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Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 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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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Beyerer, Jürgen (Editor), Maier, Alexander (Editor), Niggemann, Oliver (Editor)
Format: Electronic eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2021.
Edition:1st ed. 2021.
Series:Technologien für die intelligente Automation, Technologies for Intelligent Automation, 13
Subjects:
Cooperating objects (Computer systems).
Telecommunication.
Computer engineering.
Computer networks .
Cyber-Physical Systems.
Communications Engineering, Networks.
Computer Engineering and Networks.
Online Access:Link to Metadata
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Table of 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.

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