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  • Conference proceedings
  • © 2016

Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2015

  • 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
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (14 papers)

  1. Front Matter

    Pages I-VI
  2. Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach

    • Christoph Ide, Michael Nick, Dennis Kaulbars, Christian Wietfeld
    Pages 15-22
  3. Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission

    • Adrian Cubillo, Suresh Perinpanayagam, Marcos Rodriguez, Ignacio Collantes, Jeroen Vermeulen
    Pages 33-44
  4. Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases

    • S. Windmann, J. Eickmeyer, F. Jungbluth, J. Badinger, O. Niggemann
    Pages 45-50
  5. Towards a novel learning assistant for networked automation systems

    • Yongheng Wang, Michael Weyrich
    Pages 51-57
  6. Efficient Image Processing System for an Industrial Machine Learning Task

    • Kristijan Vukovic, Kristina Simonis, Helene Dörksen, Volker Lohweg
    Pages 59-66
  7. Kognitive Architektur zum Konzeptlernen in technischen Systemen

    • Alexander Diedrich, Andreas Bunte, Alexander Maier, Oliver Niggemann
    Pages 75-85
  8. Implementation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation

    • André Mueß, Jens Weber, Raphael-Elias Reisch, Benjamin Jurke
    Pages 87-93
  9. Machine-specific Approach for Automatic Classification of Cutting Process Efficiency

    • Christian Walther, Frank Beneke, Luise Merbach, Hubertus Siebald, Oliver Hensel, Jochen Huster
    Pages 95-102
  10. Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems

    • Adrian Böckenkamp, Frank Weichert, Jonas Stenzel, Dennis Lünsch
    Pages 111-121

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.

Editors and Affiliations

  • inIT, Hochschule Ostwestfalen-Lippe, Lemgo, Germany

    Oliver Niggemann

  • IOSB, Fraunhofer, Karlsruhe, Germany

    Jürgen Beyerer

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

  • DOI: https://doi.org/10.1007/978-3-662-48838-6

  • Publisher: Springer Vieweg Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2016

  • Softcover ISBN: 978-3-662-48836-2Published: 20 February 2016

  • eBook ISBN: 978-3-662-48838-6Published: 19 February 2016

  • Series ISSN: 2522-8579

  • Series E-ISSN: 2522-8587

  • Edition Number: 1

  • Number of Pages: VI, 121

  • Number of Illustrations: 12 illustrations in colour

  • Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Knowledge Management

Buy it now

Buying options

eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access