Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020 /

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...

Full description

Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Beyerer, Jürgen (Editor), Maier, Alexander (Editor), Niggemann, Oliver (Editor)
Format:	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:	Computer engineering. Internet of things. Embedded computer systems. Electrical engineering. Computer organization. Cyber-physical systems, IoT. Communications Engineering, Networks. Computer Systems Organization and Communication Networks. Electronic books.
Online Access:	Connect to the full text of this electronic book

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 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.