Machine learning for cyber physical systems : selected papers from the international conference ML4CPS 2020 / Jürgen Beyerer, Alexander Maier, Oliver Niggemann, editors.
ML4CPS (Conference) (5th : 2020 : Berlin, Germany); Beyerer, Jürgen, editor.; Maier, Alexander, editor.; Niggemann, Oliver, editor.
2021
Q325.5
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Title Machine learning for cyber physical systems : selected papers from the international conference ML4CPS 2020 / Jürgen Beyerer, Alexander Maier, Oliver Niggemann, editors.
Meeting Name ML4CPS (Conference) (5th : 2020 : Berlin, Germany)
ISBN 9783662627464 (electronic bk.)
3662627469 (electronic bk.)
9783662627457
3662627450
DOI https://doi.org/10.1007/978-3-662-62746-4
Published Berlin : Springer Vieweg, [2021]
Language English
Description 1 online resource (vii, 130 pages) : illustrations (some color)
Item Number 10.1007/978-3-662-62746-4 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary 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.
Note International conference proceedings.
Bibliography, etc. Note Includes bibliographical references.
Access Note Open access
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed February 25, 2021).
Added Author Beyerer, Jürgen, editor.
Maier, Alexander, editor.
Niggemann, Oliver, editor.
Series Technologien für die intelligente Automation ; Band 13. 2522-8579
Available in Other Form 3662627450
Linked Resources Online Access
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Preface
Energy Profile Prediction of Milling Processes Using Machine Learning Techniques
Improvement of the prediction quality of electrical load profiles with artificial neural networks
Detection and localization of an underwater docking station
Deployment architecture for the local delivery of ML-Models to the industrial shop floor
Deep Learning in Resource and Data Constrained Edge Computing Systems
Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis
Proposal for requirements on industrial AI solutions
Information modeling and knowledge extraction for machine learning applications in industrial production systems
Explanation Framework for Intrusion Detection
Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning
Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks
First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems
Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data.

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