Technologien für die intelligente Automation

Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2015

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

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  • Includes the full proceedings of the 2015 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 $129.00
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  • ISBN 978-3-662-48838-6
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Softcover $169.99
price for USA in USD
  • ISBN 978-3-662-48836-2
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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 Lemgo, October 1-2, 2015.

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. Oliver Niggemann ist seit November 2008 Mitglied des inIT. Er vertritt das Fachgebiet Embedded Software Engineering in der Lehre und forscht im inIT in den Bereichen Verteilte Echtzeit-Software und der Analyse und Diagnose verteilter Systeme. Gleichzeitig forscht Prof. Niggemann im Fraunhofer-Anwendungszentrum Industrial Automation (INA) in Lemgo.

Prof. Dr.-Ing. Jürgen Beyerer ist in Personalunion Inhaber des Lehrstuhls für Interaktive Echtzeitsysteme an der Fakultät für Informatik und Leiter des Fraunhofer IOSB. Die Schwerpunkte in Forschung und Lehre am Lehrstuhl für Interaktive Echtzeitsysteme liegen auf den Themen: automatische Sichtprüfung und Bildauswertung, Mustererkennung und Signal- und Informationsverarbeitung.

Table of contents (14 chapters)

Table of contents (14 chapters)
  • Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control

    Pages 1-8

    Diaz, Javier (et al.)

  • Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks

    Pages 9-14

    Korthals, Timo (et al.)

  • Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach

    Pages 15-22

    Ide, Christoph (et al.)

  • Towards Optimized Machine Operations by Cloud Integrated Condition Estimation

    Pages 23-31

    Brecher, C. (et al.)

  • Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission

    Pages 33-44

    Cubillo, Adrian (et al.)

Buy this book

eBook $129.00
price for USA in USD
  • ISBN 978-3-662-48838-6
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $169.99
price for USA in USD
  • ISBN 978-3-662-48836-2
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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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 2015
Editors
  • Oliver Niggemann
  • Jürgen Beyerer
Series Title
Technologien für die intelligente Automation
Copyright
2016
Publisher
Springer Vieweg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-662-48838-6
DOI
10.1007/978-3-662-48838-6
Softcover ISBN
978-3-662-48836-2
Series ISSN
2522-8579
Edition Number
1
Number of Pages
VI, 121
Number of Illustrations
12 illustrations in colour
Topics