Technologien für die intelligente Automation

Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2016

Editors: Beyerer, Jürgen, Niggemann, Oliver, Kühnert, Christian (Eds.)

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  • Includes the full proceedings of the 2016 ML4CPS – Machine Learning for Cyber Physical Systems Conference
  • Presents recent and new advances in automated machine learning methods
  • Provides an accessible and succinct overview on machine learning for cyber physical systems
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eBook $169.00
price for USA in USD (gross)
  • ISBN 978-3-662-53806-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
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  • Immediate eBook download after purchase
Softcover $219.99
price for USA in USD
  • ISBN 978-3-662-53805-0
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  • Usually dispatched within 3 to 5 business days.
About this book

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It  contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. 

Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.  


About the authors

Prof. Dr.-Ing. Jürgen Beyerer is Professor at the  Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.

Prof. Dr. Oliver Niggemann is Professor for Embedded Software Engineering. His research interests are in the field of Distributed Real-time Software and in the fields of analysis and diagnosis of distributed systems. He is a board member of the inIT and a senior researcher at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.

Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring.   

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Table of contents (8 chapters)

Table of contents (8 chapters)
  • A Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths

    Dripke, Caren (et al.)

    Pages 1-8

  • Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection

    Jacoby, Michael (et al.)

    Pages 9-15

  • Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment

    Ogbechie, Alberto (et al.)

    Pages 17-24

  • A Modular Architecture for Smart Data Analysis using AutomationML, OPC-UA and Data-driven Algorithms

    Kühnert, Christian (et al.)

    Pages 25-33

  • Cloud-based event detection platform for water distribution networks using machine-learning algorithms

    Bernard, Thomas (et al.)

    Pages 35-43

Buy this book

eBook $169.00
price for USA in USD (gross)
  • ISBN 978-3-662-53806-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $219.99
price for USA in USD
  • ISBN 978-3-662-53805-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning for Cyber Physical Systems
Book Subtitle
Selected papers from the International Conference ML4CPS 2016
Editors
  • Jürgen Beyerer
  • Oliver Niggemann
  • Christian Kühnert
Series Title
Technologien für die intelligente Automation
Series Volume
3
Copyright
2017
Publisher
Springer Vieweg
Copyright Holder
Springer-Verlag GmbH Germany
eBook ISBN
978-3-662-53806-7
DOI
10.1007/978-3-662-53806-7
Softcover ISBN
978-3-662-53805-0
Series ISSN
2522-8579
Edition Number
1
Number of Pages
VII, 72
Number of Illustrations
5 b/w illustrations, 19 illustrations in colour
Topics