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Machine Learning for Cyber-Physical Systems: Selected papers from the International Conference ML4CPS 2023: 18 (Technologien für die intelligente Automation) - Tapa blanda
Sinopsis
This open access proceedings 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 Hamburg (Germany), March 29th to 31st, 2023.
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.
This is an open access book.
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Acerca del autor
Prof. Dr. Oliver Niggemann held the professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo (Germany) from 2008 to 2019 and was also deputy head of the Fraunhofer IOSB-INA until 2019. In 2019, he took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut Schmidt University in Hamburg. His research at the Institute for Automation Technology is in the field of artificial intelligence and machine learning for cyber-physical systems.
Prof. Dr.-Ing. Jürgen Beyerer is a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology KIT and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Research interests include automated visual inspection, signal and image processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation.
Dr. Maria Krantz is a Postdoc at the Helmut Schmidt University in Hamburg. Her main research interests are causality in Cyber-Physical Systems and applications of diagnosis algorithms in production systems.
Dr. Christian Kühnert is senior scientist at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data analytics for cyber-physical systems.
De la contraportada
This open access proceedings 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 Hamburg (Germany), March 29th to 31st, 2023.
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.
The Editors
Prof. Dr. Oliver Niggemann held the professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo (Germany) from 2008 to 2019 and was also deputy head of the Fraunhofer IOSB-INA until 2019. In 2019, he took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut Schmidt University in Hamburg. His research at the Institute for Automation Technology is in the field of artificial intelligence and machine learning for cyber-physical systems.
Prof. Dr.-Ing. Jürgen Beyerer is a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology KIT and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Research interests include automated visual inspection, signal and image processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation.
Dr. Maria Krantz is a Postdoc at the Helmut Schmidt University in Hamburg. Her main research interests are causality in Cyber-Physical Systems and applications of diagnosis algorithms in production systems.
Dr. Christian Kühnert is senior scientist at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data analytics for cyber-physical systems.
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Machine Learning for Cyber-physical Systems : Selected Papers from the International Conference Ml4cps 2023
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Paperback. Condición: new. Paperback. This open access proceedings 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 Hamburg (Germany), March 29th to 31st, 2023. 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.This is an open access book. This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783031470615
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. Itcontains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023.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.This is an open access book. 140 pp. Englisch. Nº de ref. del artículo: 9783031470615
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Taschenbuch. Condición: Neu. Neuware -This open access proceedings 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 Hamburg (Germany), March 29th to 31st, 2023.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.This is an open access book.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch. Nº de ref. del artículo: 9783031470615
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Machine Learning for Cyber-Physical Systems : Selected papers from the International Conference ML4CPS 2023
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Springer Nature Switzerland Jun 2024, 2024
ISBN 10: 3031470613
ISBN 13: 9783031470615
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. Itcontains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023.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.This is an open access book. Nº de ref. del artículo: 9783031470615
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