1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0

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 ch...

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Detalles Bibliográficos
Autores principales: Niggemann, Oliver, Beyerer, Jürgen
Lenguaje:eng
Publicado: Springer 2016
Materias:
Engineering
Acceso en línea:https://dx.doi.org/10.1007/978-3-662-48838-6
http://cds.cern.ch/record/2137890
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author Niggemann, Oliver
Beyerer, Jürgen
author_facet Niggemann, Oliver
Beyerer, Jürgen
author_sort Niggemann, Oliver
collection CERN
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 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.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
publisher Springer
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spelling cern-21378902021-04-22T06:43:00Zdoi:10.1007/978-3-662-48838-6http://cds.cern.ch/record/2137890engNiggemann, OliverBeyerer, Jürgen1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0EngineeringThe 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.Springeroai:cds.cern.ch:21378902016
spellingShingle Engineering
Niggemann, Oliver
Beyerer, Jürgen
1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0
title 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0
title_full 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0
title_fullStr 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0
title_full_unstemmed 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0
title_short 1st International Conference on Machine Learning for Cyber Physical Systems and Industry 4.0
title_sort 1st international conference on machine learning for cyber physical systems and industry 4.0
topic Engineering
url https://dx.doi.org/10.1007/978-3-662-48838-6
http://cds.cern.ch/record/2137890
work_keys_str_mv AT niggemannoliver 1stinternationalconferenceonmachinelearningforcyberphysicalsystemsandindustry40
AT beyererjurgen 1stinternationalconferenceonmachinelearningforcyberphysicalsystemsandindustry40

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