Publications 2018

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S. Bosse, D. Lehmhus, W. Lang, M. Busse (Ed.), Material-Integrated Intelligent Systems: Technology and Applications, Wiley, ISBN: 978-3-527-33606-7 (2018)
PUBLISHER

Book Cover

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
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S. Bosse, Chapter Networks and Communication Protocols, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley, ISBN: 978-3-527-33606-7 (2018)
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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)
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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)
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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)
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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)
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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)
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S. Bosse, Chapter Robustness and Data Fusion, in Material-Integrated Intelligent Systems: Technology and Applications, Wiley, ISBN: 978-3-527-33606-7 (2018)
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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)
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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
PDF PRESENTATION
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S. Bosse, D. Lehmhus, Computing within Materials: Self-Adaptive Materials and Self-organizing Agents, Smart Systems Integration conference, 11-12.4.2018, Dresden, Germany
PDF PRESENTATION
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.
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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
PDF PRESENTATION
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.
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S. Bosse, Data mining with Machine Learning for the Social Sciences, Invited Keynote Talk, 18.5.2018, Bremen, Computational Social Sciences Talks, BIGSSS, SOCIUM, University of Bremen, Jacobs University Bremen, 2018
PDF PRESENTATION
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