Technologien für die intelligente Automation
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Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2018

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

  • Includes the full proceedings of the 2018 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, industry 4.0 and IOT
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eBook  
  • ISBN 978-3-662-58485-9
  • This book is an open access book, you can download it for free on link.springer.com
Softcover $59.99
price for USA in USD
  • ISBN 978-3-662-58484-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

This Open Access proceedings 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, October 23-24, 2018. 

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.

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.   

Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.


Table of contents (15 chapters)

Table of contents (15 chapters)
  • Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project

    Beecks, Christian (et al.)

    Pages 1-6

  • Deduction of time-dependent machine tool characteristics by fuzzy-clustering

    Frieß, Uwe (et al.)

    Pages 7-17

  • Unsupervised Anomaly Detection in Production Lines

    Graß, Alexander (et al.)

    Pages 18-25

  • A Random Forest Based Classifier for Error Prediction of Highly Individualized Products

    Gröner, Gerd

    Pages 26-35

  • Web-based Machine Learning Platform for Condition- Monitoring

    Bernard, Thomas (et al.)

    Pages 36-45

Buy this book

eBook  
  • ISBN 978-3-662-58485-9
  • This book is an open access book, you can download it for free on link.springer.com
Softcover $59.99
price for USA in USD
  • ISBN 978-3-662-58484-2
  • 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 2018
Editors
  • Jürgen Beyerer
  • Christian Kühnert
  • Oliver Niggemann
Series Title
Technologien für die intelligente Automation
Series Volume
9
Copyright
2019
Publisher
Springer Vieweg
Copyright Holder
The Editor(s) (if applicable) and The Author(s)
eBook ISBN
978-3-662-58485-9
DOI
10.1007/978-3-662-58485-9
Softcover ISBN
978-3-662-58484-2
Series ISSN
2522-8579
Edition Number
1
Number of Pages
VII, 136
Topics