material layer), deformations, and impurities during manufacturing. Detection of such kind of damages and defects by visual inspection is a challenge, even using 3D CT data, and moreover using single 2D transmission images. For damage characterisation, micro-focus CT X-ray scanner are used, providing a high resolution below 100 μm, but with the disadvantage of high scanning times (up to several hours) [CHA22]. Anomaly detectors based on advanced data-driven Machine Learning methods can be used to mark Regions-of-Interest (ROI) in images automatically (feature selection process). ROI feature extraction is the first stage of an automated damage diagnostic system providing damage detection, classification, and localisation. But data-driven methods require typically a sufficient large set of training examples (with respect to diversity and generality), which cannot be provided commonly in engineering and damage diagnostics (e.g., an impact damage can only be "created" one time and is not reversible). In this work, the challenges, limits, and detection accuracy of automated ROI damage feature detection from low-quality and low-resolution 2D X-ray image data using data-driven anomaly detectors are investigated and evaluated. In addition to experimental data, X-ray simulation is used to create an augmented training and test data set. Simulated and experimental X-ray data are compared. The simulation is carried out with the gvirtualxray [GVX23;VID21] software It is based on the Beer-Lambert law to compute the absorption of light (i.e. photons) by 3D objects (here polygon meshes). Additionally, X-ray ray-tracing by the the x-ray projection simulator [DAC23] software is used for comparison. Suitable data-driven anomaly detectors estimating and marking the ROI candidates of damage areas are Convolutional Neural Networks trained supervised (i.e., using manually feature labelled data), either used as a pixel-based feature classifier (Point-Net) or as a region-based proposal network (Region-based CNN, R-CNN, Fast R-CNN, Region-proposal networks) [KHA18].The generated knowledge and the image data collected would further accelerate the development in the field of autonomous SHM of the composite structures which would further reduce the safety risks and total time associated with structural integrity assessment. References: [KHA18] S. Khan, H. Rahmani, S. A. A. Shah, and M. Bennamoun, A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool Publishers, 2018; [GVX23] gvirtualxray, https://gvirtualxray.fpvidal.net, accessed on-line on 24.1.2023; [VID21] F. P. Vidal, Introduction to X-ray simulation on GPU using gVirtualXRay, In Workshop on Image-Based Simulation for Industry 2021 (IBSim-4i 2020), London, UK, October, 2021; [DAC23] X-Ray Projection Simulator based on Raytracing, https://git.scc.kit.edu/dach/raytracingx-rayprojectionsimulator, access on-line on 24.1.2023; [CHA22] C. Shah, S. Bosse, and A. von Hehl, “Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics,” Materials, vol. 15, no. 13, p. 4645, Jul. 2022, doi: 10.3390/ma15134645.
[c23.4]
Dirk Lehmhus, Stefan Bosse, A.P. Mounchili, A. Struss, Stiffness-oriented Optimization of Material Distribution in Multi-Material Components, FEMS EUROMAT 2023, the 17 European Congress and Exhibition on Advanced Materials and Processes, on 03 - 07 September 2023 in Frankfurt am Main (Germany).
2023 Slides PDFAbstract PDF
Optimization of material distribution with the aim of maximizing stiffness is a common problem in engineering design aimed at structures offering low weight and/or limited design space, and several solutions are known [1]. In parallel, manufacturing techniques are being developed which allow realization of arbitrary material distribution in multi-material components: Typically, these focus on bi-material structures and are based either in casting, using a compound casting approach in which one material is introduced as insert around which the other is cast [2], or in additive manufacturing, where multi-material processes are increasingly being realized for several material classes, including polymers and metals [3,4]. The present study considers the Multi Phase Topology Optimization (MPTO) approach originially suggested by Burblies and Busse and further studied by Mounchili at al. [5,6]. This technique is based on iterative linear-elastic FEM simulations and the evaluation of strain energy data on model as well as element level and builds on the fact that association of high stiffness material properties with elements experiencing high levels of strain energy will serve to minimize total strain energy, and thus decrease displacement under a given load. The analysis covers (a) the realization of the MPTO approach based on different algorithms for adapting the material distribution and (b) its capability of identifying best combinations of low weight and high stiffness for a given load case and design space subject to variation of material volume fractions. Fundamental investigations on a completely stochastic and a genetic algorithm, with and without added integration of a physics-based sorting approach, are performed on a simple load case and limited model size to support fast optimization runs, thus allowing scrutiny of the scatter of results. The weight optimization problem is addressed using the same model of an asymmetric three-point bending setup incorporating equal fractions of three materials. A final validation is performed on a more complex model and load case with a the number of degrees of freedom increased by two orders of magnitude. The presentation closes with an outlook on further development paths, which include, on the computational side, pre-filtering of configurations to reduce the number of FEM simulations e.g. via integration of a machine learning-based predictor function, and on the materials engineering side the consideration of material interface characteristics as well as extensions towards incorporating aspects of plasticity. References [1] H. Z. Yu, S. R. Cross, C. A. Schuh Journal of Materials Science, 2017, 52, 4288-4298. [2] D. Schittenhelm, A. Burblies, M. Busse Forschung im Ingenieurwesen, 2018, 82, 131-147. [3] Y. Zheng, W. Zhang, D. M. Baca Lopez, R. Ahmad Polymers, 2021, 13, 1957. [4] A. Bandyopadhyay, B. Heer Mat. Sci. Eng. R: Reports, 2018, 129, 1-16. [5] A. Burblies, M. Busse Proceedings of the Multiscale & Functionally Graded Materials Conference (FGM), October 15th-18th, 2006, Honolulu (Hawaii, USA) [6] A.P. Mounchili, S. Bosse, D. Lehmhus, A. Struss MATEC Web of Conferences, 2021, 349, 03001.
[c23.5]
S. AL-Zaidawi, A. Tönjes, Stefan Bosse, Feature Characterisation of additively manufactured Implants made of Ti6Al4V using Hybrid Machine Learning Models, Measuring Data, and Process Parameters,FEMS EUROMAT 2023, the 17 European Congress and Exhibition on Advanced Materials and Processes, on 03 - 07 September 2023 in Frankfurt am Main (Germany).
2023 Abstract PDF
In the case of medical products, in particular implants, high demands are placed on the materials and their properties. With additively manufactured implants made of Ti6Al4V, porosity develops due to the process. In terms of process technology, this is kept to a level of less than one percent and is often almost completely closed by a connected thermo-mechanical after treatment, HIP for short (hot isostatic pressing). Thus, the end products have almost no recognizable porosity anymore. , There may be significant differences in the mechanical properties. In this case, especially with the dynamic loading of the components. Since a failure of implants causes a highly sensitive situation, the relationship between the process parameters in the manufacturing process and the mechanical properties must be investigated. Of particular interest is the influence of the final properties by the HIP. Using models of machine learning and image analysis, differences in the microstructure such as Melt trace size and shape, Grain size, Phase components, alpha and Beta, Phase characteristics (shape, size and position), Grain and phase orientation, as well as Pores [1] can be recognized, described and associated with the process parameters and mechanical properties. Here, samples in the as-built as well as in the hyped state are to be examined. Challenges exist in particular in the differentiation and identification of features in the very fine microstructure as well as the relatively small number of laboratory tests due to the experimental and preparation effort. An experimental design will be developed in cooperation between the data sciences and the materials sciences, which should lead to a continuous refinement of the models through an iterative procedure. Proposed data science and analysis methods and algorithms: Density-based clustering, CNN / Region-based CNN, F-RSN, Residual Neural Network, Autoencoder-based anomaly detectors, Numerical approaches, image transformations, clustering analysis. Formally, there is a model predictor function f(x):x -> y, with input x as measuring data from a set of experiments and specimens, and output y characterizing features of the manufactured material. The target features have relevant impact on the lifecycle and robustness of medical implants, which have to be identified, too. The input data is heterogeneous, but often it consists of images, therefore image analysis and object detection (with ROI) are fundamental pre-processing steps [2]. There is a large set of already available object detectors, e.g., coco-ssd. A major challenge in object detection is the complexity of ROi proposals with respect to model complexity and computational complexity. Using pure data-driven models, e.g.,coco-ssd, trained for environmental scene recognition, the ROI proposal and object detection in measuring images, e.g., from pore micrographs, will result in bad coverage and accuracy. For this reason, we will apply model-assisted object detectors, e.g., to find critical material pores, to identify cracks, and different material regions. References: [1] M. L. Altmann, S. Bosse, C. Werner, R. Fechte-Heinen, and A. Toenjes, Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem, Materials, vol. 15, no. 20, p. 7090, Oct. 2022, doi: 10.3390/ma15207090; [2] C. Shah, C., S. Bosse, A. von Hehl, Materials 2022, 15, 4645. https://doi.org/10.3390/ma15134645
[c23.6]
Stefan Bosse, IoT and Edge Computing using virtualized low-resource integer Machine Learning with support for CNN, ANN, and Decision Trees, IoT-ECAW, 18th Conference on Computer Science and Intelligence Systems FedCSIS 2023 (IEEE #57573), Warsaw, Poland, 17–20 September,
2023 PublisherPDF
[c23.7]
Stefan Bosse, Dirk Lehmhus, Automated X-ray-based damage detection and characterisation in composite materials by data-driven anomaly predictor models trained by a fusion of real and simulated X-ray data, 1st Workshop on ARTIFICIAL INTELLIGENCE IN MATERIALS SCIENCE AND ENGINEERING - AI MSE, Saarbrücken, Germany, 20–22 November,
2023 Abstract PDFSLIDES
S.M.K. Al-Zaidawi, S. Bosse, A Pore Classification System for the Detection of Additive Manufacturing Defects Combining Machine Learning and Numerical Image Analysis, Eng. Proc. 2023, 58, 122.
https://doi.org/10.3390/ecsa-10-16024 PublisherPublisher PDF
Publications 2022
[j22.3]
M. L. Altmann, S. Bosse, C. Werner, R. Fechte-Heinen, and A. Toenjes, Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem, Materials, vol. 15, no. 20, p. 7090, Oct. 2022, doi: 10.3390/ma15207090.
Available: http://dx.doi.org/10.3390/ma15207090 Paper PDFPublisher
In this Article, the targeted adjustment of the relative density of laser additive manufactured components made of AlSi10Mg is considered. The interest in demand-oriented process parameters is steadily increasing. Thus, shorter process times and lower unit costs can be achieved with decreasing component densities. Especially when hot isostatic pressing is considered as a post-processing step. In order to be able to generate process parameters automatically, a model hypothesis is learned via artificial neural networks (ANN) for a density range from 70% to almost 100%, based on a synthetic dataset with equally distributed process parameters and a statistical test series with 256 full factorial combined instances. This allows the achievable relative density to be predicted from given process parameters. Based on the best model, a database approach and supervised training of concatenated ANNs are developed to solve the inverse parameter prediction problem for a target density. In this way, it is possible to generate a parameter prediction model for the high-dimensional result space through constraints that are shown with synthetic test data sets. The presented concatenated ANN model is able to reproduce the origin distribution. The relative density of synthetic data can be predicted with an R2-value of 0.98. The mean build rate can be increased by 12% with the formulation of a hint during the backward model training. The application of the experimental data shows increased fuzziness related to the big data gaps and a small number of instances. For practical use, this algorithm could be trained on increased data sets and can be expanded by properties such as surface quality, residual stress, or mechanical strength. With knowledge of the necessary (mechanical) properties of the components, the model can be used to generate appropriate process parameters. This way, the processing time and the amount of scrap parts can be reduced.
[j22.2]
Chirag Shah, Stefan Bosse, and Axel von Hehl. Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics, Materials 15 (MDPI), no. 13 (2022): 4645.
Paper PDFPublisher
Due to the increasing use of the different composite materials in lightweight applications, such as in aerospace, it becomes crucial to understand the different damages occurring within them during life cycle and their possible inspection with different inspection techniques in different life cycle stages. A comprehensive classification of these damage patterns, measuring signals, and analysis methods using a taxonomical approach can help in this direction. In conjunction with the taxonomy, this work addresses damage diagnostics in hybrid and composite materials, such as fibre metal laminates (FMLs). A novel unified taxonomy atlas of damage patterns, measuring signals, and analysis methods is introduced. Analysis methods based on advanced supervised and unsupervised machine learning algorithms, such as autoencoders, self-organising maps, and convolutional neural networks, and a novel z-profiling method, are implemented. Besides formal aspects, an extended use case demonstrating damage identification in FML plates using X-ray computer tomography (X-ray CT) data is used to elaborate different data analysis techniques to amplify or detect damages and to show challenges.
[r22.2]
Stefan Bosse, JAM: The JavaScript Agent Machine for Distributed Computing and Simulation with reactive and mobile Multi-agent Systems - A Technical Report, arXiv:2207.11300
(2022) Paper PDFARXIV
Agent-based modelling (ABM), simulation (ABS), and distributed computation (ABC) are established methods. The Internet and Web-based technologies are suitable carriers. This paper is a technical report with some tutorial aspects of the JavaScript Agent Machine (JAM) platform and the programming of agents with AgentJS, a sub-set of the widely used JavaScript programming language for the programming of mobile state-based reactive agents. In addition to explaining the motivation for particular design choices and introducing core concepts of the architecture and the programming of agents in JavaScript, short examples illustrate the power of the JAM platform and its components for the deployment of large-scale multi-agent system in strong heterogeneous environments like the Internet. JAM is suitable for the deployment in strong heterogeneous and mobile environments. Finally, JAM can be used for ABC as well as for ABS in an unified methodology, finally enabling mobile crowd sensing coupled with simulation (ABS).
[j22.1]
S. Bosse, PSciLab: An Unified Distributed and Parallel Software Framework for Data Analysis, Simulation and Machine Learning—Design Practice, Software Architecture, and User Experience , Appl. Sci. 2022, 12(6), 2887;
10.3390/app12062887 Paper PDFPublisher
A hybrid distributed-parallel cluster software framework for heterogeneous computer networks is introduced, which supports simulation, data analysis, and Machine Learning (ML) using widely available JavaScript Virtual Machines (VM) and Web browsers to perform the working load. This work addresses parallelism primarily on control-path level and partially on data-path level targeting different classes of numerical problems that can be either data-partitioned or replicated. composed from a set of interacting worker processes that can be easily parallelised or distributed, e.g., for large-scale multi-element simulation or ML. The suitability and scalability for static- and dynamic sized problems is experimentally investigated with respect to the proposed multi-process and communication architecture and the data management using customised SQL data bases with network access. The framework consists of a set of tools and libraries, mainly the WorkBook (processed by a Web Browser) and the WorkShell (processed by node.js). It can be shown that the proposed distributed-parallel multi-process approach with a dedicated set of inter-process communication methods (message- and shared-memory-based) scales efficiently with the problem size and the number of processes. Finally, it is shown that the JavaScript-based approach can be easily used for exploiting parallelism by a typical numerical programmer and data analyst not requiring any special knowledge about parallel and distributed systems and their interaction. There is a focus on VM processing.
Stefan Bosse, Parth Kasundra, Robust Underwater Image Classification using Image Segmentation, CNN, and dynamic ROI Approximation, Proc. of the The 9th International Electronic Conference on Sensors and Applications, 1-5.10.2022, on-line,
(2022) Paper PDFPresentation VIDEOPresentation PDFPresentation HTMLConference
[c22.4]
Stefan Bosse, Wireless Agent-based Distributed Sensor Tuple Spaces using Bluetooth and IP Broadcasting, Proc. of the 17th CONFERENCE ON COMPUTER SCIENCE AND INTELLIGENCE SYSTEMS FedCSIS 2022, Sofia, Bulgaria, 4-7 September
(2022) Conference
[c22.3]
Chirag Shah, Stefan Bosse, Carolin Zinn, and Axel von Hehl, Optimization of Non-destructive Damage Detection of Hidden Damages in Fiber Metal Laminates Using X-ray Tomography andMachine Learning Algorithms, Proc. of the SysInt Conference, Sep. 6, 2022 to Sep. 8, 2022 - Genova, Italy, DOI: 10.1007/978-3-031-16281-7_37
(2022) Paper PDFConference
[c22.2]
Christoph Polle, Stefan Bosse, Michael Koerdt, Björn Maack, and Axel S. Herrmann, Fast Temperature-Compensated Method for Damage Detection and Structural Health Monitoring with Guided Ultrasonic Waves and Embedded Systems, Proc. of the SysInt Conference, Sep. 6, 2022 to Sep. 8, 2022 - Genova, Italy, DOI: 10.1007/978-3-031-16281-7_35
(2022) Paper PDFConference
[c22.1]
Stefan Bosse, Fusion of Distributed Sensor Tuple Spaces and Agents using Broadcast Radio Communication for Mobile Networks, Proc. of the IARIA Mobility Conference, June 26, 2022 to June 30, 2022 - Porto, Portugal,
(2022) Paper PDFPresentation VIDEOPresentation SLIDESConference
Short-time and short-range device-to-device and device-to-service communication in ad-hoc mobile networks is a challenge. A prominent example of such a mobile device is the smartphone carried by users with a typical speed of 1m/s. Most Internet-of-Things (IoT) devices and smart sensors are connected via the Internet. Although, Internet access is widely available, there are places that are not covered by wireless IP networks, IP networks are not suitable for ad-hoc short-time and short-range communication, and the spatial context is not (accurately) considered by Internet connectivity. In this work, devices communicate connectionless and ad-hoc by using low-energy Bluetooth broadcast messaging available in any smartphone and in most embedded computers. Bi-directional connectionless communication is established via parallel usage of the advertisement and scanning modes by exchanging data tuples. The communication is performed via a tuple space service on each node. Tuple space access is performed by simple event-based agents. Mobile devices can act as tuple carriers that can carry tuples between different locations. Additionally, UDP-based Intranet communication can be used to access tuple spaces on a larger spatial range. The Bluetooth Low Energy Tuple Space (BeeTS) service enables opportunistic, ad-hoc and loosely coupled device communication with a spatial context.
[r22.1]
Stefan Bosse, BeeTS: Smart Distributed Sensor Tuple Spaces combined with Agents using Bluetooth and IP Broadcasting, CoRR abs/2204.02464
(2022) ARXIV
Most Internet-of-Things (IoT) devices and smart sensors are connected via the Internet using IP communication driectly accessed by a server that collect sensor information periodically or event-based. Although, Internet access is widely available, there are places that are not covered and WLAN and mobile cell communication requires a descent amount of power not always available. Finally, the spatial context (the environment in which the sensor or devices is situated) is not considered (or lost) by Internet connectivity. In this work, smart devices communicate connectionless and ad-hoc by using low-energy Bluetooth broadcasting available in any smartphone and in most embedded computers, e.g., the Raspberry PI devices. Bi-directional connectionless communication is established via the advertisements and scanning modes. The communication nodes can exchange data via functional tuples using a tuple space service on each node. Tuple space access is performed by simple evenat-based agents. Mobile devices act as tuple carriers that can carry tuples between different locations. Additionally, UDP-based Intranet communication can be used to access tuple spaces on a wider spatial range. The Bluetooth Low Energy Tuple Space (BeeTS) service enables opportunistic, ad-hoc and loosely coupled device communication with a spatial context.
[b22.4]
M. Valle, S. Bosse, D. Lehmhus et al. (Ed.), Advances in System-Integrated Intelligence: Proceedings of the 6th
International Conference on System-Integrated Intelligence (SysInt 2022), Springer, 2022, ISBN 978-3031162800
[b22.3]
Stefan Bosse, Crowdsourcing and Simulation with Mobile Agents and the JavaScript Agent Machine, Lulu.com,
2022, ISBN 978-1471078132 PublisherEPUB Online
[b22.2]
S. Bosse, Large-scale agent-based simulation and crowd sensing with mobile agents, in: Handbook of computational social science ; Volume 2: Data science, statistical modelling, and machine learning methods, Editors U. Engel et al., 2022, Routledge
ISBN 9781032111391 DOI Chapter PDFPublisher
[b22.1]
S. Bosse, L. Dahlhaus, U. Engel, Web data mining: collecting textual data from web pages using R,
in: Handbook of computational social science ; Volume 2: Data science, statistical modelling, and machine learning methods, Editors U. Engel et al., 2022, Routledge
ISBN 9781032111391 DOI Chapter PDFPublisher
Publications 2021
[j21.1]
S. Bosse, D. Weiss, D. Schmidt, Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study, Computers 2021, 10(3), 34;
doi:10.3390/computers10030034 Paper PDFPublisher
Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties like damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free Distributed Machine Learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent Artificial Neural Networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.
[c21.1]
S. Bosse, Distributed Serverless Chat Bot Networks using mobile Agents: A Distributed Data Base Model for Social Networking and Data Analytics, 13th International Conference on Agents and Artificial Intelligence (ICAART),
Online, Worldwide, 4-6-2.2021 Paper PDFPresentation VIDEOPresentation SLIDESConference
Today human-machine dialogues performed and moderated by chat bots are ubiquitous. Commonly, centralised and server-based chat bot software is used to implement rule-based and intelligent dialogue robots. Furthermore, human networking is not supported. Rule-based chat bots typically implement an interface to a knowledge data base in a more natural way. The dialogue topics are narrowed and static. Intelligent chat bots aim to improve dialogues and conversational quality over time and user experience. In this work, mobile agents are used to implement a distributed, decentralised, serverless dialogue robot network that enables ad-hoc communication between humans and machines (networks) and between human groups via the chat bot network (supporting personalized and mass communication). I.e., the chat bot networks aims to extend the communication and social interaction range of humans, especially in mobile environments, by a distributed knowledge and data base approach. Additionally, the chat bot network is a sensor data acquisition and data aggregator system enabling large-scale crowd-based analytics. A first proof-of-concept demonstrator is shown identifying the challenges arising with self-organising distributed chat bot networks in resource-constrained mobile networks. The novelty of this work is a hybrid chat bot multi-agent architecture enabling scalable distributed and adaptive communicating chat bot networks.
[c21.2]
S. Bosse, Parallel and Distributed Agent-based Simulation of large-scale socio-technical Systems with loosely coupled Virtual Machines, Proc. of the SIMULTECH Conference 2021, International Conference on Simulation and Modeling Methodologies, Technologies and Applications,
7-9.7.2021, Online
Paper PDFPresentation VIDEOPresentation SLIDESConference
Abstract. Agent-based systems are inherently distributed and parallel by a distributed memory model, but agent-based simulation is often characterised by a shared memory model. This paper discusses the challenges of and solution for large-scale distributed agent-based simulation using virtual machines. Simulation of large-scale multi-agent systems with more than 10000 agents on a single processor node requires high computational times that can be far beyond the constraints set by the users, e.g., in real-time capable simulations. Parallel and distributed simulation involves the transformation of shared to a communication-based distributed memory model that can create a significant communication overhead. In this work, instead distributing an originally monolithic simulator with visualisation, a loosely coupled distributed agent process platform cluster network performing the agent processing for simulation is monitored by a visualisation and simulation control service. A typical use case of traffic simulation in smart city context is used for evaluation the performance of the proposed DSEJAMON architecture.
[c21.3]
D. Lehmhus, S. Bosse, A. Mounchili, A. Struß, Putting Stiffness where it’s needed: Optimizing The Mechanical Response of Multi-Material Structures, ICEAF VI - 6th International Conference of Engineering Against Failure,
22-25-6-2021 Presentation SLIDESConference
Modern manufacturing processes like multi-material additive manufacturing or, to a lesser degree, compound casting, allow almost arbitrary distribution of different materials, or, for that matter, different density levels, over a component‘s volume. The difficulty lies in the optimal spatial material distribution. Multi-Phase Topology Optimization (MPTO) is one approach towards this end. This method relies on iterative, linear-elastic FEM simulations which provide element- as well as part-level data on elastic strain energy. This information is used to redistribute predefined material fractions characterized by different values of Young’s Modulus according to their relative properties in order to minimize the total strain energy under a given design load. Solving such a minimization problem is the central part of this work. Achieving this aim means that a configuration has been identified which provides maximum stiffness. This said, the present study compares different material redistribution and optimization techniques based on genetic algorithms and simulated annealing and compares them in terms of their optimization results, applicability, relative performance and scalability. Specifically, unconstrained (randomized) model-free approaches using Monte Carlo methods are contrasted to others incorporating physically or technically justified constraints limiting the configuration space during the re-association of materials properties to the individual finite elements. Typically, the minimization problems delivers a set of solutions. Iterative minimization algorithms tend to settle in local non-optimal minimum states. Evolutionary as well as simulated annealing deploys partial randomization for the generation of new configurations, a key methodology to explore the search space in a much larger volume than classical gradient-based algorithms do. In contrast to simulated annealing, genetic algorithms construct new material configuration from previous solutions. The cost functions used by both approaches depends on FEM simulation, a computational intensive task. To minimize FEM simulation and cost function calculation, approximating caching and configuration pre-analysis/selection using constrain models are introduced.
Typical data surveys as human-centred data source reflect only snapshots of dynamical systems on the time-scale. Commonly, in social science surveys are performed in participatory way and by well designed (static) surveys. But crowd sensing gains attraction to collect either supplementary data or aiming to replace traditional survey formats moving towards ad-hoc opportunistic micro-surveys [1]. The data quality of such crowd sourced data is varying and often questionable with high bias and missing values [2]. Ubiquitous and mobile devices gain attraction as data sources with a high spatial and temporal coverage, e.g., smart phones. Continuous sampling of data streams can improve quality of statistical data analysis, generalisation of predictive modelling, and simulation significantly. We present an unified agent-based data collection, aggregation, analysis, and tightly coupled simulation methodology, providing valuable contribution to Computational Social Science (CSS), at least theoretically. Mobile computational agents (mobile software processes) are used for self-organising data collection and aggregation by using machine data and user data via scriptable dialogues. This approach extends the data collection process in the spatial and temporal domain providing a high data coverage and quality, required, e.g., by accurate ML methods. The issues and challenges of long-term self-organising mobile crowd sensing are discussed and analysed with some practical demonstrations in comparison with theoretical expectations.
[c21.5]
S. Bosse, D. Schmidt, Ortsaufgelöste Schadensdiagnostik mit geführten Wellen und zustandsbasierten Modellen mit Modellfusion für Faserverbundwerkstoffe, Proc. of the DAGA 2021 - 47. Jahrestagung für Akustik,
15. - 18. August 2021, Wien & Online Paper PDFPresentation VIDEOPresentation SLIDESConference
[c21.6]
S. Bosse, Surrogate Predictive and Multi-domain Modelling of Complex Systems by fusion of Agent-based Simulation, Cellular Automata, and Machine Learning, Proc. of the SIMUL 2021 Conference, The Thirteenth International Conference on Advances in System Simulation, IARIA,
3. - 7. October 2021, Barcelona, Spain, & Online Paper PDFPresentation VIDEOPresentation HTMLPresentation PDFPresentation EPUBConference
Modelling of complex dynamic systems like pandemic outbreaks or traffic flows in cities on macro-level is difficult due to a high variance on entity micro-level and unknown or incomplete interaction models. Agent-based and Cellular Automata (CA) simulations based on micro-level modelling can be used to investigate the outcome of system observables in a sandbox. For a reasonable accuracy a high number of agents, sufficient behaviour variance, high computational times, and calibrated model parameters are required. Surrogate predictive modelling of the multi-agent system can be used to replace time-consuming simulations. In this work we present a hybrid aprroach combining Agent-based Simulation, probabilistic contextual CA, and Machine Learning (ML). We investigate the replacement of the ABS-CA by surrogate ML models trained by simulation data. The predictive model is state-based and applied to time-series data to predict future development of aggregated system observables. We discuss and show the negative impact of uncalibrated real-world sensor data on time-series prediction and an improvement by surrogate modelling of simulation. A use-case of pandemic simulation using real-world statistical data is used to investigate and evaluate the suitability and accuracy of the proposed methods and to show the high sensitivity of surrogate modelling on distorted and biased data.
[c21.7]
S. Bosse, C. Polle, Spatial Damage Prediction in Composite Materials using Multipath Ultrasonic Monitoring, advanced Signal Feature Selection and combined Classifier-Regression Artificial Neural Network, The 8th International Electronic Conference on Sensors and Applications (ECSA), in Engineering Proceedings, MDPI,
Online, Worldwide, 1-15-10.2021 Paper PDFPresentation VIDEOPresentation SLIDESProceedings
Automated damage detection in Carbon-Fibre and Fibre Metal Laminates is still a challenge. Impact damages are typically not visible from the outside. Different measuring and analysis methods are available to detect hidden damages, e.g., delaminations or cracks. Examples are X-ray computer tomography and methods based on guided ultrasonic waves (GUW). All measuring techniques are characterised by a high-dimensional sensor data, in the case of GUW that is a set of time-resolved signals as a response to a actuated stimulus. We present a simple but powerful two-level method that reduces the input data (time-resolved sensor signals) significantly by a signal feature selection computation finally applied to a damage predictor function. Beside multi-path sensing and analysis, the novelty of this work is a feed-forward ANN posing low complexity and that is used to implement the predictor function that combines a classifier and a spatial regression model.
Publications 2020
[c20.1]
S. Bosse, Learning Damage Event Discriminator Functions with Distributed Multi-instance RNN/LSTM Machine Learning - Mastering the Challenge, Proc. of the 5th International Conference on System-Integrated Intelligence Conference, 11.11-13.11.2020,
Bremen, Germany, 2020 Paper PDFPresentation HTMLPresentation Video
Common Structural Health Monitoring systems are used to detect past damages occurred in structures with sensor networks and external sensor data processing. The time of the damage creation event is commonly unknown. Numerical methods and Machine Learning are used to extract relevant damage information from sensor signals that is characterised by a high data volume and dimension. In this work, distributed multi-instance learning applied to raw time-series of sensor data is deployed to predict the event of the occurrence of a hidden damage in a mechanical structure using typical vibrations of the structure. The sensor processing and learning is performed locally on sensor node level with a global fusion of prediction results to estimate the damage location and the time of the damage creation. Recurrent neural networks with a long-short-term memory architecture are considered implementing a damage discriminator function. The sensor data required for the evaluation of the proposed approach is generated by a multi-body physics simulation approximating material properties.
[c20.2]
S. Bosse, Self-organising Urban Traffic control on micro-level using Reinforcement Learning and Agent-based Modelling, Proc. of the SAI IntelliSys Conference, 3-4.9.2020,
Amsterdam, Netherlands, 2020 Paper PDF
[c20.3]
S. Bosse, Self-adaptive Traffic and Logistics Flow Control using Learning Agents and Ubiquitous Sensors, Proc. of the 5th International Conference on System-Integrated Intelligence Conference, 11.11-13.11.2020,
Bremen, Germany, 2020 Paper PDFPresentation HTMLPresentation Video
Traffic flow optimisation is a distributed complex problem. Traditional traffic and logistics flow control algorithms operate on a system level and address mostly switching cycle adaptation of traffic signals and lights. This work addresses traffic flow optimisation by self-adaptive micro-level control by combining Reinforcement Learning and rule-based agent models for action selection with a new hybrid agent architecture. I.e., long-range routing is performed by agents that adapt their decision making for re-routing on local environmental sensors. Agent-based modelling and simulation are used to study emergence effects on urban city traffic flows with learning agents. The approach and the proposed agent architecture can be generalised and applied to a broader range of application fields, e.g., logistics and general transport phenomena.
[c20.4]
S. Bosse, E. Kalwait, Fatigue and damage diagnostics with predictor functions for new advanced materials by Machine Learning, MAPEX SpaceMat 2020 Symposium 31.8 -1.9.2020,
Bremen, Germany Paper PDFPoster PDF
There is an emerging field of new materials highly related to space applications like fibre-metal laminates (DFG FOR3022). Typically, material properties are determined from tensile tests. We investigate approximating predictor functions by Machine Learning (ML) for inelastic and fatigue prediction by history data measured from simple tensile tests within the elastic range of the material. We show some preliminary results from a broad range of materials and outline the challenges to derive such predictor functions by ML.
[c20.5]
S. Bosse, E. Kalwait, Damage and Material-state Diagnostics with Predictor Functions using Data Series Prediction and Artificial Neural Networks, ECSA 2020 MDPI, 15.11 -30.11.2020,
Basel, Switzerland Paper PDFPresentation HTMLPresentation Video
There is an emerging field of new materials highly related to space applications like fibre-metal laminates (DFG FOR3022). Typically, material properties are determined from tensile tests. We investigate approximating predictor functions by Machine Learning (ML) for inelastic and fatigue prediction by history data measured from simple tensile tests within the elastic range of the material. We show some preliminary results from a broad range of materials and outline the challenges to derive such predictor functions by ML.
[p20.1]
U. Engel, S. Bosse, et al., Blick in die Zukunft: Wie künstliche Intelligenz das Leben verändern wird Ergebnisse eines Umfrageprojekts in der Wissenschaft, Politik und Bevölkerung der Freien Hansestadt Bremen, Universität Bremen, Methodenzentrum,
Februar 2020, Bericht Report PDF
Die Freie Hansestadt Bremen ist ein bedeutender Wissenschafts- und Wirtschaftsstandort in Deutschland und für ihre Bürgerinnen und Bürger eine ebenso bedeutende Stadt zum Leben. Wir möchten einen Blick in die Zukunft wagen und fragen, wie sich diese Zukunft aus Sicht der in Bremen beheimateten Wissenschaft und Politik und der Bremer Bevölkerung darstellt. Auch bitten wir die in Bremen ansässigen Medien um Mitwirkung an dieser Studie.
[p20.2]
S. Bosse, Anwendung von Maschinellen Lernalgorithmen auf SHM/NDT Daten. Eingeladener Überblicksvortrag mit praktischen Erfahrungen und Anwendung im Rahmen der DFG Forschungsgruppe 3022, Deutsche Gesellschaft für zerstörungsfreie Prüfung,
Fachausschusssitzung SHM, Dresden, 30.9.2020 Script PDFPresentation HTML
In diesem Vortrag werden die Möglichkeiten von verteilten Multiinstanzlernen für SHM Anwendungen an Beispielen der Dehnungs- und Ultraschallsensorik gezeigt und analysiert. Dabei liegt ein Fokus auf den Einsatz auf Systemen mit eingeschränkten Ressourcen und Robustheit. Dabei werden neben Neuronalen Netzen und Deep Learning auch Entscheidungsbaumlerner diskutiert.
Publications 2019
[j19.1]
S. Bosse, D. Lehmhus, Material-integrated cluster computing in self-adaptive robotic materials using mobile multi-agent systems, Cluster Computing, doi 10.1007/s10586-018-02894-x, Volume 22, Number 3, pp. 1017-1037, 2019
ISSN 1386-7857 PublisherPaper OnlinePaper PDF
Recent trends like internet-of-things (IoT) and internet-of-everything (IoE) require new distributed computing and com- munication approaches as size of interconnected devices moves from a cm3 - to the sub-mm3 -scale. Technological advance behind size reduction will facilitate integration of networked computing on material rather than structural level, requiring algorithmic and architectural scaling towards distributed computing. Associated challenges are linked to use of low reliability, large scale computer networks operating on low to very low resources in robotic materials capable of per- forming cluster computing on micro-scale. Networks of this type need superior robustness to cope with harsh conditions of operation. These can be provided by self-organization and -adaptivity. On macro scale, robotic materials afford unified distributed data processing models to allow their connection to smart environments like IoT/IoE. The present study addresses these challenges by applying mobile Multi-agent systems (MAS) and an advanced JavaScript agent processing platform (JAM), realizing self-adaptivity as feature of both data processing and the mechanical system itself. The MAS’ task is to solve a distributed optimization problem using a mechanically adaptive robotic material in which stiffness is increased via minimization of elastic energy. A practical realization of this example necessitates environmental interaction and perception, demonstrated here via a reference architecture employing a decentralized approach to control local property change in service based on identification of the loading situation. In robotic materials, such capabilities can support actuation and/or lightweight design, and thus sustainability.
[j19.2]
S. Bosse, Modellierung und Simulation komplexer Systeme mit annotiertem JavaScript, Industrie 4.0 Management, Intelligente vernetzte Systeme, 1.2019, GITO Verlag,
ISSN 2364-9208 PublisherPaper PDF
Der Entwurf und die Simulation komplexer mechatronischer und verteilter intelligenter Systeme erfordern eine einheitliche Systemmodellierungs- und Programmiersprache. Dieser Beitrag stellt JavaScript als eine vereinheitlichte Modellierungs- und Programmiersprache vor, indem JavaScript mit einem semantischen Typsystem JST erweitert wird, um die Lücke zwischen Modellen und Implementierungen zu schließen. Daraus resultiert die JS+ SupersetSprache, die Typisierung, Modellierung und Programmierung kombiniert. Es werden verschiedene Modelldomänen und ihre Beziehung zum JS+ Programmierungsmodell einschließlich einiger generischer Transformationsregeln am Beispiel eines sensorischen Materials gezeigt. Schließlich wird das Multidomain Simulationswerkzeug SEJAM eingeführt, das physikalische und datenverarbeitende Simulation mit Agenten kombiniert.
[j19.3]
S. Bosse, U. Engel, Real-time Human-in-the-loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities, Sensors (MDPI), 2019,
doi: 10.3390/s19204356 PublisherPaper OnlinePaper PDF
Modelling and simulation of social interaction and networks is of high interest in multiple disciplines and fields of application ranging from fundamental social science to smart city management. Future smart city infrastructures and management is characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, i.e., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds pose higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by extending simulation worlds with real world interaction and vice-versa. The simulation world interacts with real world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, light) of mobile devices like smart phones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smart phone Apps or integrated in WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and creating a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact of social interaction, mobility, and opinion making, which has to be considered, too. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of challenges and methodologies to study and control large-scale and complex socio-technical systems using agent-based methods.
[c19.1]
S. Bosse, U. Engel, Combining Crowd Sensing and Social Data Mining with Agent-based Simulation using Mobile Agents towards Augmented Virtuality, Proc. of the Social Simulation Conference, 24-27.9.2019,
Mainz, Germany Paper PDFPresentation HTMLThe Agent Laboratory: Demonstration
Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information. In contrast, simulation worlds are commonly closed and rely on artificial social behaviour and synthetic sensory information generated by the simulator program or using data collected off-line by surveys. Agent-based modelling used for investigation and evaluation of social interaction and networking relies on parameterisable models. Finding accurate and representative parameter settings can be a challenge. In this work, a new simulation paradigm is introduced, providing augmented virtuality by coupling crowd sensing and social data mining with simulation worlds by using mobile agents in an unified way. A simple social network analysis case-study based on the Sakoda social interaction model and mobile crowd sensing demonstrate the capabilities of the new hybrid simulation method.
[c19.2]
S. Bosse, D. Lehmhus, Robust detection of hidden material damages using low-cost external sensors and Machine Learning, 6th International Electronic Conference on Sensors and Applications (ECSA), 15-30 Nov. 2019,
MDPI Paper PDFPresentation HTMLPublisher
Machine Learning (ML) techniques are widely used in Structural Health Monitoring (SHM) and Non-destructive Testing (NDT), but the learning process, the learned models. and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damages in materials monitored using low-cost strain sensors. The investigation is performed using a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.
[p19.1]
S. Bosse, Smarte Adaptive Materialien und Agenten, Invited Talk, AWT - VDI - Arbeitskreis Werkstofftechnik Bremen 2018/19, 06.03. 2019, Leibniz-Institut für Werkstofforientierte Technologien - IWT,
Bremen, Germany Presentation PDFPresentation HTML
Die Sensorierung von Materialien und Strukturen hin zu smarten sensorischen Materialien schreitet durch den technologischen Fortschritt immer weiter voran. Smarte sensorische Materialien bedeuten materialintegrierte Sensornetzwerke, die ganz neue Anforderungen an die verteilte Datenverarbeitung und Kommunikation stellen. Werden diese sensorische Materialien, die intrinsische und extrinsische Perzeption ermöglichen, durch integrierte Aktoren (z.B. Thermoplaste) erweitert entstehen smarte adaptive Materialien. Diese smarten adaptiven Materialien können auf veränderte Umgebungsbedingungen (wie z.B. Lastsituationen) oder Schäden reaktiv ihre Material- und Struktureigenschaften derart ändern dass die Verteilung von mechanischen Größen (Dehnung, Spannung, Kräfte, usw.) für die aktuelle Situation optimiert werden kann. Dazu wird ein Paradigma der verteilten Datenverarbeitung aus der Informatik eingesetzt und vorgestellt: Reaktive Multiagentensysteme. Diese kooperierenden Agenten sollen selbstorganisierend und möglichst autonom die Struktur hinsichtlich einer Zielgröße (z.B. minimale mechanische Energie) bei veränderlichen Lastsituationen optimieren.
[p19.2]
U. Engel, S. Bosse, et al., Ein Delphi-Survey über KI und das Zusammenleben von Menschen und Robotern in der digitalisierten Welt von Morgen, Projektbroschüre, published in Oct. 2019, Bremen,
Universität Bremen, Methodenzentrum, Germany Report PDF
Die Freie Hansestadt Bremen ist ein bedeutender Wissenschafts- und Wirtschaftsstandort in Deutschland und für ihre Bürgerinnen und Bürger eine ebenso bedeutende Stadt zum Leben. Wir möchten einen Blick in die Zukunft wagen und fragen, wie sich diese Zukunft aus Sicht der in Bremen beheimateten Wissenschaft und Politik und der Bremer Bevölkerung darstellt. Auch bitten wir die in Bremen ansässigen Medien um Mitwirkung an dieser Studie.
Publications 2018
[j18.1]
S. Bosse, D. Lehmhus, Adaptive Materialien mit Multigatentensystemen, Industrie 4.0 Management, 4.2018, GITO Verlag,
ISSN 2364-9208 PublisherPaper PDF
Tragende Strukturen werden typischerweise in Bezug auf relevante Lastfälle entworfen, wobei statische Formen und vorgegebene Materialeigenschaften angenommen werden, die während des Entwurfs und der Materialauswahl ausgewählt werden. Neue Technologien, die das Design von Strukturen ermöglichen, die lokale Eigenschaften im Betrieb als Reaktion auf Lastwechsel verändern könnten, würden zusätzliche Gewichtsersparnispotenziale schaffen und somit Leichtbau und Nach- haltigkeit unterstützen. Materialien mit solchen Fähigkeiten bestehen aus Netzwerken mit zahlreichen aktiven Zellen, die eine Erfassungs-, Signal- und Datenverarbeitungs-, Kommunikations- und Aktuierungs-/Stimulationsfähigkeit bereitstellen, die adaptronische Strukturen bilden. Ein Beispiel für ein solches Material ist eine spezielle Klasse von Polymeren, die in der Lage sind, ihre Elastizität basierend auf dem Einfluss von optischen, thermischen oder elektrischen Feldern zu ändern. Ein zu lösendes Problem in Bezug auf aktive intelligente zellulare Strukturen ist die korrelierte und selbstorganisierende Steuerung der Reaktion und Steuerung von Zellen und die zugrundeliegende Informationsorganisation, die Robustheit und Echtzeitfähigkeiten bereitstellen muss. Wir schlagen einen hybriden Ansatz vor, der mobile und reaktive selbstorganisierende Multi-Agenten-Systeme (MAS) und Maschinelles Lernen kombiniert. Die MAS stellen die wesentliche robuste Informations- und Kommunikationstechnologie (IKT) dar. Die Agenten werden dabei in Material-integrierten Netzwerken aus Mikrorechnern ausgeführt. Die Simulation und Umsetzung solcher komplexen Systeme stellt eine große Herausforderung dar.
[b18.1]
S. Bosse, D. Lehmhus, W. Lang, M. Busse (Ed.), Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) PublisherOnline Library
1. Introduction 1.1 On Concepts and Challenges of Realizing Material-integrated Intelligent Systems 2. System Development 2.1 Design Methodology for Intelligent Technical Systems 2.2 Smart Systems Design Methodologies and Tools 3. Sensor Technologies 3.1 Microelectromechanical Systems (MEMS) 3.2 Fiber-optic sensors 3.3 Electronics Development for Integration 4. Material Integration Solutions 4.1 Sensor Integration in Fibre Reinforced Polymers 4.2 Sensor Integration in Sheet Metal Structures 4.3 Sensor and Electronics Integration in Additive Manufacturing 5. Signal and data processing: The Sensor Node Level 5.1 Analogue Sensor Signal Processing and Analog-to-Digital Conversion 5.2 Digital real-time Data Processing with Embedded Systems 5.3 The Known World - Model-based Computing and Inverse Numerics 5.4 The Unknown World - Model-free Computing and Machine Learning 5.5 Robustness and Data Fusion 6. Networking and Communication: The Sensor Network Level 6.1 Communication Hardware 6.2 Networks and Communication Protocols 6.3 Distributed and Cloud Computing: The Big Machine 6.4 The Mobile Agent and Multi-Agent Systems 7. Energy Supply 7.1 Energy Management and Distribution 7.2 Micro-energy Storage 7.3 Energy Harvesting 8. Application Scenarios 8.1 Structural Health Monitoring (SHM) 8.2 Achievements and Open Issues Towards Embedding Tactile Sensing and Interpretation into Electronic Skin Systems 8.3 Intelligent Materials in Machine Tool Applications - a review 8.4 New Markets/Opportunities through availability of Product Life Cycle Data 8.5 Human-Computer Interaction with Novel and Advanced Materials
[b18.2]
S. Bosse, Chapter Networks and Communication Protocols, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.3]
S. Bosse, Chapter Distributed and Cloud Computing: The Big Machine, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.4]
S. Bosse, Chapter The Mobile Agent and Multi-Agent Systems, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.5]
J. Horstmann, S. Bosse, Chapter Analog Sensor Signal Processing and Analog-to-Digital Conversion, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.6]
S. Bosse, D. Lehmhus, Chapter Digital Real-Time Data Processing with Embedded Systems, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.7]
S. Bosse, Chapter The Unknown World: Model-free Computing and Machine Learning, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.8]
S. Bosse, Chapter Robustness and Data Fusion, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.9]
S. Bosse, T. Behrmann, Chapter Energy Management and Distribution , in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.10]
A. Lechleiter, S. Bosse, Chapter The Known World: Model-based Computing and Inverse Numeric, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley,
ISBN: 978-3-527-33606-7 (2018) Manuscript PDF
[b18.11]
S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems: Models, Platforms, and Technological Aspects,
ISBN 9783746752228 (Hardcover), ISBN 9783746759470 (Softcover), epubli, 2018 Sample PDF
This work addresses the challenge of unified and distributed computing in strong heterogeneous environments ranging from Sensor Networks to Internet Clouds by using Mobile Multi-Agent Systems. A unified agent behaviour model, agent processing platform architecture, and synthesis framework should close the operational gap between low-resource data processing units, for example, single microchips embedded in materials, mobile devices, and generic computers including servers. Robustness, Scalability, Self organization, Reconfiguration and Adaptivity including Learning are major cornerstones. The range of fields of application is not limited: Sensor Data Processing, Load monitoring of technical structures, Structural Health Monitoring, Energy Management, Distributed Computing, Distributed Databases and Search, Automated Design, Cloud-based Manufacturing, and many more. This work touches various topics to reach the ambitious goal of unified smart and distributed computing and contributing to the design of intelligent sensing systems: Multi-Agent Systems, Agent Processing Platforms, System-on-Chip Designs, Architectural and Algorithmic Scaling, High-level Synthesis, Agent Programming Models and Languages, Self-organizing Systems, Numerical and AI Algorithms, Energy Management, Distributed Sensor Networks, and multi-domain simulation techniques. None of these topics may be considered standalone. Only a balanced composition of all topics can meet the requirements in future computing networks, for example, the Internet-of-Things with billions of heterogeneous devices. Smart can be defined on different operational and processing levels and having different goals in mind. One aspect is the adaptivity and reliability in the presence of sensor, communication, node, and network failures that should not compromise the trust and quality of the computed information, for example, the output of a Structural Health Monitoring System. A Smart System can be considered on node, network, and network of network level. Another aspect of "smartness" is information processing with inaccurate or incomplete models (mechanical, technical, physical) requiring machine learning approaches, either supervised with training at design-time or unsupervised based on reward learning at run-time. Some examples of Self-organizing and Adaptive Systems are given in this work, for example, distributed feature recognition and event-based sensor processing.
[c18.1]
S. Bosse, Autonome und robuste Datenanalyse mit Maschinellen Lernen und KI in der Schadensprüfung und Überwachung, DGM Workshop FA Hybride Werkstoffe und Strukturen mit dem AK Mischverbindungen im FA Aluminium,
Dortmund, 19-20.2.2018 Paper PDFPresentation HTML
[c18.2]
S. Bosse, D. Lehmhus, Computing within Materials: Self-Adaptive Materials and Self-organizing Agents, Smart Systems Integration conference,
11-12.4.2018, Dresden, Germany,
ISBN 9781510867710 Paper PDFPresentation HTML
Materials Informatics addresses commonly the design of new materials using advanced algorithms and methods from computer science like Machine Learning and Data Mining. Ongoing miniaturization of computers down to the micro-scale-level enables the integration of computing in structures and materials that can be understand as Materials Informatics from another point of view. There are two major application classes: Smart Sensorial Materials and Smart Adaptive Materials. The latter class is considered in this work by combining self-organizing and adaptive Multi-agent Systems with materials posing changeable material properties like stiffness by actuators. It is assumed that the computational part of this micro-scale Cyber-Physical-System is entirely integrated in the material or structure as a distributed computer composed of a network of low-resource computers. Each node is connected to sensors and actuators. Actually only macroscopic systems can be realized. Therefore a multi-domain simulation combining computational and physical simulation is used to demonstrate the approach and to evaluate self-adaptive algorithms.
[c18.3]
S. Bosse, M. Koerdt, A. v. Hehl, Robust and Adaptive Non Destructive Testing of Hybrids with Guided Waves and Learning Agents, 3. Internationale Konferenz Hybrid Materials and Structures 2018,
18-19.4.2018, Bremen, Germany Paper PDFPresentation HTML
Monitoring of mechanical structures is a Big Data challenge concerning Structural Health Monitoring and Non-destructive Testing. The sensor data produced by common measuring techniques, e.g., guided wave propagation analysis, is characterized by a high dimensionality in the temporal domain, and moreover in the spatial domain using 2D scanning. The quality of the results gathered from guided wave analysis depends strongly on the pre-processing of the raw sensor data and the selection of appropriate region of interest windows (ROI) for further processing (feature selection). Commonly, structural monitoring is a task that maps high-dimensional input data on low-dimensional output data (feature extraction of information), e.g., in the simplest case a Boolean output variable “Damaged”. Machine Learning (ML), e.g., supervised learning, can be used to derive such a mapping function. But quality and performance depends strongly on feature selection, too. Therefore, adaptive and reliable input data reduction (feature selection) is required at the first layer of an automatic structural monitoring system. Assuming some kind of one- or two-dimensional sensor data (or n-dimensional in general), image segmentation can be used to identify ROIs. Major difficulties in image segmentation are noise and the differing homogeneity of regions, complicating the definition of suitable threshold conditions for the edge detection or region splitting/clustering. Many traditional image segmentation algorithms are constrained by this issue. In this work, autonomous agents are used as an adaptive and self-organizing software architecture solving the feature selection problem. Agents are operating on dynamically bounded data from different regions of a signal or an image (i.e., distributed with simulated mobility), adapted to the locality, being reliable and less sensitive to noisy sensor data. Finally, adaptive feature extraction (information of structural state and damage) is performed by numerical algorithms and Machine Learning based on ultrasonic measurements of hybrid probes with impact damages.
[c18.4]
S. Bosse, A Unified System Modelling and Programming Language based on JavaScript and a Semantic Type System, Procedia Manufacturing, Volume 24, 2018, Pages 21-39, Proc. of the 4th International Conference on System-Integrated Intelligence Conference, Hanover, Germany,
DOI: 10.1016/j.promfg.2018.06.005 Paper PDFPublisherPresentation HTML
The design and simulation of complex mechatronic and intelligent systems require a unified system modelling and programming language. This work introduces JavaScript as a unified modelling and programming language by extending JavaScript with a semantic type system extension JST as a possible solution to fill the gap between models and implementations, finally resulting in the JS+ super set language combining typing, modelling, and programming. The paper shows various model domains and their relation to the JS+ programming model including some generic transformation rules. Finally, a system compiler framework is introduced that can process JS+ models and program code. The tool uses JS+ input to produce a wide range of output formats for software and hardware design, and multi-domain simulation.
[c18.5]
S. Bosse, Smart Micro-scale Energy Management and Energy Distribution in Decentralized Self-Powered Networks Using Multi-Agent Systems, FedCSIS Conference, 6th International Workshop on Smart Energy Networks & Multi-Agent Systems, 9-12.9.2018, Posznan,
Poland, 2018, Paper PDF
Energy distribution as a main part of energy man- agement in self-powered micro-scale networks like sensor net- works is a challenge with the goal to satisfy a safe and reliable operational state on system and node level. Under the assumption that nodes are arranged in mesh-like networks with links posing the capability to transfer energy between nodes a self-organizing MAS is deployed in this work successfully to distribute energy without a system/world level model and knowledge of the single nodes about the system state. Different agent behaviour were in- vestigated and evaluated. The exploring help strategy with deliver child agents showed the best and efficient overall behaviour. The agents were programmed in JavaScript using the JavaScript Agent Platform that can be deployed in strong heterogeneous envi- ronments
This talk focuses on the simulation of complex and large-scale agent-based systems. There is an ongoing activity to model and study social systems using agent based modeling (ABM) and simulation. Commonly ABM is performed in a sandbox with a very limited world model. Moreover, the boundary between human beings and machines is vanishing. For example, recently exposed, automatic chat bots gain influence on society opinions and decision making processes (in politics, elections, business). Commonly ABM is performed in a closed environment only using simulated artificial agents in an artificial simulation world. There is no interaction or data exchange with real worlds. Although pure digital, real worlds include the WWW, social platforms, and Clouds. The outcome of such limited scope and simplified systems is application specific. In a large-scale agent-based simulation embedded in and connected to real world environments (so called "human- or hardware-in-the-loop" simulation) agents can represent different behaviour, goals, and individuals like chat bots or artificial humans and their interaction with virtual and real individuals, e.g., via WEB interfaces or robots (software agents meet hardware agents). The tight coupling of simulation, technical systems (e.g., robots or WEB services), and human interaction can be established by using mobile agents and a highly portable agent processing platform that can be deployed in strong heterogeneous environments (including WEB browser and mobile devices like smart phones) and simulation simultaneously. This distributed multi-agent system is well suited to include and perform Crowd Sensing to extend the data base. Such a simulation system can be used to study a broad range of complex socio-technical systems and machine-human interactions on large-scale level. One prominent example is modeling of opinion and decision making under the influence of digital technologies. It can be expected that the simulation of large-scale agent societies with agent population beyond one Million individual agents delivers statistical strength and generality.
[c18.7]
S. Bosse, U. Engel, Augmented Virtual Reality: Combining Crowd Sensing and Social Data Mining with Large-Scale Simulation Using Mobile Agents for Future Smart Cities. Proceedings, Volume 4, ECSA-5
5th International Electronic Conference on Sensors and Applications 15–30 November, 2018
DOI 10.3390/ecsa-5-05762 Proceedings PDFPresentation HTMLProceedings Publisher
Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information. In contrast, simulation worlds are commonly closed and rely on artificial sensory information generated by the simulator program or using data collected off-line. In this work, a new simulation paradigm is introduced, providing augmented virtuality by integrating crowd sensing and social data mining in simulation worlds by using mobile agents. The simulation world interacts with real world environments, humans, machines, and other virtual worlds in real-time. Mobile agents are closely related to bots that can interact with humans via chat blogs. Among the mining of physical sensors (temperature, motion, position, light, …), mobile agents can perform Crowd Sensing by participating in question–answer dialogs via a chat blog provided by a WEB App that can be used by the masses. Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents). Virtual sensors are sensor aggregators performing sensor fusion in a spatially region.
[p18.1]
S. Bosse, Data mining with Machine Learning for the Social Sciences, Invited Keynote Talk, 18.5.2018, Bremen, Computational Social Sciences Talks, BIGSSS, SOCIUM, doi 10.13140/RG.2.2.12746.67526,
University of Bremen, Jacobs University Bremen, 2018 Presentation Script PDFPresentation HTML
Data mining, especially as applied to social science data, is a rapidly changing and emerging field. Data mining (DM) is the name given to a variety of computer-intensive techniques for discovering structure and for analyzing patterns in data. Using those patterns, DM can create predictive models, or classify things, or identify different groups or clusters of cases within data. Data mining uses machine learning and predictive analytics that are already widely used in technical areas and business and are starting to spread into social science and other areas of research. This talk will give an introduction to machine learning techniques, its challenges, applications, and pitfalls closely
Publications 2017
[j17.1]
S. Bosse, Incremental Distributed Learning with JavaScript Agents for Earthquake and Disaster Monitoring, International Journal of Distributed Systems and Technologies (IJDST), (2017), IGI-Global, Vol. 8, Issue 4,
DOI: 10.4018/IJDST.2017100103 Paper PDFPublisher
Ubiquitous computing and The Internet-of-Things (IoT) emerge rapidly in today’s life and evolve to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work mobile agents are used to merge the IoT with Mobile and Cloud environments seamlessly. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-agent Systems (MAS) in strongly heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to regions of sensor data from stations of a seismic network with global ensemble voting. This network environment can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application. The incremental distributed learning algorithm outperforms a prior developed non-incremental algorithm (Distributed Interval Decision Tree learner) and can be efficiently used in low-resource platform networks.
[c17.1]
D. Lehmhus, S. Bosse, M. Busse, Autonomous Property Change in Adaptive Composites: A Simulation-based study on Multi-Agent-Systems Approaches, DGM Verbundwerkstoffe Congress, 21. Symposium,
5. - 7. July 2017, Bremen, Germany Publisher
Load-bearing structures are typically designed towards relevant load cases assuming static shape and fixed sets of materials properties decided upon during design and materials selection. Structures that could change local properties in service in response to load change could raise additional weight saving potentials , thus supporting lightweight design and sustainability. Materials with such capabilities must necessarily be composite in the sense of a heterogeneous build-up, exhibiting e. g. an architecture consisting of numerous active cells with sensing, signal and data processing and actuation/stimulation capability. One concern regarding active smart cellular structures is correlated control of cells’ responses, and the underlying informational organization providing robustness and real-time capabilities. We suggest a two-stage approach which combines machine learning with mobile and reactive Multi-agent Systems (MAS). In it, the MAS’ task is to analyze loading situations based on sensor data and negotiate matching spatial redistributions of material properties like elastic modulus to achieve higher-level optimization aims like a minimum of the total strain energy within the structure, or a reduction of peak stress levels. The associated machine learning approach would be employed to recognize loading situations already encountered in the past for which optimized solutions exist and in such cases bypass the MAS system to directly enforce the respective property distribution. In the present study, a proof of concept of the approach is presented which combines finite element method (FEM) and MAS simulation, with the former primarily taking the place of the physical structure. In addition, FEM simulations are used for off-line training of the MAS prior to its deployment in the real or simulated structure. The classification models learned this way represent a starting point which is constantly being updated at run-time during the service life of the structure using incremental learning techniques.
[c17.2]
S. Bosse, E. Pournaras, An Ubiquitous Multi-Agent Mobile Platform for Distributed Crowd Sensing and
Social Mining, FiCloud 2017: The 5th International Conference on Future Internet of Things and Cloud, Aug 21, 2017 - Aug 23, 2017,
Prague, Czech Republic Paper PDFPublisher
Smart mobile devices are fundamental date sources for crowd activity tracing. Large-scale mobile networks and the Internet-of-Things (IoT) expand and become part of pervasive and ubiquitous computing offering distributed and transparent services. With the IoT, Crowd Sensing is extended by Things Sensing, creating heterogeneous smart environments. A unified and common data processing and communication methodology is required so that the IoT, mobile networks, and Cloud-based environments seamlessly integrate, which can be fulfilled by self-organizing mobile agents, discussed in this work. Currently, portability, resource constraints, security, and scalability of Agent Processing Platforms (APP) are essential issues for the deployment of Multi-agent Systems (MAS) in highly heterogeneous networks. Beside the operational aspects of MAS, an organizational structure is required for the deployment of MAS in crowd sensing and social mining applications. The Planetary Nervous system (Nervousnet) consists of virtual sensors building the core functionality for such applications running on smart phones with a Cloud-like architecture. The virtual sensors enable a holistic composition and modeling approach. Self-organizing and adaptive mobile agents are well known as the core cells of holistic and modular systems. In this work, both concepts are combined. JavaScript agents are introduced as virtual sensors in the Nervousnet environment, evaluated with a simulation of a distributed sensor fusion use-case in a mobile network based on real-world data from Nervousnet, showing the suitability of the hybrid approach, benefiting from local and event-based sensor processing performed by the MAS.
[c17.3]
S. Bosse, D. Lehmhus, Towards Large-scale Material-integrated Computing: Self-Adaptive Materials and Agents, IEEE 2nd International Workshops on Foundations and Applications of Self Systems (FASW), DOI: 10.1109/FAS-W.2017.123,
18-22 September 2017, University of Arizona, Tucson, AZ Paper PDFPublisher
In the past decades there was an exponential growth of computer networks and computing devices, connecting computers with a size in the m3 range. The Internet-of-Things (IoT) emerges connecting everything, demanding for new distributed computing and communication approaches. Currently, the IoT connects devices with a size in the cm3 range. But new technologies enable the integration of computing in materials and technical structures with sensor and actor networks connecting devices in the mm3 range. This work investigates issues in large-scale computer networks related to the deployment of low- and very-low resource miniaturized nodes integrated within materials. These networks operate under harsh conditions with possibility of technical failures requiring robustness. Despite sensor networks used for structural monitoring, self-adaptive materials can profit from self-organizing and autonomous distributed data processing using Multi-agent systems, demonstrated in this paper. Self-adaptive materials are able to adapt the material or mechanical structure properties based on their environmental interaction (load/stress) to minimize the risk of overloading. A structure that could change its local properties in service based on the identified loading situation could thus potentially raise additional weight saving potentials and thus supporting lightweight design, and in consequence, sustainability.
[c17.4]
D. Lehmhus, S. Bosse, A. Gemilang, A Multi-Agent System based approach for Adaptive Property Control in Smart Load-Bearning Structures, European Congress and Exhibition on Advanced Materials and Processes, EUROMAT (2017), Symposium E6, Modeling, Simulation
and Optimization,
17-22 September, 2017, Thessaloniki, Greek Publisher
[c17.5]
S. Bosse, D. Schmidt, M. Koerdt, Robust and Adaptive Signal Segmentation for Structural Monitoring Using Autonomous Agents, In Proceedings of the 4th Int. Electron. Conf. Sens. Appl., 15–30 November 2017;
Doi:10.3390/ecsa-4-04917 Paper PDFPublisher
Monitoring of mechanical structures is a Big Data challenge and includes Structural Health Monitoring (SHM) and Non-destructive Testing (NDT). The sensor data produced by common measuring techniques, e.g., guided wave propagation analysis, is characterized by a high dimensionality in the temporal and spatial domain. There are off- and on-line methods applied at maintenance- or run-time, respectively. On-line methods (SHM) usually are constrained by low-resource processing platforms, sensor noise, unreliability, and real-time operation requiring advanced and efficient sensor data processing. Commonly, structural monitoring is a task that maps high-dimensional input data on low-dimensional output data (information, that is feature extraction), e.g., in the simplest case a Boolean output variable “Damaged”. Machine Learning (ML), e.g., supervised learning, can be used to derive such a mapping function. But ML quality and performance depends strongly on the input data size. Therefore, adaptive and reliable input data reduction (that is feature selection) is required at the first layer of an automatic structural monitoring system. Assuming some kind of two-dimensional sensor data (or n-dimensional data in general), image segmentation can be used to identify Regions of Interest (ROI), e.g., of wave propagation fields. Wave propagation in materials underlie reflections that must be distinguished, especially in hybrid materials (e.g., combining metal and fibre-plastic composites) there are complex wave propagation fields. The image segmentation is one of the most crucial part of image processing (Mishra, 2011). Major difficulties in image segmentation are noise and the differing homogeneity (fuzziness and signal gradients) of regions, complicating the definition of suitable threshold conditions for the edge detection or region splitting/clustering. Many traditional image segmentation algorithms are constrained by this issue. Artificial Intelligence can aid to overcome this limitation by using autonomous agents as an adaptive and self-organizing software architecture, presented in this work. Using a collection of co-operating agents decomposes a large and complex problem in smaller and simpler problems with a Divide-and-Conquer approach. Related to the image segmentation scenario, agents are working mostly autonomous (de-coupled) on dynamic bounded data from different regions of an image (i.e., distributed with simulated mobility), adapted to the locality, being reliable and less sensitive to noisy sensor data. In this work, different agent behaviour and segmentation approaches are introduced and evaluated with measured high-dimensional data from piezo-electric acusto-ultrasonic sensors recording wave propagation in plate-like structures. Commonly, SHM deploys only a small set of sensors and actuators at static positions delivering only a few temporal resolved sensor signals (1D), whereas NDT methods additionally can use spatial scanning to create images of wave signals (2D). Both one-dimensional temporal and two-dimensional spatial segmentation is considered to find characteristic ROIs.
Publications 2016
[j16.1]
Stefan Bosse, Armin Lechleiter, A hybrid approach for
Structural Monitoring with self-organizing multi-agent systems and
inverse numerical methods in material-embedded sensor networks, Mechatronics, (2016),
DOI:10.1016/j.mechatronics.2015.08.005. Paper PDFPaper OnlinePublisher
One of the major challenges in Structural Monitoring of mechanical structures is the derivation of meaningful information from sensor data. This work investigates a hybrid data processing approach for material-integrated Structural Health and Load Monitoring systems by using self-organizing mobile multi-agent systems (MAS), and inverse numerical methods providing the spatial resolved load information from a set of sensors embedded in the technical structure with low-resource agent processing platforms scalable to microchip level, enabling material-integrated real-time sensor systems. The MAS is deployed in a heterogeneous environment and offers event-based sensor preprocessing, distribution, and pre- computation. Inverse numerical approaches usually require a large amount of computational power and storage resources, not suitable for resource constrained sensor node implementations. Instead, the computation is partitioned into spatial off-line (outside the network) and on-line parts, with on-line sensor processing performed by the agent system. A unified multi-domain simulation framework is used to profile and validate the proposed approach.
[j16.2]
Stefan Bosse, Industrielle Agenten und Agenten-basiertes Lernen im technischen Kontext,
Industrie Management, 6/2016. Paper PDFManuscript PDFPublisher
Datenverarbeitungsprozesse werden immer komplexer hinsichtlich der Datenmenge, Datendimension, und dem Zusammenhang der abgeleiteten Informationen mit den Eingangsdaten. Dieses wird bei sensorischen Messprozessen besonders deutlich, wo Messunsicherheiten, Kalibrierungsfehler, und Unzuverlässigkeit von Sensoren signifikanten Einfluss auf die Informationsgewinnung haben. Gerade im technischen und industriellen Kontext stellt die zunehmende Komplexität und Verteilung der Datenverarbeitung ein zunehmendes Problem dar. Häufig stehen hinter der Informationsableitung mathematische Modelle und Funktionen, die aber nicht immer vollständig sind. Geht es um die Gewinnung von Zustandsaussagen eines Systems oder um Adaption, können Lernverfahren eine Alternative darstellen. Traditionell werden Messdaten zentral gesammelt und ausgewertet. Es soll aufgezeigt werden, wie verteiltes Maschinelles Lernen mit mobilen Agenten und selbst-organisierenden Systemen einen signifikanten Beitrag zur Verbesserung der Datenverarbeitung in technischen und industriellen Systemen leisten kann, dieses sowohl hinsichtlich der Qualität der Aussagen von Schlussfolgerungen, der Effizienz, als auch der Robustheit. Dieser Beitrag soll einen Überblick der verschiedenen Teilbereiche Lernen, Agenten, und Architekturen geben.
[c16.1]
Stefan Bosse, Industrial Agents and Distributed Agent-based Learning, 3rd International Electronic Conference on Sensors and Applications . 15-30 Nov. 2016, MDPI, 2016,
DOI:10.3390/ecsa-3-S2004. Paper PDFPublisher
Today sensor data processing and information mining become more and more complex concerning the amount of sensor data to be processed, the data dimension, the data quality, and the relationship between derived information and input data. This is the case especially in large-scale sensing and measuring processes embedded in Cloud environments. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue. Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete or unknown. If reasoning of statements is primarily desired, Machine Learning can be an alternative. Traditionally, sensor data is acquired and delivered to and processed by a central processing unit. In this paper, the deployment of distributed Machine Learning using mobile Agents forming self-organizing and self-adaptive systems (self-X) is discussed and posing the benefit for the enhancement of the sensor and data processing in technical and industrial systems. This also addresses the quality of the computed statements, e.g., an accurate prediction of run-time parameters like mechanical loads or health conditions, the efficiency, and the reliability in the presence of partial system failures.
[c16.2]
Dirk Lehmhus, Stefan Bosse, Self-adaptive Smart Materials: A new Agent-based Approach, 3rd International Electronic Conference on
Sensors and Applications . 15-30 Nov. 2016, MDPI, 2016,
DOI:10.3390/ecsa-3-S2005. Paper PDFPublisher
Load-bearing engineering structures typically have a static shape fixed during design based on expected usage and associated load cases. But neither can all possible loading situations be foreseen, nor could this large set of conditions be reflected in a practical design methodology— and even if either was possible, the result could only be the best compromise and thus deviate significantly from the optimum solution for any specific load case. In contrast, a structure that could change its local properties in service based on the identified loading situation could potentially raise additional weight saving potentials and thus support lightweight design, and in consequence, sustainability. Materials of this kind would necessarily exhibit a cellular architecture consisting of active cells with sensing and actuation capabilities. Suitable control mechanisms both in terms of algorithms and hardware units would form an integral part of these. A major issue in this context is correlated control of actuators and informational organization meeting real-time and and robustness requirements. In this respect, the present study discusses a two-stage approach combining mobile & reactive Multi-agent Systems (MAS) and Machine Learning. While MAS will negotiate property redistribution, machine learning shall recognise known load cases and suggest matching property fields directly.
[c16.3]
Stefan Bosse, Distributed Machine Learning with Self-organizing Mobile Agents for Earthquake Monitoring, IEEE 1st International Workshops on Foundations and Applications of Self Systems (FASW), SASO Conference, DSS Workshop, 12 September 2016, Augsburg, Germany, 2016, 2016,
DOI:10.1109/FAS-W.2016.38. Paper PDFPublisher
Ubiquitous computing and The Internet-of-Things (IoT) raises rapidly in today's life and is becoming part of self-organizing systems (SoS). A unified and scalable information processing and communication methodology using mobile agents is presented to merge the IoT with Mobile and Cloud environments seamless. A portable and scalable Agent Processing Platform (APP) is an enabling technology that is central for the deployment of Multi-agent Systems (MAS) in strong heterogeneous networks including the Internet. A large-scale distributed heterogeneous seismic sensor and geodetic network used for earthquake analysis is one example, which can be extended by ubiquitous sensing devices like smart phones. To simplify the development and deployment of MAS in the Internet domain agents are directly implemented in JavaScript (JS). The proposed JS Agent Machine (JAM) is an enabling technology. It is capable to execute AgentJS agents in a sandbox environment with full run-time protection, low-resource requirements, and Machine Learning as a service. A simulation of a seismic network and real earthquake data demonstrates the deployment of the JAM platform. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting, and the application.
[c16.4]
Stefan Bosse, Mobile Multi-Agent Systems for the Internet-of-Things and Clouds using the JavaScript Agent Machine Platform and Machine Learning as a Service, The IEEE 4th International Conference on Future Internet of Things and Cloud , 22-24 August 2016, Vienna, Austria, 2016, 2016,
DOI:10.1109/FiCloud.2016.43. Paper PDFPublisher
The Internet-of-Things (IoT) gets real in today's life and is becoming part of pervasive and ubiquitous computing networks offering distributed and transparent services. A unified and common data processing and communication methodology is required to merge the IoT, sensor networks, and Cloud-based environments seamless, which can be fulfilled by the mobile agent-based computing paradigm, discussed in this work. Currently, portability, resource constraints, security, and scalability of Agent Processing Platforms (APP) are essential issues for the deployment of Multi-agent Systems (MAS) in strong heterogeneous networks including the Internet, addressed in this work. To simplify the development and deployment of MAS it would be desirable to implement agents directly in JavaScript, which is a well known and public widespread used programming language, and JS VMs are available on all host platforms including WEB browsers. The novel proposed JS Agent Machine (JAM) is capable to execute AgentJS agents in a sandbox environment with full run-time protection and Machine learning as a service. Agents can migrate between different JAM nodes seamless preserving their data and control state by using a on-the-fly code-to-text transformation in an extended JSON+ format. A Distributed Organization System (DOS) layer provides JAM node connectivity and security in the Internet, completed by a Directory-Name Service offering an organizational graph structure. Agent authorization and platform security is ensured with capability-based access and different agent privilege levels.
[c16.5]
Stefan Bosse, Structural Monitoring with Distributed-Regional and Event-based NN-Decision Tree Learning using Mobile Multi-Agent Systems and common JavaScript platforms, Procedia Technology, 3rd International Conference on System-Integrated Intelligence: New Challenges for Product and Production Engineering, June 13th (Mon.) - 15th (Wed.) 2016: Paderborn, Germany, 2016,
DOI:10.1016/j.protcy.2016.08.063. Paper PDFPublisher
Among the Internet-of-Things, one major field of application deploying agent-based sensor and information processing is Structural Load and Structural Health Monitoring (SLM/SHM) of mechanical structures. This work investigates a data processing approach for material-integrated and mobile ubiquitous SHM and SLM systems by using self-organizing mobile multi-agent systems (MAS), executed on a highly portable JavaScript-based Agent Processing Platform (APP), and optimized Machine Learning (ML) methods providing load class recognition from a set of sensors embedded in the technical structure. Machine learning approaches usually require a large amount of computational power and storage resources and ML is commonly performed off-line, not suitable for resource constrained sensor network implementations. Instead, a novel distributed-regional on-line learning is applied, with on-line distributed sensor processing and learning performed by the agent system. The APP provides ML as a service, and the agent itself only collects training and analysis data passed to the APP, finally returning a learned model that is saved by the agent in a compact format (and is available on any other location). A case study shows that the learning algorithm is suitable (stable) for noisy and time varying sensor data. Spatial global learning is reduced and mapped on local region learning with global voting.
[c16.6]
Stefan Bosse, Armin Lechleiter, Dirk Lehmhus, Data evaluation in smart sensor networks using inverse methods and artificial intelligence (AI): Towards real-time capability and enhanced flexibility, Proc. of the CIMTEC, - 7th Forum on New Materials, Perugia, Italy, June 5 to 9, 2016, 5th International Conference “Smart and Multifunctional Materials, Structures and Systems, 2016, 2016,
DOI:10.4028/www.scientific.net/AST.101.55. Paper PDFPublisher
Data evaluation is crucial for gaining information from sensor networks. Main challenges include processing speed and adaptivity to system change, both prerequisites for SHM-based weight reduction via relaxed safety factors. Our study looks at soft real time solutions providing feedback within defined but flexible, application-controlled intervals. These can rely on minimizing computation/communication latencies e.g. by parallel computation. Strategies towards this aim can be model-based, including inverse FEM, or model-free, including machine learning, which in practice bases training on a defined system state, too, hence also facing challenges at state changes. We thus introduce hybrid data evaluation combining multi-agent based systems (MAS) with inverse FEM, mainly relying on matrix operations that can be partially distributed: The MAS perform sensor data acquisition, aggregation, pre-computation, and finally application (the LM/SHM itself and higher information processing and visualization layers, i.e., WEB interfaces). System capabilities are evaluated against a virtual test case, demonstrating enhanced stability and reliability. Besides, we analyze system performance under conditions of in-service change and discuss system layouts suited to improve coverage of this issue.
Publications 2015
[j15.1]
Dirk Lehmhus, Thorsten Wuest, Stefan Wellsandt, Stefan Bosse, Toshiya Kaihara, Klaus-Dieter Thoben, Matthias Busse, Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift, Sensors (MDPI), 15 (12), pp. 32079-32122, 2015,
DOI:10.3390/s151229905. Paper PDFPaper OnlinePublisher
Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.
[j15.2]
Stefan Bosse, Design and Simulation of Material-Integrated
Distributed Sensor Processing with a Code-Based Agent Platform and
Mobile Multi-Agent Systems, Sensors (MDPI), 15 (2), pp. 4513-4549,
2015,
DOI:10.3390/s150204513. Paper PDFPaper OnlinePublisher
Multi-agent systems (MAS) can be used for decentralized and self-organizing data processing in a distributed system, like a resource-constrained sensor network, enabling distributed information extraction, for example, based on pattern recognition and self-organization, by decomposing complex tasks in simpler cooperative agents. Reliable MAS-based data processing approaches can aid the material-integration of structural-monitoring applications, with agent processing platforms scaled to the microchip level. The agent behavior, based on a dynamic activity-transition graph (ATG) model, is implemented with program code storing the control and the data state of an agent, which is novel. The program code can be modified by the agent itself using code morphing techniques and is capable of migrating in the network between nodes. The program code is a self-contained unit (a container) and embeds the agent data, the initialization instructions and the ATG behavior implementation. The microchip agent processing platform used for the execution of the agent code is a standalone multi-core stack machine with a zero-operand instruction format, leading to a small-sized agent program code, low system complexity and high system performance. The agent processing is token-queue-based, similar to Petri-nets. The agent platform can be implemented in software, too, offering compatibility at the operational and code level, supporting agent processing in strong heterogeneous networks. In this work, the agent platform embedded in a large-scale distributed sensor network is simulated at the architectural level by using agent-based simulation techniques.
[b15.1]
Stefan Bosse, Design and Simulation of a Low- Resource
Processing Platform for Mobile Multi-Agent Systems in Distributed
Heterogeneous Networks, Béatrice Duval, Herik, Jaap van den,
Loiseau, Stephane, Filipe, Joaquim (Ed.): Agents and Artificial
Intelligence (LNAI 8946), Springer, 2015, ISBN: 978-3-319-25209-4,
DOI:10.1007/978-3-319-25210-0_5. Paper PDFPublisher
The design and simulation of an agent processing platform suitable for distributed computing in heterogeneous sensor networks consisting of low- resource nodes is presented, providing a unique distributed programming model and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. In this work multi-agent systems with mobile activity-based agents are used for sensor data processing in unreliable mesh-like networks of nodes, consisting of a single microchip with limited low computational resources, which can be integrated in- to materials and technical structures. The agent behaviour, based on an activity- transition graph model, the interaction, and mobility can be efficiently integrated on the microchip using a configurable pipelined multi-process architecture based on the Petri-Net model and token-based processing. A new sub-state partitioning of activities simplifies and optimizes the processing platform significantly. Additionally, software implementations and simulation models with equal functional behaviour can be derived from the same program source. Hardware, software, and simulation platforms can be directly connected in heterogeneous networks. Agent interaction and communication is provided by a simple tuple-space data- base. A reconfiguration mechanism of the agent processing system offers activity graph changes at run-time. The suitability of the agent processing platform in large scale networks is demonstrated by using agent-based simulation of the plat- form architecture at process level with hundreds of nodes.
[c15.1]
Dirk Lehmhus, Stefan Bosse, Material-Integrated Intelligent Systems: A Review on State of the Art, Challenges and Trends, Proceedings of the 2nd Int. Electron. Conf. Sens. Appl., Nov 15, 2015 - Nov 30, 2015, MDPI, 2015,
DOI:10.3390/ecsa-2-D002. Paper PDFPublisher
As a concept, material-integrated intelligent systems represent the vision of embedding not only sensors, but full sensor networks in technical materials, irrespective of their application being dominated by functional or structural properties. In this sense, the term full sensor networks encompasses the sensors, the associated signal processing, the data evaluation and information retrieval, provisions for communication within the network and beyond it, and an energy supply system. The concept as such can be applied to any type or class of host material, ranging from organic materials to composites, metals and ceramics. The result are materials that are, in a manner of speaking, able to “feel” in the broader sense associated with this term. The present work discusses current approaches towards realizing material-integrated intelligent systems on hard- and software level as well as potential applications for such materials. It names the specific challenges associated with integration and suggests state of the art and future paths to address them. A special section is dedicated to the advent of additive manufacturing techniques adapted to facilitate sensor integration: The present growth in this field is expected to also extend into the realm of sensor-integrated materials and structures.
[c15.2]
Stefan Bosse, Agent-Based Solutions for Industrial Environments composed of Autonomous Mobile Agents, Modular Agent Platforms, and Tuple Spaces, Proceedings of the 2nd Int. Electron. Conf. Sens. Appl., Nov 15, 2015 - Nov 30, 2015, 2015,
DOI:10.3390/ecsa-2-S5001. Paper PDFPublisher
Design and Production processes become more and more complex. Today, industrial manufacturing environments consists of large-scale networks connecting smart sensors, embedded systems, server, desktop, and mobile computers. Mobile software Agents can be used in such strong heterogeneous environments with design, manufacturing, and logistics facilities. A unified agent model and a agent processing platform can overcome network and system barriers arising in such complex systems. The deployment of Industrial Agents can improve the scalability, productivity, and stability of modern adaptive and customized production processes, and aid the integration of sensor networks in cloud computing. This article shows the deployment and the relationship of industrial agents to industrial environments and common agent models, finally mapped on mobile program code executed by a low-resource and highly portable agent processing platform.
[c15.3]
Stefan Bosse, Unified Distributed Computing and Co-ordination in Pervasive/Ubiquitous Networks with Mobile Multi-Agent Systems using a Modular and Portable Agent Code Processing Platform, The 6th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2015), Procedia Computer Science, Berlin, Germany, 27-30.9.2015, 63, Procedia Computer Science, Elsevier, 2015,
DOI:10.1016/j.procs.2015.08.312. Paper PDFView PUB
A novel and unified approach for reliable distributed and parallel computing using mobile agents is introduced. The agents can be deployed in large scale and hierarchical network environments crossing barriers transparently. The networks can consist of high- and low-resource nodes ranging from generic computers to microchips, and the supported network classes range from body area networks to the Internet including any kind of sensor and ambient network. Agents are represented by mobile program code that can be modified at run-time. The presented approach enables the development of sensor clouds and smart systems of the future integrated in daily use computing environments and the Internet. Agents can migrate between different hardware and software platforms by migrating the program code of the agent, embedding the state and the data of an agent, too. The entire information exchange and coordination of agents with other agents and the environment is performed by using a tuple space database. Beside architecture specific hardware and software implementations of the agent processing platform, there is a JavaScript (JS) implementation layered on the top of a distributed management layer. The JS platform enables the integration of Multi-agent Systems (MAS) in Internet server and application environments (e.g., WEB browser). Agents can migrate transparently between hardware-level sensor networks and WEB browser applications or network servers and vice versa without any transformation required.
[c15.4]
Stefan Bosse, A Unified Distributed Computing Framework with Mobile Multi- Agent Systems and Virtual Machines for Large-Scale Applications: From the Internet-of-Things to Sensor Clouds, Annals of Computer Science and Information Systems Volume 6, Position Papers of the 2015 Federated Conference on Computer Science and Information Systems (FEDCSIS), Lodz, Poland, 13 - 16 Sep, 6, 2015,
DOI:10.15439/2015F252. Paper PDFPublisher
A novel and unified design approach for reliable distributed and parallel data processing in large scale networks consisting of high- and of low-resource nodes (ranging from generic computers to microchips) using mobile agents is introduced. This approach enables the development of sensor clouds of the future integrated in daily use computing environments and the Internet. Agents can migrate between different hard- ware and software platforms by migrating the program code of the agent, embedding the state and the data of an agent, too. Agent mobility crossing different execution platforms, agent interaction by using tuple-space databases, and agent code recon- figuration enable the design of reliable distributed sensor and information processing networks. The Agent Processing Plat- form can be implemented in hardware (microchip level), soft- ware (embedded system), WWW using JavaScript (including client-side browser applications), and simulation. All implementations offer compatibility on operational and communication level. A graph-linked multi broker service is established for the JavaScript processing platform class to provide service ports and the access of the agent platform from the outside in browser applications, which can usually only act as clients and are usual- ly hidden by a private network and firewalls.
[c15.5]
Stefan Bosse, From the Internet-of-Things to Sensor Clouds - Unified Distributed Computing in Heterogeneous Environments with Smart and Mobile Multi-Agent Systems, Proc. of the Smart Systems Integration Conference, 11-12 March 2015, Copenhagen, 2015.
ISBN: 9781510823617 Paper PDFPublisher
A novel and unified design approach for reliable distributed and parallel data processing in large scale networks consisting of high- and of low-resource nodes using mobile agents is introduced. This approach enables the development of sensor clouds of the future integrated in daily use computing environments and the Internet. Agents can mi- grate between different hardware and software platforms by migrating the program code of the agent, embedding the state and the data of an agent, too. Agent mobility crossing different execution platforms, agent interaction by using tuple-space databases, and agent code reconfiguration enable the design of reliable distributed sensor processing networks.
Publications 2014
[j14.1]
Dirk Lehmhus, Stefan Bosse, Walter Lang, P.C. Chao, F.
Chang, Guest Editorial Special Issue on Material-Integrated Sensing,
Data Processing and Communication, IEEE Sensors, 14 (7), 2014,
DOI: 10.1109/JSEN.2014.2330133. Paper PDF
TODAY, trends like the Internet of Things are nearing large-scale implementation. They rely on solutions enabling objects to become cyber-physical systems capable of perceiving their environment or their internal state. Sensing provides the information gateway to achieve this coupling between object and environment, as well as their interaction. This understanding has fuelled considerable research efforts on intelligent and sensor-equipped structures. Among the obstacles that impede their introduction are economic ones. Material rather than component-integrated sensing and intelligence has the potential to circumnavigate some of the obstacles by paving the way to economy-of-scale effects.
[j14.2]
Stefan Bosse, Distributed Agent-based Computing in
Material-Embedded Sensor Network Systems with the Agent-on-Chip Architecture, IEEE Sensors Journal, Special Issue MIS, 2014,
DOI: 10.1109/JSEN.2014.2301938. Paper PDFPaper Online
[IEEE Sensors Top 25 Downloads in May/June 2014] Distributed material-embedded systems like sensor networks integrated in sensorial materials require new data processing and communication architectures. Reliability and robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures must be offered, especially concerning limited service of material-embedded systems after manufacturing. In this work multi-agent systems with state based mobile agents are used for computing in unreliable mesh-like networks of nodes, usually consisting of a single microchip, introducing a novel design approach for reliable distributed and parallel data processing on embedded systems with static resources. An advanced high-level synthesis approach is used to map the agent behaviour to multi-agent systems implementable entirely on microchip-level supporting Agent-On-Chip processing architectures (AoC). The agent behaviour, interaction, and mobility are fully integrated on the microchip using a reconfigurable pipelined communicating process architecture implemented with finite-state machines and register-transfer logic. The agent processing architecture is related to Petri Net token processing. A reconfiguration mechanism of the agent processing system achieves some degree of agent adaptation and algorithmic selection . The agent behaviour, interaction, and mobility features are modelled and specified with an activity-based agent behaviour programming language (AAPL). Agent interaction and communication is provided by a simple tuplespace database implemented on node level and signals providing remote inter-node level communication and interaction.
[c14.1]
Stefan Bosse, Armin Lechleiter, Structural Health and Load Monitoring with Material-embedded Sensor Networks and Self-organizing Multi-Agent Systems, Procedia Technology, Proceeding of the SysInt 2014 Conference, 2-4 July 2014, Bremen, Germany, 2014,
DOI:10.1016/j.protcy.2014.09.039. Paper PDFPublisher PDF
One of the major challenges in Structural Health Monitoring and load monitoring of mechanical structures is the derivation of meaningful information from sensor data. This work investigates a hybrid data processing approach for material-integrated SHM and LM systems by using self-organizing mobile multi-agent systems (MAS), with agent processing platforms scaled to microchip level which offer material-integrated real-time sensor systems, and inverse numerical methods providing the spatial resolved load information from a set of sensors embedded in the technical structure. Inverse numerical approaches usually require a large amount of computational power and storage resources, not suitable for resource constrained sensor node implementations. Instead, off-line computation is performed, with on-line sensor processing by the agent system.
[c14.2]
Stefan Bosse, Processing of Mobile Multi-Agent Systems with a Code-based Agent Platform in Material-Integrated Distributed Sensor Networks, 1st International e-conference on Sensors and Applications, Section D: Sensor Networks, 2014, 2014,
DOI:10.3390/ecsa-1-d010. Paper PDFPublisher PDF
[Best Presentation Award winner] Multi-agent systems (MAS) can be used for a decentralized and self-organizing approach of data processing in a distributed system like a sensor network, enabling information extraction, for example, based on pattern recognition, decomposing complex tasks in simpler cooperative agents. MAS-based data processing approaches can aid the Material-integration of Structural-Health-Monitoring applications, with agent processing platforms scaled to microchip level which offer material-integrated real-time sensor processing.The behaviour model of mobile agents suitable for sensor network operations bases on an activity-transition graph (ATG) and is implemented with stack-based program code holding the control and data state of an agent, which can be modified by the agent itself using code morphing techniques, and which is capable to migrate in the network between nodes. The program code is a self contained unit (a container) and embeds the agent data, the initialization instructions, and the ATG. The agent processing platform used for the execution of the agent code is a pipelined multi-stack virtual machine with a zero-operand instruction format, leading to small sized agent program code, low system complexity, and high system performance. Agents processed on one particular network node can interact by using a tuple-space database provided by each sensor node. Remote interaction is provided by propagating signals carrying data. This approach provides a high degree of computational independency from the underlying platform and other agents, and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. Support for heterogeneous networks considering hardware (System-on-Chip designs) and software (microprocessor) platforms is covered by one design and high-level synthesis flow including functional behavioural simulation.An even-based sensor data processing MAS is used as a test case for the proposed agent processing platform and a microchip level implementation. The sensor data pre-processing MAS delivers sensor data event-based if a change of the sensors was detected (based on pattern recognition), reducing network activity and energy consumption significantly.
[c14.3]
Stefan Bosse, Design of Material-integrated Distributed Data Processing Platforms with Mobile Multi-Agent Systems in Heterogeneous Networks, Proc. of the 6’th International Conference on Agents and Artificial Intelligence ICAART 2014, 2014,
DOI:10.5220/0004817500690080. Paper PDFPublisher
[Nominated for Best Paper Award] An agent processing platform suitable for distributed computing in sensor networks consisting of low-resource (e.g., material-integrated) nodes is presented, providing a unique distributed programming model and enhanced robustness of the entire heterogeneous environment in the presence of node, sensor, link, data processing, and communication failures. In this work multi-agent systems with mobile activity-based agents are used for sensor data processing in unreliable mesh-like networks of nodes, consisting of a single microchip with limited low computational resources. The agent behaviour, interaction, and mobility (between nodes) can be efficiently integrated on the microchip using a configurable pipelined multi-process architecture based on Petri-Nets. Additionally, software implementations and simulation models with equal functional behaviour can be derived from the same source model. Hardware and software platforms can be directly connected in heterogeneous networks. Agent interaction and communication is provided by a simple tuple-space database and signals providing remote inter-node level communication and interaction. A reconfiguration mechanism of the agent processing system offers activity graph changes at run-time.
Publications 2013
[j13.1]
Thomas Behrmann, Christoph Budelmann, Stefan Bosse, Dirk Lehmhus, Marc C Lemmel, Tool chain for harvesting, simulation and management of energy in Sensorial Materials, Journal of Intelligent Material Systems and Structures, 2013,
DOI:10.1177/1045389X13488248. Paper PDFPaper Online
The continuing decrease in size and energy demand of electronic sensor circuits allows endowing engineering structures and, to an increasing degree, materials with integrated sensing and data processing capabilities. Materials that adhere to this description are designated as Sensorial Materials. Their development is multidisciplinary and requires knowledge beyond materials science in fields like sensor science, computer science, energy harvesting, microsystems technology, low-power electronics, energy management, and communication. Development of such materials will benefit from systematic support for bridging research area boundaries. The present article introduces the backbone of an easy-to-use toolbox for layout of the energy supply of smart sensor nodes within a sensorial material. The fundamental approach is transferred from rapid control development, where a comparable MATLAB/Simulink tool chain is already in use. The main goal is to manage limited power resources without unacceptably compromising functionality in a given application scenario. The toolbox allows analysis of the modeled system in terms of energy and power and allows analyzing factors such as energy harvesting, use of predictive power estimation, power saving (e.g. sleep modes), model-based cognitive data reduction methods, and energy aware algorithm switching. It is linked to a simulation environment allowing analysis of energy demand and production in a specific application scenario. Its initial version presented here supports single self-powered sensor nodes. A broad set of application cases is used to develop scenario-dependent solutions with minimum energy needs and thus demonstrate the use of the toolbox and the associated development process. The initial test case is a large-scale sensor network with optical fiber–based data and energy transmission, for which optimization of energy consumption is attempted. The toolbox can be used to improve the power-aware design of sensor nodes on digital hardware level using advanced high-level synthesis approaches and provides input for sensor node and sensor network level.
[j13.2]
Stefan Bosse, Frank Kirchner, Autonomie und Robustheit in
Verteilten Cyber-Physical Systems und Sensorischen Materialien mit
Methoden der Künstlichen Intelligenz, Industrie Management, 1, 2013,
ISSN: 1434-1980. Paper PDF
Sensoren und Aktoren finden immer häufiger Anwendung in der industriellen Produktion. Traditionell werden zentralistische Ansätze für die Verarbeitung der Sensordaten und Ansteuerung der Aktoren verwendet. Zunehmende Dichte von Sensoren und Aktoren, mit gleichzeitig fortschreitender Miniaturisierung, erfordern dezentrale Datenverarbeitung in verteilten Netzwerken aus Sensoren und Aktoren. Die Künstliche Intelligenz, ein Teilgebiet der Informatik, kann wichtige Beiträge für Robustheit und Autonomie bei der Verarbeitung und Verteilung von Daten in solchen Netzwerken liefern.
[b13.1]
Dirk Lehmhus, Stefan Bosse, Matthias Busse, Sensorial Materials, Chapter 17, Dirk Lehmhus, Matthias Busse, Axel S. Herrmann, Kambiz Kayvantash (Ed.): Structural Materials and Processes in Transportation, pp. 517-548, Wiley-VCH, 2013, ISBN: 9783527327874,
DOI:10.1002/9783527649846. Chapter PDFPublisher HTML
By our definition, sensorial materials are materials that are able to feel, that is, materials that can gather and evaluate sensorial information. The definition is wide enough to accommodate different kinds of physical or chemical signals, but it does call for integration of the associated sensor nodes or networks in the material, and for additional data-processing capabilities. Naturally, aspects such as provision for (internal and external) communication as well as a reliable energy supply need to be added. The natural equivalent of such a technical system is the nervous system of the various animal genera. For them, it is the basis of one of the defining characteristics of life itself, irritability or the response to stimuli. The technical motivation to reproduce nature’s invention ismanifold, as are the envisaged applications.Within the context of this book, we will confine ourselves to advantages foreseen in the field of load-bearing structures, and thus naturally to usage in structural health monitoring (SHM). SHM is well established in civil engineering and currently being discussed for aerospace applications. In the latter field, the transition from metal to composite aircraft structures reflected in several chapters of this work has raised concerns with respect to these materials’ typical response to impact loading. Optically, non-detectable failure may occur, calling for a constant monitoring of the state of the material. The transition from the metaldominated ‘‘silver eagle’’ to the composite-based ‘‘blackbird’’ is therefore expected to provide a major boost for SHM not only in terms of market penetration, but also technologically, as it is likely to fuel developments toward sensor-integrated and sensorial materials. Aerospace structures call for component sizes orders of magnitude smaller than what is acceptable for bridges, the present mainstay of SHM applications. Safety of potentially impact-loaded composite structures is one motivation for implementing SHM or, to begin with, load-monitoring systems. There are others, too. Knowing the exact state of a structure at any moment in time allows timing of maintenance work according to actual needs. Need-based, as opposed to regular, maintenance can reduce operating costs specifically in industries where high costs are incurred either by the maintenance work itself or by taking the object of maintenance out of service. Offshore wind energy plants and commercial aircraft are examples in this respect. Further advantages may be realized wherever damagetolerant as opposed to fail-safe or safe-life dimensioning is applied as design philosophy. Here, constant monitoring in conjunction with an understanding of the development of damage over time will enable predictive maintenance strategies as well as weight savings based on an adaptation of safety factors. The latter would be justified by the greater proximity to damage development achieved by means of an integrated system.
[c13.1]
Stefan Bosse, Intelligent Microchip Networks: An Agent-on-Chip Synthesis Framework for the Design of Smart and Robust Sensor Networks, Proceedings of the SPIE 2013, Microtechnologie Conference, Session EMT 102 VLSI Circuits and Systems, 24-26 April 2013, Alpexpo/Grenoble, France, SPIE, 2013,
DOI:10.1117/12.2017224. Paper PDFPublisher HTML
Sensorial materials consisting of high-density, miniaturized, and embedded sensor networks require new robust and reliable data processing and communication approaches. Structural health monitoring is one major field of application for sensorial materials. Each sensor node provides some kind of sensor, electronics, data processing, and communication with a strong focus on microchip-level implementation to meet the goals of miniaturization and low-power energy environments, a prerequisite for autonomous behaviour and operation. Reliability requires robustness of the entire system in the presence of node, link, data processing, and communication failures. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. Traditionally multi-agent systems are abstract programming models which are implemented in software and executed on program controlled computer architectures. This approach does not well scale to micro-chip level and requires full equipped computers and communication structures, and the hardware architecture does not consider and reflect the requirements for agent processing and interaction. We propose and demonstrate a novel design paradigm for reliable distributed data processing systems and a synthesis methodology and framework for multi-agent systems implementable entirely on microchip-level with resource and power constrained digital logic supporting Agent-On-Chip architectures (AoC). The agent behaviour and mobility is fully integrated on the micro-chip using pipelined communicating processes implemented with finite-state machines and register-transfer logic. The agent behaviour, interaction (communication), and mobility features are modelled and specified on a machine-independent abstract programming level using a state-based agent behaviour language (APL). With this APL a high-level agent compiler is able to synthesize a hardware model (RTL, VHDL), a software model (C, ML), or a simulation model (XML) suitable to simulate a multi-agent system using the SeSAm simulator framework. Agent communication is provided by a simple tuple-space database implemented on node level providing fault tolerant access of global data. A novel synthesis development kit (SynDK) based on a graph-structured database approach is introduced to support the rapid development of compilers and synthesis tools, used for example for the design and implementation of the APL compiler.
[c13.2]
Stefan Bosse, Florian Pantke, Stefan Edelkamp, Robot Manipulator with emergent Behaviour supported by a Smart Sensorial Material and Agent Systems, Proceedings of the Smart Systems Integration Conference SSI 2013, Topic 5, Amsterdam NL, 13-14.3.2013, 2013,
ISBN: 978-3-8007-3490-0. Paper PDF
Intelligent behaviour of robot manipulators become important in unknown and changing environments. Emergent behaviour of a machine arises intelligence from the interactions of robots with its environment. Sensorial materials equipped with networks of embedded miniaturized smart sensors can support this behaviour. In this work an integrated autonomous decentralized sensor networks is shown providing perception in a robot arm manipulator. Each sensor network is connected to strain gauge sensors mounted on a flexible polymer surface, delivering spatial resolved information of external forces applied to the robot arm, required for example for obstacle avoidance or for manipulation of objects. Each autonomous sensor node provides communication, data processing, and energy management implemented on microchip level. Commonly a high number of strain gauge sensors are used to satisfy a high spatial resolution. Our approach uses advanced Artificial Intelligence and Machine Learning methods for the mapping of only a few non-calibrated and non-long-term stable noisy strain sensor signals to spatially resolved load information and a decentralized data processing approach to improve robustness. Robustness in the sensor network is provided by 1. autonomy of sensor nodes, 2. by smart adaptive communication to overcome link failures and to reflect changes in network topology, and 3. by using intelligent adaptive algorithms. Robust cooperation and distributed data processing is achieved by using Mobile Agent systems. Agent behaviour and cooperation is implemented on microchip level.
Publications 2012
[j12.1]
S. Bosse, F. Pantke, Distributed computing and reliable communication in sensor networks using multi-agent systems, Production Engineering, Research and Development, 2012, ISSN: 0944-6524,
DOI:10.1007/s11740-012-0420-8. Paper PDFPaper Online
There is a growing demand for robust distributed computing and systems in sensor networks. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. The focus of the application scenario lies on sensor networks and low-power, resource-aware single System-On-Chip (SoC) designs, i.e., for use in sensor-equipped technical structures and materials. We propose and compare two different data processing and communication architectures for the implementation of mobile agents in sensor networks consisting of single microchip low-resource nodes. Furthermore, a reliable smart communication protocol for incomplete and irregular networks are introduced. Two case studies show the suitability of agent-based approaches for distributed computing.
[c12.1]
S. Bosse, F. Pantke, F. Kirchner, Data Processing and Communication in Distributed Low-power Sensor Networks using Multi-agent Systems, Proceedings of the 1st Joint Symposium on System-integrated Intelligence: New Challenges for Product and Production Engineering, June 27th – 29th 2012: Hannover, Germany,
Special Session Enabling Technologies for Sensorial Materials – Taking sensor integration, 2012. Paper PDF
Recently emerging trends in engineering and micro-system applications such as the development of sensorial materials show a growing demand for autonomous networks of miniaturized smart sensors and actuators embedded in technical structures. With increasing miniaturization and sensor-actuator density, decentralized network and data processing architectures are preferred or required. A multi-agent system is used for a decentralized and self-organizing approach of data processing in a distributed system like a sensor network, enabling the mapping of distributed data sets to related information, for example, required for object manipulation with a robot manipulator. Traditionally, mobile agents are executed on generic computer architectures, which usually cannot easily be reduced to single-chip systems like they are required, e.g., in sensorial materials with high sensor node densities. We propose and compare two different data processing and communication architectures for the implementation of mobile agents in sensor networks consisting of single microchip low-resource nodes. The distributed programming model of mobile agents has the advantage of simplification and reduction of synchronization constraints owing to the autonomy of agents. We propose and compare two different data processing and communication architectures for the implementation of mobile agents in sensor networks consisting of single microchip low-resource nodes.
[c12.2]
T. Behrmann, C. Zschippig, M. Lemmel, S. Bosse, Rapid Control Prototyping for Energy management of self-powering Sensors and embedded Cyber Physical Systems, 1st Joint Symposium on System-integrated Intelligence: New Challenges for Product and Production Engineering, June 27th – 29th 2012: Hannover, Germany, Special Session Enabling Technologies for Sensorial Materials – Taking sensor integration, 2012.
[c12.3]
S. Bosse, F. Pantke, F. Kirchner, Distributed Computing in Sensor Networks Using Multi-Agent Systems and Code Morphing, Proceedings of the 11th International Conference on Artificial Intelligence and Soft Computing Conference ICAISC 2012, 29.4. – 3.5.2012, Zakapone,
Poland, Springer, 2012. Paper PDF
There is a growing demand for distributed computing and systems in sensor networks. We propose and show a parallel and distributed runtime environment for multi-agent systems that provides spatial agent migration ability by employing code morphing. The focus of the application scenario lies on sensor networks and low-power, resource-aware single System-On- Chip designs, used in sensor-equipped technical structures and materials. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves which actions are performed, and they are capable of reacting on the environment and other agents with flexible behaviour. Data processing nodes exchange code rather than data to transfer information. A part of the state of an agent is preserved within its own program code, which also implements the agent’s migration functionality. The practicability of the approach is shown using a simple distributed Sobel filter as an example.
[c12.4]
K. Tracht, S. Hogreve, S. Bosse, Intelligent Interpretation of Multiaxial Gripper Force Sensors, Proceedings of CIRP Conference on Assembly Technologies,
CATS 2012. Paper PDF
Mechanical grippers are key components of handling devices in automated assembly systems. For complex handling tasks these grippers can be equipped with additional force measuring modules. This paper presents the prototype of a gripper with fingers made of sensorial material. These gripper fingers contain six single force sensors and can measure forces along multiple axes. The results of the experimental investigations of the sensor performance are shown. It is also demonstrated how further information about the handling conditions can be derived by the computational combination of the sensor signals. A sensor network will enrich the capabilities of the gripper fingers.
[c12.5]
S. Bosse, F. Kirchner, Smart Energy Management and Energy Distribution in Decentralized Self-Powered Sensor Networks Using Artificial Intelligence Concepts, Proceedings of the Smart Systems Integration Conference 2012, Session 4, Zürich, Schweiz, 21 – 22 Mar. 2012,
ISBN: 978-3-8007-3423-8. Paper PDF
Sensorial materials equipped with embedded miniaturized smart sensors provide environmental information required for advanced machine and robotics applications. With increasing miniaturization and sensor-actuator density, decentralized self-supplied energy concepts and energy distribution architectures are preferred and required. Self-powered sensor nodes collect energy from local sources, but can be supplied additionally by external energy sources. Nodes in a sensor network can use communication links to transfer energy, for example, optical links are capable of transferring energy using Laser or LE diodes in conjunction with photo diodes on the destination side, with a data signal modulated on an energy supply signal. We propose and demonstrate a decentralized sensor network architecture with nodes supplied by 1. energy collected from a local source, and 2. by energy collected from neighbour nodes using smart energy management (SEM). Nodes are arranged in a two-dimensional grid with connections to their four direct neighbours. Each node can store collected energy and distribute energy to neighbour nodes. Each autonomous node provides communication, data processing, and energy management. There is a focus on single System-On-Chip (SoC) design satisfying low-power and high miniaturization requirements. Energy management is performed 1. for the control of local energy consumption, and 2. for collection and distribution of energy by using the data links to transfer energy. Typically, energy management is performed by a central controller in which a program is implemented [5], with limited fault robustness and the requirement of a well-known environment world model for energy sources, sinks, and storage. Energy management in a network envolves the transfer of energy. The loss of energy ε (in the range between 0 and 1) at each node occurring each time when “energy” is routed along different nodes from a source to a destination node (assuming N intermediate nodes) reduces overall efficiency dramatically in the order of η=εN. By using electrical connections, only negligible loss of energy can be expected in a distributed network, in contrast to optical and radio wave connections which have significant loss in the order of ε≅10-30% per node. Additionally, in the latter case there is no physical interaction between a source and a sink node requesting energy, thus requiring active management (routing). To overcome these limitations and to increase operational robustness, this work proposes smart energy management performed by using concepts from artificial intelligence. Initially, the sensor network is a distributed group of independent computing nodes. Interaction between nodes is required to manage and distribute information and energy. One common interaction model is the mobile agent. Different kinds of agents with different behaviours are used to negotiate energy demands and energy distribution and to implement group communication. A multi-agent system is a decentralized and self-organizing approach for data processing in a distributed system like a sensor network. Recent work shows the benefit and suitability of multi-agent systems used for energy management. Section 2. describes the communication architecture and some aspects of the technical implementation required for a communication link which is capable of transferring and receiving data and energy. Section 3. gives a short introduction to the multi-agent approach and the agent implementation used for smart energy management, targeting single microchip technologies (SoC designs). Section 4. finally discusses some results retrieved from simulation, showing the benefits of smart energy management using agents.
[c12.6]
S. Bosse, S. Hogreve, K. Tracht, Design of a Mechanical Gripper with an Integrated Smart Sensor Network for Multi-Axial Force Sensing and Perception of Environment, Proceedings of the Smart Systems Integration Conference 2012, Session 5, Zürich, Schweiz, 21 – 22 Mar. 2012,
ISBN: 978-3-8007-3423-8. Paper PDF
The dynamic process of grasping different kinds of objects which are pressure sensitive is difficult to handle with classical feedback controllers based on few force sensor values acquired and processed outside of the gripper structure. Side effects like slipping can not be detected at all or too late. Miniaturized smart sensors embedded in structures like grippers can significantly increase the perception of the environment with which a structure interacts. A high-density network of strain-gauge sensors distributed in/on the gripper structure providing local sensor signal-to-information computation can deliver much more suitable information. Traditionally, strain-gauge sensors are used to measure an applied force in a specific direction. The analog signal acquisition is difficult due to low noise immunity of weak input signals. External signal acquisition with a large distance from sensor to electronics raises noise and reduces signal-to-noise ratio and resolution. We propose and demonstrate the integration of an active smart sensor network into a mechanical gripper structure (finger). The network consists of several highly miniaturized low-power sensor nodes providing sensor signal acquisition, data processing, and communication. Each sensor node can handle up to two strain-gauge sensors detecting different forces at different positions of the gripper structure. The relation between strain and force is derived from FEM simulation of the gripper structure under certain load conditions. Each node performs sensor signal acquisition using a zooming ADC approach, sensor data evaluation, and auto-calibration. Hence, non-calibrated and non-long term-stable sensors can be integrated and used, a prerequisite for robust sensorial materials. It can be demonstrated that an integrated sensor network leads to increasing functionality and robustness. A smart communication protocol is used to provide robust and fault-tolerant communication between nodes and an external interface, for example, a generic processor-based controller. Beside the collection of single force values measured at different positions of the gripper, temporal and spatial composition information derived from the set of measured forces can be computed using data fusion, performed by the nodes of the sensor network itself using distributed computing algorithms. These are overload conditions, force gradients, object recognition and classification, and other higher-level information which can be computed. A multi-agent system is used for a decentralized and self-organizing approach of data processing in a distributed system like a sensor network, enabling the mapping of distributed data sets to related information required for object manipulation.
[c12.7]
K. Tracht, B. Kuhfuss, E. Brinksmeier, M. Busse, L. Kroll, S. Hogreve, M. Garbrecht, D. Lehmhus, M. Heinrich, S. Bosse, Enabling the factories of the future: The role of smart systems in manufacturing and robotics, Proceedings of the Smart Systems Integration Conference 2012, Special Session EpoSS, Zürich, Schweiz, 21 – 22 Mar. 2012, Presentation, 2012,
ISBN: 978-3-8007-3423-8. Presentation PDF
Smart Systems for Manufacturing, Smart Products, Smart Production, Intelligent tools and processes: Sensor integration, embedded systems, real-time data evaluation, improved process understanding and modeling that allow immediate reaction to processing deviations, data evaluation and knowledge accumulation that allow derivation of reaction strategies, predictive maintenance etc. Guidance through production assistants: Reduction of external control complexity by provision of multiple sensor/signal- and knowledgebased, simplified views on production status to operators. Intelligent production design process: Use of sensorial materials as product models for physical simulation/evaluation of production designs in experimental machine setups. User-friendly human-machine-interfaces (HMI): New ways to communicate status information and justify decisions in part or fully autonomous production systems.
Publications 2011
[j11.1]
Stefan Bosse, Frank Kirchner, Smart energy management and low-power embedded system design, SPIE Newsroom, 2011,
DOI:10.1117/2.1201106.003694. Paper PDFPublisher
Today there is an increasing demand for miniaturized smart sensors embedded in sensorial materials and smart actuators. Each sensor and actuator node provides some kind of sensor, electronics, data processing, and communication. With increasing miniaturization and sensor-actuator density, decentralized network and data processing architectures are preferred, but energy supply is still centralized. Using local energy-harvesting technologies a decentralized energy supply can be provided, too. Energy harvesting, for example using solar cells or thermo-electrical sources, actually delivers only low electrical power (due to technology or size constraints). We propose and demonstrate a design methodology for embedded systems satisfying low power requirements suitable for self-powered sensor and actuator nodes. This design methodology focuses on 1. smart energy management at runtime using advanced computer science algorithms (artifical intelligence) and 2. application-specific System-On-Chip (SoC) design using High-level synthesis at design time. Low-power systems are designed on algorithmic, rather than on technological level. Smart energy management is performed spatially at runtime by a selection from a set of different (implemented) algorithms classified by their demand of computation power, and temporally by varying data processing rates. It can be shown that power/ energy consumption of an application-specific SoC design depends strongly on computation complexity.
[c11.1]
T. Behrmann, C. Budelmann, M. Lemmel, S. Bosse, Tool chain for Harvesting, Simulation and Management of Energy for Sensorial Materials , Euromat 2011 Conference, Montpellier (Frankreich), 12.-15. September, 2011, 2011.
[c11.2]
F. Pantke, S. Bosse, D. Lehmhus, M. Lawo, M. Busse, Combining Simulation and Machine-Learning for Real-Time Load Identification in Sensorial Materials, Proceedings of the International Conference SIMBIO-M-2011, Simulations in BIO-Sciences and Multiphysics, 20-22.6.2011,
Marseille, France, 2011. Presentation PDF
Sensorial materials are biologically inspired technical systems – materials which are able to “feel” based on integrated miniaturized sensor networks, communication and data processing facilities. Seen from an engineering perspective, the nervous systems of animals complement the latter’s load-bearing structures, allowing active protection by, e.g., utilizing the notion of pain in cases of unexpected or accidental loads. Transfer of this basic principle to engineered technical structures ranging from bridges to robot arms, prosthetics, and implants affords studies in several fields. Simulation is one of them, both in the role of a supporting tool for layout and evaluation of such systems, and as a basis of internal sensor signal evaluation. Several links to classic biomaterials research exist: Smart implants can be envisaged which monitor healing, their condition, as well as changes in their integration with the natural structure, or prosthetics that autonomously infer, record, and monitor their loading history and give immediate feedback to their wearer and/or designer. The technical approach of the present study is to bring together multi-agent based simulation and finite element analysis. It is under the assumption that, in foreseeable future, the considerable computational power necessitated by the latter technique cannot be efficiently miniaturized to the scale required for embedment within the structure and, thus, is only available at design-time. As an intermediate step, to gain knowledge how sensorial structures can most effectively be built, an Artificial Intelligence based process for the construction of such structures was developed and realized in an experimental setup which uses machine learning for fast load location, force, and deformation identification. It is presented in this paper, along with evaluation results obtained in experiments using a finite element model and sensor data from a strain-gauge equipped plate which demonstrate the general practicability.
[c11.3]
T. Behrmann, M. Lemmel, S. Bosse, Energy management for self-powered sensor nodes, Energy Harvesting & Storage Europe 2011, München (Deutschland), 21.-22. Juni, 2011, 2011.
[c11.4]
Stefan Bosse, Thomas Behrmann, Smart Energy Management and Low-Power Design of Sensor and Actuator Nodes on Algorithmic Level for Self-Powered Sensorial Materials and Robotics, Proceedings of the SPIE Microtechnologies 2011 Conference, 18.4.-20.4.2011, Prague, Session EMT 101 Smart Sensors, Actuators and MEMS, 2011,
DOI:10.1117/12.888124. Paper PDFPresentation PDF
We propose and demonstrate a design methodology for embedded systems satisfying low power requirements suitable for self-powered sensor and actuator nodes. This design methodology focuses on 1. smart energy management at run-time and 2. application-specific System-On-Chip (SoC) design at design time, contributing to low-power systems on both algorithmic and technology level. Smart energy management is performed spatially at runtime by a behaviour-based or state-action-driven selection from a set of different (implemented) algorithms classified by their demand of computation power, and temporally by varying data processing rates. It can be shown that power/energy consumption of an application-specific SoC design depends strongly on computation complexity. Signal and control processing is modelled on abstract level using signal flow diagrams. These signal flow graphs are mapped to Petri Nets to enable direct high-level synthesis of digital SoC circuits using a multi-process architecture with the Communicating- Sequential-Process model on execution level. Power analysis using simulation techniques on gate-level provides input for the algorithmic selection during run-time of the system, leading to a closed-loop design flow. Additionally, the signal flow approach enables power management by varying the signal flow and data processing rates depending on actual energy consumption, estimated energy deposit, and required Quality-of-Service.
[c11.5]
Stefan Bosse, Hardware-Software-Co-Design of Parallel and Distributed Systems Using a unique Behavioural Programming and Multi-Process Model with High-Level Synthesis, Proceedings of the SPIE Microtechnologies 2011 Conference, 18.4.-20.4.2011, Prague, Session EMT 102 VLSI Circuits and Systems, 2011,
DOI:10.1117/12.888122. Paper PDFPresentation PDF
A new design methodology for parallel and distributed embedded systems is presented using the behavioural hardware compiler ConPro providing an imperative programming model based on concurrently communicating sequential processes (CSP) with an extensive set of inter-process-communication primitives and guarded atomic actions. The programming language and the compiler-based synthesis process enables the design of constrained power- and resource-aware embedded systems with pure Register-Transfer-Logic (RTL) efficiently mapped to FPGA and ASIC technologies. Concurrency is modelled explicitly on control- and datapath level. Additionally, concurrency on data-path level can be automatically explored and optimized by different schedulers. The CSP programming model can be synthesized to hardware (SoC) and software (C,ML) models and targets. A common source for both hardware and software implementation with identical functional behaviour is used. Processes and objects of the entire design can be distributed on different hardware and software platforms, for example, several FPGA components and software executed on several microprocessors, providing a parallel and distributed system. Inter-system-, inter-process-, and object communication is automatically implemented with serial links, not visible on programming level. The presented design methodology has the benefit of high modularity, freedom of choice of target technologies, and system architecture. Algorithms can be well matched to and distributed on different suitable execution platforms and implementation technologies, using a unique programming model, providing a balance of concurrency and resource complexity.
[c11.6]
Stefan Bosse, Thomas Behrmann, Frank Kirchner, Smart Energy Management and Low-Power Design of Embedded Systems on Algorithmic Level for Self-Powered Sensorial Materials and Robotics, Proceedings of the Smart Systems Integration Conference 2011, Session 4, Dresden, 22 – 23 Mar. 2011, VDE VERLAG GMBH, 2011,
ISBN: 978-3-8007-3324-8. Paper PDFPoster PDF
A new design methodology for low-power embedded systems is presented which is based on advanced algorithms from computer science. System-On- Chip architecture, power analysis, and advanced system modelling methodologies are used.
[c11.7]
F. Pantke, S. Bosse, D. Lehmhus, M. Lawo, An Artificial Intelligence Approach Towards Sensorial Materials, Proceedings of The Third International Conference on Future Computational Technologies and Applications (Future Computing 2011),
Sept. 25-30, 2011. Paper PDF
Sensorization aims at equipping technical structures with an analog of a nervous system by providing a network of sensors and communication facilities that link them. The objective is that, instead of having been designed to loads and tested to conditions, a structure can experience and report design constraint violations by means of realtime self-monitoring. Specialized electronic components and computational algorithms are needed to derive meaning from the combined signals. For this task, artificial intelligence approaches constantly gain importance; the more so as the trend of ever increasing sensor network size and density suggests that sensor and structure may soon become one, forming a sensorial material. Current simulation techniques capture many aspects of sensor networks and structures. For decision making and communication, the Intelligent Agent paradigm is an accepted approach, as is finite element analysis for structural behavior. To gain knowledge how sensorial structures can most effectively be built, an artificial intelligence based process for the construction of such structures was developed that uses machine learning methods for fast load inference. It is presented in this paper, along with evaluation results obtained in experiments using a finite element model of a strain gauge equipped plate which demonstrate the general practicability.
Publications 2010
[c10.1]
Stefan Bosse, Hardware Synthesis of Complex System-on-Chip-Designs for Embedded Systems Using a Behavioural Programming and Multi-Process Model, Proceedings of the 55th IWK – Internationales Wissenschaftliches Kolloquium, Session C4,
Ilmenau, 13 – 17 Sept. 2010 Paper PDFPresentation PDF
Embedded Systems used for control, for example in Cyber-Physical-Systems (CPS), perform the monitoring and control of complex physical processes using applications running on dedicated execution platforms in a resource-constrained manner. Application-specific System-On-Chip (SoC) designs providing the execution platform have advantages compared with traditionally used program-controlled multiprocessor architectures. SoC designs can be modelled on structural and behavioural level. The behavioural level is generally a more sophisticated modelling level. In the context of CPS, these are mainly reactive systems with dominant and complex control paths. The major contribution to concurrency appears on control path level. A new SoC design methodology is presented using the behavioural hardware compiler ConPro providing an imperative programming model based on concurrently communicating sequential processes (CSP) with an extensive set of interprocess- communication primitives and guarded atomic actions. The programming language and the compiler-based synthesis process enables the design of constrained power- and resource-aware embedded systems with pure Register- Transfer-Logic efficiently mapped to FPGA and ASIC technologies. Concurrency is modelled explicitly on control- and datapath level. Additionally, concurrency on datapath level can be explored and optimized automatically by different schedulers. The CSP programming model can be synthesized to different levels, not only used for hardware circuit synthesis: software models (C, ML), intermediate μCode, RTL state level, and finally VHDL. A common source for both hardware and software implementation with identical functional behaviour is used.
[c10.2]
T. Behrmann, C. Zschippig, M. Lemmel, S. Bosse, Toolbox for Energy Analysis and Simulation of self-powered Sensor Nodes, Proceedings of the 55th IWK – Internationales Wissenschaftliches Kolloquium, Session A3, Ilmenau, 13 – 17 Sept. 2010
As the numbers of available high performance but low-power embedded system rise, new application scenarios for tailored sensor systems get in reach to be implemented. In some cases battery powered or selfpowered systems are needed, e.g. in the context of wireless sensor networks. The question has to be considered, if the system has enough energy and always enough power to fulfil its task. Often the answer can only be given, in case the analysis is carried out in the context of the real application. A simulation on this will only be meaningful, if it describes the environmental condition of this context as well. Therefore we propose a simulation toolbox for energy and power analysis of independent sensor nodes. This presentation shows the foundations of a new simulation toolbox, a tool for designing modular sensor systems. The focus of this tool will be on the economic and efficient use of power and energy on the level of embedded systems. The base of the toolbox is a growing number of simulation blocks modelling the power behaviour of embedded modules like energy sources, converters, storage and load. A set of tools for observing losses, energy throughput and power lags, assists the system designer to set up an economic solution. A strong emphasis lies on the modelling of modern energy harvesting principles and the embedding physical situation. One main goal of this research work is to overcome the principle of always oversizing the power supply of electric system for the worst case. Instead a situation-dependant adaptive energy management will set different operation modes of embedded systems to cope with power supply and energy situation. Therefore these systems will act much more reliable than the traditional ones. To save energy, the different operation modes will lead to a tailored sensor data processing. Instead of using a full micro processor, the next development steps are configurable hardware blocks. Therefore the load models will consider different implementations on work task level. A simple but comprehensible example will show the possibilities of system analysis and should lead to a productive discussion about future enhancements from the point of view of system designers and users.
[c10.3]
Stefan Bosse, Dirk Lehmhus, System-On-Chip Design and Communication in Embedded Wired High-Density Sensor Networks: A Contribution from Behavioural High-Level Synthesis and Functional Printing, E-MRS 2010 Spring Meeting, June 7-11, 2010, Congress Center,
Strasbourg, France, 2010. Paper PDFPoster PDF
Communication gains impact with increasing miniaturization and densities in sensor- and actuator networks, especially in the context of robotics and sensorial materials - they require basically wired networks. The sensor network is a massive parallel computer performing data fusion with smart nodes, requiring a functional system design flow. Traditionally, there are two different ways to model and implement System-on-Chip designs (SoC) used in highly miniaturized sensor- and actuator networks: using a structural and/or · using a behavioural model level. Parallelism is mostly required to satisfy system latency time, resource, and power constraints.
[c10.4]
Florian Pantke, Jörn Sprado, Edit Pal, Stefan Bosse, Michael Lawo, Evaluating Simulation Techniques for Sensorial Materials, Symposium A : From embedded sensors to sensorial materials of the E-MRS 2010 Spring Meeting, Congress Center in Strasbourg (France) from June 7 to 11, 2010
Copying nature, engineering science aims at providing technical structures with an analogue of a nervous system in terms of networks of sensors, communication facilities linking these and specific hardware as well as computational methods to derive meaning from their combined signals. For the latter task, artificial intelligence approaches constantly gain importance; the more so as the trend of ever increasing sensor network size and density suggests that sensor and structure may soon become one, forming a sensorial material. Current simulation techniques capture many aspects of sensor networks and structures. For decision making and communication, multi agent based simulation (MABS) is an accepted method, as is finite element analysis (FEA) for structural behaviour. To take advantage of developments in sensorisation and gain knowledge of what sensorial materials mean, a monitoring system was designed which is able to deduce the loads applied as well as violations of design rules from sensor information and structural subsystem response. The concept behind this is that instead of having been designed to loads and tested to conditions, the structural model, and, in its physical realisation, the material or structure, can experience and report design constraint violations. We present first results of the modelling and simulation approach obtained in experiments done with a strain gauge equipped plate using optical surface metrology to gain reference values of local strain.
[c10.5]
Stefan Bosse, Dirk Lehmhus, Smart Communication in a Wired Sensor- and Actuator-Network of a Modular Robot Actuator System using a Hop-Protocol with Delta-Routing, Proceedings of the Smart Systems Integration conference, Como, Italy, 23-24.3.2010 (2010), 2010,
ISBN: 978-3-8007-3208-1. Paper PDFPoster PDF
Communication gains impact with increasing minaturization and densities in sensorand actuator networks, especially in the context of robotics and sensorial materials. System-On-Chip design on Register-Transfer-Logic (RTL) level using FPGA and ASIC technologies enables 1. small sized integration of sensors and data processing units (DPU) scaled down to one-chip designs using ASIC- and MEMS technology, and 2. satisfies low power requirements, required especially for high density sensor networks sourced by environmental energy (energy harvesting). But using resource and power aware designs constraint algorithm complexity significantly, concerning control, data processing, and communication. Software based communication approaches, like TCP/IP based, can not be implemented entirely in hardware, and they are still too complex. Another important quality of communication in sensor- and actuator networks is reliability and robustness against link failures Thus, communication protocols aligned to low resource and low power designs are required. Most actual work in communication focus on wireless networks [4]. But sensorial materials and highly integrated robotics systems require basically wired networks. The network topology of sensor- and actuator networks is in generally distributed and decentralised, and nodes of such a network have different computing power and storage, resulting in the following constraints for protocol design: 1. message based point-to-point communication, 2. application specific scalable protocol regarding network and data sizes to satisfy A. low power design, and B. reducing computing and storage requirements, 3. no unique node addressing (not usable in high density sensor networks), 4. simple routing strategies, but finally 5. robustness related to alternative path finding.
[c10.6]
Stefan Bosse, Synthesis of Parallel and System-on-Chip Designs With Behavioural High-Level Hardware-Synthesis Using Communicating Sequential Processes and the ConPro-Framework (Technical Report),
BSSLAB 2010. Report PDF
Traditionally, there are two different ways to model and implement System-On-Chip-Designs (SoC): using a structural and/or a behavioural level. The structural level decomposes a SoC into independent submodules interacting with each other using centralized or distributed networks and communication protocols. The behavioural level usually describes the behaviour of the full design interacting with the environment. Complex reactive systems with dominant and complex control paths play an increasing role in SoC-design. The major contribution to concurrency appears on control path level. This article gives an introduction to SoC-design methodology using the behavioural hardware compiler ConPro providing a programming model based on concurrent communication sequential processes (CSP) with an extensive set of interprocess-communication primitives. An extended case study of a communication protocol used in high density sensor-actuator-networks should demonstrate the design of a SoC for a robot actuator. The communication protocol is suited for high-density intra- and interchip networks.
Publications 2009
[c09.1]
J. Hilljegerdes, P. Kampmann, S. Bosse, F. Kirchner, Development of an Intelligent Joint Actuator Prototype for Climbing and Walking Robots (Proceeding), 12th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, 09-11 September 2009, Istanbul, Turkey, 2009, DOI:10.1142/9789814291279_0115.
In this paper, a new joint actuator is introduced which builds the basis for the newly developed SpaceClimber robot by the German Research Center for Artificial Intelligence. Based on in-house developed joint actuators for ambu-lating robots, this complete new design combines performance, stability, and space-related components. The newly developed on-board electronics enables the possibility of a biologically inspired functionality like decentralized au-tonomous joint control. In this paper, we explain the design and the control architecture of the actuator. We describe the selected components and present the fully functional prototype. The results of the first performance experiments are presented.
[c09.2]
D. Lehmhus, M. Busse, H. W. Zoch, W. Lang, S. Bosse, F. Kirchner, Sensor usage in the transport industry – a review of current concepts and future trends (Conference), European Congress and Exhibition on Advanced Materials and Processes, 7-10. Sep.2009, Glasgow UK, 2009.
[r09.1]
S. Bosse, ConPro: Rule-Based Mapping of an Imperative Programming Language to RTL for Higher-Level-Synthesis Using Communicating Sequential Processes
(Techreport) Report PDF
The ConPro programming language, an new enhanced imperative programming language is mapped to Register-Transfer-Logic using a higher-level-synthesis approach performed by the syn- thesis tool ConPro. In contrast to other approaches using modified existing software languages like C, this language is designed from scratch providing a consistent model for both hardware de- sign and software programming. The programming model and the language provide parallelism on control path level using a multi-process model with communicating sequential processes (CSP), and on data path level using bounded program blocks. Each process is mapped to a Finite- State-Machine and is executed concurrently. Additionally, program blocks can be parameterized and can control the synthesis process (scheduling and allocation). Synthesis is based on a non- iterative, multi-level and constraint selective rule-set based approach, rather than on a traditional constrained iterative scheduling and allocation approach. Required inter-process communication is provided by a set of primitives, entirely mapped to hardware, already established in concurrent software programming (multi-threading), implemented with an abstract data type object model and method-based access. It is demonstrated that this synthesis approach is efficient and stable enough to create complex circuits reaching the million gates boundary.
Publications 2008
title: Publications 2006-
Publications 2006
[j06.1]
S. Bosse, VAMNET: the Functional Approach to Distributed Programming (Article), SIGOPS Oper. Syst. Rev., 40, pp. 108-114, 2006,
DOI:10.1145/1151374.1151376. Paper PDFPublisher
This article gives a design overview of a new reliable distributed operating system environment, combining the world of functional and distributed programming using a virtual machine approach for hiding system dependencies, offering rapid prototyping facilities. The basic operating system concepts used are derived from the Amoeba operating system by Andrew Tanenbaum and his work group, developed 20 years ago. VAMNET is not only a native operating system. It is a hybrid solution for expanding widely used operating systems like UNIX with a distributed execution environment.
[c06.1]
Dirk Spenneberg, Stefan Bosse, Jens Hilljegerdes, Andreas Strack, Heiko Zschenker, Control of an Bio-Inspired Four-Legged Robot for Exploration of Uneven Terrain (Proceeding), Proc. of ASTRA 2006 Workshop, ESA-ESTEC. Noordwijk,
NL, 2006. Paper PDF
This paper describes the four-legged ARAMIES robot which was built on the experiences gained with a first integration study. It describes the revised mechanics. The new modular electronics low-level control concept is presented and the developed FPGA-based joint control is explained in detail. Furthermore an overview on the software control approach is given and the new ground contact detection module using an IR-sensor is explained.
Publications 2005
[b05.1]
S. Bosse, The VAMNET Book - The Virtual Amoeba Machine Environment, AMUNIX, and the VX-Amoeba System (Book),
BSSLAB, 2005. Book PDF
The VAMNET is a hybrid operating system environment for distributed applications in a heterogeneous environment, concerning both the hardware architectures used and operating systems already present, for example the UNIX−OS. The VAMNET consists of several parts. Some of them can operate standalone. All of them build up a hybrid distributed operating system environment. Fields of application 1. Distributed measuring and data acquisition systems, for example remote digital camera servers connected with an ethernet network equipped with digital imaging software. 2. The native Amoeba kernel is very well suited for embedded systems, like PC104 single board equipmment. 3. Distributed systems for machine control. 4. High performance parallel computing and other distributed numerical computations. 5. Distributed filesystems on the top of standard operating systems. 6. Distributed remote (wireless) robot control. 7. Educational tool for the convinient study of distributed services and operating systems. Advantages of a hybrid system 1. The basic concepts of the distrubted operating system Amoeba are avialable with common operating systems with a convinient desktop environment. New operating systems mostly lack of actual device drivers, especially on the i86−pc platform with a wide spectrum of available hadrware. 2. For specializied (perhaps embedded) machines, for example data acquisition systems, or hadware device reduced numeric cluster machines, the native Amoeba kernel is the best choice, featuring a modern and clean microkernel, and exploring the power of the Amoeba system. 3. Both worlds, embedded and specialized computers and desktop computers, can be merged with simple but powerfull methods and concepts using a hybrid system solution. Each machine gets the system which fits best.
Publications 2003
[c03.1]
S. Patzelt, S. Bosse, G. Goch, Laser optical surface characterization of complex optical elements in the nanometric range (Proceeding), Proc. of the 4th euspen International Topical Conference, Aachen, Germany, 19.-20.05.2003, S. 519-522., 2003,
ISBN: 3926832304. Publisher
[c03.2]
S. Patzelt, S. Bosse, G. Goch, Surface characterization of optical elements based on monochromatic scattered light techniques (Proceeding), Proc. of the Sensor 2003 – 11th International Conference, SENSOR Proceedings, B8.3, Nürnberg 2003,
S. 305-310. Paper PDF
Surface roughness in the nanometric range represents one of the key properties of high quality optical elements (lenses, mirrors, replication tools and molds). Besides the roughness local defects (scratches, abrasions, pits, cracks and chips) influence the optical functionality. Furthermore, in the case of structured surfaces, pattern deviations (e.g., insufficient periodicity) affect the structure function. In general, such surfaces are characterized with conventional measuring devices, like tactile profilometers, white light interferometers, confocal microscopes, scanning force microscopes, or near field acoustic microscopes. They measure the surface topography in detail and calculate roughness parameters from the measuring data. The surface assessment of optical components is also part of the Transregional Cooperative Research Center SFB/TR4 "Process Chains for the Replication of Complex Optical Elements" funded by the DFG (Deutsche Forschungsgemeinschaft). One objective within this research center is to lay the scientific foundations of microtopography characterization for a deterministic and economical mass production of optical elements with a complex geometry. As the elements are used to shape optical beams, the investigations focus on optical measuring methods. In the case of mass production the measuring methods for both the production tools (e.g., molds) and the products should additionally be capable of in-situ measurements or even in-process measurements. Laser measuring principles based on scattered light and speckle correlation processes are suitable for this task. Scattered light techniques are generally parametric, i.e. integral roughness parameters like Ra and Rq (ISO 4287) can be extracted directly from one measurement without reproducing the 3D-topography. The view field dimensions are in the millimeter range. Therefore, scattered light techniques ensure the required short measuring times and high clock rates, i.e. they show in-process capabilities.
Publications 2002
[t02.1]
S. Bosse, An experimental Laser light-scattering method for measuring velocity gradients in non-newtonian fluids with high viscosity (PhD Thesis), University of Bremen,
Bremen, Germany, 2002. Thesis PDF
The goal of this PhD thesis was to study local velocity gradients, i. To investigate local velocity gradients of viscoelastic fluids using different types of flow with a laser scattering optical method. From the measured velocity gradient components, conclusions are to be drawn on the expected non-Newtonian behavior of these highly viscous fluids and compared with the models used in rheology for non-Newtonian fluids and analyzed. Furthermore, the suitability of the light scattering method used is to be investigated and the limits of its possible uses to be demonstrated. For measuring individual gradient components of the local velocity field in the flowing fluid, which may be variable in time, or a linear combination of these, a double pulse optical pulse scattered light method (double pulse strophometry) is applied, which is characterized by ease of use and manages without explicit calibration. This method was originally used to measure velocity gradients in laminar and turbulent water flows. In this measuring method, the scattered light of a pulsed laser beam of scattering particles contained in the liquid is picked up by a one-dimensional or two-dimensional light detector. Most commonly used are CCD detectors (Charged Coupled Device Cameras).
Publications 2000
[j00.1]
S. Bosse, W. Staude, Measurement of rotation and distortion of opaque surfaces by laser light scattering (Article), Meas. Sci. Technol., 11,
pp. 1557-1564, 2000. Paper PDF
We present an experimental method to measure velocity gradients caused by distortions and rotations of arbitrary light scattering surfaces. The method is sensitive, gauge free and has a fairly high resolution. It is based on the scattering of coherent plane light wave at the surface and the evaluation of the intensity pattern of the scattered light in the far field by use of a CCD-camera. By proper choosing the scattering geometry one can measure definite components of the velocity gradient of the surface.
Publications 1998
[t98.1]
S. Bosse, Measurement of Rotation and Distortion of Rough Solidstate Surfaces using coherent light Scattering (Diploma thesis), University of Bremen,
Bremen, Germany, 1998 Thesis PDF
Universität Koblenz - FB Informatik
Universität Koblenz - FB Informatik
