Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2017: Technologien für die intelligente Automation, cartea 11
Editat de Jürgen Beyerer, Alexander Maier, Oliver Niggemannen Limba Engleză Paperback – 10 apr 2019
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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Specificații
ISBN-13: 9783662590836
ISBN-10: 3662590832
Pagini: 90
Ilustrații: VII, 87 p. 1 illus.
Dimensiuni: 168 x 240 mm
Greutate: 0.17 kg
Ediția:1st ed. 2020
Editura: Springer Berlin, Heidelberg
Colecția Springer Vieweg
Seria Technologien für die intelligente Automation
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3662590832
Pagini: 90
Ilustrații: VII, 87 p. 1 illus.
Dimensiuni: 168 x 240 mm
Greutate: 0.17 kg
Ediția:1st ed. 2020
Editura: Springer Berlin, Heidelberg
Colecția Springer Vieweg
Seria Technologien für die intelligente Automation
Locul publicării:Berlin, Heidelberg, Germany
Cuprins
Prescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks.- Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors.- Differential Evolution in Production Process Optimization of Cyber Physical Systems.- Machine Learning for Process-X: A Taxonomy.- Intelligent edge processing.- Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems.- Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis.- Verstehen von Maschinenverhalten mit Hilfe von Machine Learning.- Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Recongurable Architectures.- The Acoustic Test System for Transmissions in the VW Group.
Notă biografică
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. Dr. Oliver Niggemann 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.
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. Dr. Oliver Niggemann 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.
Textul de pe ultima copertă
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 25th-26th, 2017.
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 ofgroup 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. Dr. Oliver Niggemann 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.
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 ofgroup 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. Dr. Oliver Niggemann 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.
Caracteristici
Includes the full proceedings of the 2017 ML4CPS – Machine Learning for Cyber Physical Systems Conference Presents recent and new advances in automated machine learning methods Provides an accessible and succinct overview on machine learning for cyber physical systems