Machine learning for cyber physical systems : selected papers from the international conference ML4CPS 2020 ; Berlin, Germany, March 12-13, 2020

Machine learning for cyber physical systems selected papers from the international conference ML4CPS 2020 ; Berlin, Germany, March 12-13, 2020

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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 P...

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Detalles Bibliográficos
Otros Autores: Beyerer, Jürgen (Editor ), Beyerer, Jürgen. editor (editor), Maier, Alexander. editor, Niggemann, Oliver. editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Nature 2021
2021.
Edición:1st edition 2021.
Colección:Technologies for Intelligent Automation, 13
Materias:
Machine learning > Congresses.
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
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009430291706719
Descripción
Sumario: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. The Editors Prof. Dr.-Ing. Jürgen Beyerer 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. Dr. Alexander Maier 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. Prof. Oliver Niggemann got his doctorate in 2001 at the University of Paderborn with the topic "Visual Data Mining of Graph-Based Data". He then worked for almost 8 years in leading positions in the industry. From 2008-2019 he held a professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo/Germany. Until 2019 Prof. Niggemann was also deputy head of the Fraunhofer IOSB-INA, which works in industrial automation. On April 1, 2019 Prof. Niggemann took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut-Schmidt-University in Hamburg / Germany. There he does research at the Institute for Automation Technology IfA in the field of artificial intelligence and machine learning for cyber-physical systems.
Descripción Física:1 online resource (VII, 130 p. 42 illus., 25 illus. in color.)
ISBN:9783662627464

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