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oapen-20.500.12657-46102
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dspace
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oapen-20.500.12657-461022021-01-14T03:06:05Z Machine Learning for Cyber Physical Systems Beyerer, Jürgen Maier, Alexander Niggemann, Oliver 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 bic Book Industry Communication::T Technology, engineering, agriculture::TH Energy technology & engineering::THR Electrical engineering bic Book Industry Communication::T Technology, engineering, agriculture::TJ Electronics & communications engineering::TJK Communications engineering / telecommunications bic Book Industry Communication::U Computing & information technology::UT Computer networking & communications 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. 2021-01-13T08:56:31Z 2021-01-13T08:56:31Z 2021 book ONIX_20210113_9783662627464_8 https://library.oapen.org/handle/20.500.12657/46102 eng Technologien für die intelligente Automation application/pdf n/a 2021_Book_MachineLearningForCyberPhysica.pdf https://www.springer.com/9783662627464 Springer Nature Springer Vieweg 10.1007/978-3-662-62746-4 10.1007/978-3-662-62746-4 6c6992af-b843-4f46-859c-f6e9998e40d5 Springer Vieweg 13 130 open access
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OAPEN
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DSpace
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| language |
English
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| description |
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.
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| title |
2021_Book_MachineLearningForCyberPhysica.pdf
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| spellingShingle |
2021_Book_MachineLearningForCyberPhysica.pdf
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| title_short |
2021_Book_MachineLearningForCyberPhysica.pdf
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| title_full |
2021_Book_MachineLearningForCyberPhysica.pdf
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| title_fullStr |
2021_Book_MachineLearningForCyberPhysica.pdf
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| title_full_unstemmed |
2021_Book_MachineLearningForCyberPhysica.pdf
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| title_sort |
2021_book_machinelearningforcyberphysica.pdf
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| publisher |
Springer Nature
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| publishDate |
2021
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| url |
https://www.springer.com/9783662627464
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| _version_ |
1771297527092477952
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