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        Machine Learning for Cyber Physical Systems

        Selected papers from the International Conference ML4CPS 2020

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        Contributor(s)
        Beyerer, Jürgen (editor)
        Maier, Alexander (editor)
        Niggemann, Oliver (editor)
        Language
        English
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        Abstract
        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.
        URI
        https://library.oapen.org/handle/20.500.12657/46102
        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
        DOI
        10.1007/978-3-662-62746-4
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2021
        Imprint
        Springer Vieweg
        Series
        Technologien für die intelligente Automation, 13
        Classification
        Electrical engineering
        Communications engineering / telecommunications
        Computer networking and communications
        Pages
        130
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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