Overview
- 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)
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Front Matter
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 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
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