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Machine Learning for Cyber Physical Systems

Inside this Book

If you make use of this material, you may credit the authors as follows:

Beyerer Jürgen et al. (Editors), "Machine Learning for Cyber Physical Systems", Springer Nature, 2021, DOI: 10.1007/978-3-662-62746-4, License: http://creativecommons.org/licenses/by/4.0/

This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. 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.

Keywords

Cyber-physical Systems, Iot, Communications Engineering, Networks, Computer Systems Organization And Communication Networks, Cyber-physical Systems, Computer Engineering And Networks, Machine Learning, Artificial Intelligence, Cognitive Robotics, Internet Of Things, Computational Intelligence, Computer-based Algorithms, Smart Grid, Open Access, Industry 4.0, Electrical Engineering, Cybernetics & Systems Theory, Communications Engineering / Telecommunications, Computer Networking & Communications

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Except where otherwise noted, this item has been published under the following license:

http://creativecommons.org/licenses/by/4.0/

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