Non-Fiction Books:

Machine Learning for Cyber Physical Systems

Selected papers from the International Conference ML4CPS 2017
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Description

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.

Author Biography:

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.
Release date NZ
April 10th, 2019
Audience
  • Professional & Vocational
Contributors
  • Edited by Alexander Maier
  • Edited by Jurgen Beyerer
  • Edited by Oliver Niggemann
Edition
1st ed. 2020
Illustrations
1 Illustrations, black and white; VII, 87 p. 1 illus.
Pages
87
ISBN-13
9783662590836
Product ID
29779257

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