Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2017: 11 - Brossura

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ISBN 10: 3662590832 ISBN 13: 9783662590836

Casa editrice: Springer Vieweg, 2019

Riassunto

<p>The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It&nbsp; contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017.&nbsp;</p><p>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.</p><p><br></p>

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Informazioni sull?autore

<p><b>Prof. Dr.-Ing. Jürgen Beyerer</b> is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.</p><p><b>Dr. Alexander Maier </b>is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry.&nbsp;<b> &nbsp;</b> &nbsp;&nbsp;<br></p><p><b>Prof. Dr. Oliver Niggemann </b>is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.<br></p><p><br></p>

Dalla quarta di copertina

<p>The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It&nbsp; contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 25th-26th, 2017.&nbsp;</p><p>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.</p><p><b>The Editors</b></p><p><b>Prof. Dr.-Ing. Jürgen Beyerer</b>&nbsp;is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.</p><p><b>Dr. Alexander Maier&nbsp;</b>is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry.&nbsp;<b>&nbsp;&nbsp;</b>&nbsp;&nbsp;&nbsp;<br></p><p><b>Prof. Dr. Oliver Niggemann </b>is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.<br></p>

Dettagli dell'edizione

• Editore: Springer Vieweg
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3662590832
• ISBN 13: 9783662590836
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 96
• Redattore: Beyerer Jürgen, Maier Alexander, Niggemann Oliver
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania