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Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2017

  • Conference proceedings
  • © 2020

Overview

  • Includes the full proceedings of the 2017 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

Part of the book series: Technologien für die intelligente Automation (TIA, volume 11)

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Table of contents (10 papers)

Keywords

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 25th-26th, 2017. 

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

  • Institut für Optronik, Systemtechnik und Bildauswertung, Fraunhofer, Karlsruhe, Germany

    Jürgen Beyerer

  • Industrial Automation, Fraunhofer-Anwendungszentrum, Lemgo, Germany

    Alexander Maier

  • inIT - Institut für industrielle Informationstechnik, Hochschule Ostwestfalen-Lippe, Lemgo, Germany

    Oliver Niggemann

About 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. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry.      

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.


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