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© 2016

Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2015

  • Oliver Niggemann
  • Jürgen Beyerer
  • 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

Conference proceedings

Part of the Technologien für die intelligente Automation book series (TIA)

Table of contents

  1. Front Matter
    Pages I-VI
  2. Christoph Ide, Michael Nick, Dennis Kaulbars, Christian Wietfeld
    Pages 15-22
  3. Adrian Cubillo, Suresh Perinpanayagam, Marcos Rodriguez, Ignacio Collantes, Jeroen Vermeulen
    Pages 33-44
  4. S. Windmann, J. Eickmeyer, F. Jungbluth, J. Badinger, O. Niggemann
    Pages 45-50
  5. Yongheng Wang, Michael Weyrich
    Pages 51-57
  6. Kristijan Vukovic, Kristina Simonis, Helene Dörksen, Volker Lohweg
    Pages 59-66
  7. Alexander Diedrich, Andreas Bunte, Alexander Maier, Oliver Niggemann
    Pages 75-85
  8. André Mueß, Jens Weber, Raphael-Elias Reisch, Benjamin Jurke
    Pages 87-93
  9. Christian Walther, Frank Beneke, Luise Merbach, Hubertus Siebald, Oliver Hensel, Jochen Huster
    Pages 95-102
  10. Adrian Böckenkamp, Frank Weichert, Jonas Stenzel, Dennis Lünsch
    Pages 111-121

About these proceedings

Introduction

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.

Keywords

Big Data Condition Monitoring Data Mining Image Processing and Diagnosis Machine Learning Predictive Maintenance

Editors and affiliations

  • Oliver Niggemann
    • 1
  • Jürgen Beyerer
    • 2
  1. 1.inITHochschule Ostwestfalen-LippeLemgoGermany
  2. 2.IOSBFraunhoferKarlsruheGermany

About the editors

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.

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
  • Series Abbreviated Title Technologien für die intelligente Automation
  • DOI https://doi.org/10.1007/978-3-662-48838-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2016
  • Publisher Name Springer Vieweg, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Softcover ISBN 978-3-662-48836-2
  • eBook ISBN 978-3-662-48838-6
  • Edition Number 1
  • Number of Pages VI, 121
  • Number of Illustrations 0 b/w illustrations, 12 illustrations in colour
  • Topics Computational Intelligence
    Data Mining and Knowledge Discovery
    Knowledge Management
  • Buy this book on publisher's site
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