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Table of contents

  1. Front Matter
    Pages I-VII
  2. Christian Beecks, Shreekantha Devasya, Ruben Schlutter
    Pages 1-6 Open Access
  3. Uwe Frieß, Martin Kolouch, Matthias Putz
    Pages 7-17 Open Access
  4. Alexander Graß, Christian Beecks, Jose Angel Carvajal Soto
    Pages 18-25 Open Access
  5. Thomas Bernard, Christian Kühnert, Enrique Campbell
    Pages 36-45 Open Access
  6. Jonathan Krauß, Maik Frye, Gustavo Teodoro Döhler Beck, Robert H. Schmitt
    Pages 46-57 Open Access
  7. Oliver Rettig, Silvan Müller, Marcus Strand, Darko Katic
    Pages 58-65 Open Access
  8. Johannes Sailer, Christian Frey, Christian Kühnert
    Pages 66-76 Open Access
  9. Anke Stoll, Norbert Pierschel, Ken Wenzel, Tino Langer
    Pages 77-86 Open Access
  10. Klaudia Kovacs, Fazel Ansari, Claudio Geisert, Eckart Uhlmann, Robert Glawar, Wilfried Sihn
    Pages 87-96 Open Access
  11. Carlos Paiz Gatica, Alexander Boschmann
    Pages 107-115 Open Access
  12. Jorge Francés-Chust, Joaquín Izquierdo, Idel Montalvo
    Pages 133-136 Open Access

About these proceedings

Introduction

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.

The Editors

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.

Keywords

Machine Learning Artificial Intelligence Cognitive Robotics Internet of Things Computational intelligence Cyber-Physical Systems Computer-based algorithms Smart grid Open Access

Editors and affiliations

  • Jürgen Beyerer
    • 1
  • Christian Kühnert
    • 2
  • Oliver Niggemann
    • 3
  1. 1.Institut für Optronik, Systemtechnik und BildauswertungFraunhoferKarlsruheGermany
  2. 2.MRDFraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSBKarlsruheGermany
  3. 3.inIT - Institut für industrielle InformationstechnikHochschule Ostwestfalen-LippeLemgoGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-58485-9
  • Copyright Information The Editor(s) (if applicable) and The Author(s) 2019
  • License CC BY
  • Publisher Name Springer Vieweg, Berlin, Heidelberg
  • eBook Packages Intelligent Technologies and Robotics
  • Print ISBN 978-3-662-58484-2
  • Online ISBN 978-3-662-58485-9
  • Series Print ISSN 2522-8579
  • Series Online ISSN 2522-8587
  • Buy this book on publisher's site