Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020 / edited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann.

Format:

Book

Author/Creator:

Beyerer, Jürgen., Editor.

Contributor:

Beyerer, Jürgen, Editor.

Maier, Alexander., Editor.

Niggemann, Oliver., Editor.

Series:

Technologien für die intelligente Automation, Technologies for Intelligent Automation, 2522-8587 ; 13

Language:

English

Subjects (All):

Cooperating objects (Computer systems).

Telecommunication.

Computer engineering.

Computer networks.

Cyber-Physical Systems.

Communications Engineering, Networks.

Computer Engineering and Networks.

Local Subjects:

Cyber-Physical Systems.

Communications Engineering, Networks.

Computer Engineering and Networks.

Physical Description:

1 online resource (VII, 130 p. 42 illus., 25 illus. in color.)

Edition:

1st ed. 2021.

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2021.

Language Note:

English

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.

Contents:

Preface

Energy Profile Prediction of Milling Processes Using Machine Learning Techniques

Improvement of the prediction quality of electrical load profiles with artficial 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 ExampleAttacks

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.

ISBN:

3-662-62746-9

OCLC:

1231609193