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Machine learning for cyber physical ...

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Machine learning for cyber physical systemsselected papers from the International Conference ML4CPS 2020 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for cyber physical systemsedited by Jurgen Beyerer, Alexander Maier, Oliver Niggemann.
Reminder of title:	selected papers from the International Conference ML4CPS 2020 /
remainder title:	ML4CPS 2020
other author:	Beyerer, Jurgen.
corporate name:
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg :2021.
Description:	vii, 130 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learningCongresses.
Online resource:	https://doi.org/10.1007/978-3-662-62746-4
ISBN:	9783662627464$q(electronic bk.)

Machine learning for cyber physical systemsselected papers from the International Conference ML4CPS 2020 /
Machine learning for cyber physical systemsselected papers from the International Conference ML4CPS 2020 /[electronic resource] :ML4CPS 2020edited by Jurgen Beyerer, Alexander Maier, Oliver Niggemann. - Berlin, Heidelberg :Springer Berlin Heidelberg :2021. - vii, 130 p. :ill., digital ;24 cm. - Technologien fur die intelligente automation = Technologies for intelligent automation,Band 132522-8579 ;. - Technologien fur die intelligente automation ;Band 13..

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.

Open access.

ISBN: 9783662627464$q(electronic bk.)

Standard No.: 10.1007/978-3-662-62746-4doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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505 0 $a 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.
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