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Machine Learning for Cyber Physical Systems

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Cover of 'Machine Learning for Cyber Physical Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Energy Profile Prediction of Milling Processes Using Machine Learning Techniques
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    Chapter 2 Improvement of the prediction quality of electrical load profiles with artificial neural networks
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    Chapter 3 Detection and localization of an underwater docking station in acoustic images using machine learning and generalized fuzzy hough transform
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    Chapter 4 Deployment architecture for the local delivery of ML-Models to the industrial shop floor
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    Chapter 5 Deep Learning in Resource and Data Constrained Edge Computing Systems
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    Chapter 6 Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis
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    Chapter 7 Proposal for requirements on industrial AI solutions
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    Chapter 8 Information modeling and knowledge extraction for machine learning applications in industrial production systems
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    Chapter 9 Explanation Framework for Intrusion Detection
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    Chapter 10 Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning
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    Chapter 11 Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks
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    Chapter 12 First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems
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    Chapter 13 Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data
Attention for Chapter 6: Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis
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Chapter title
Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis
Chapter number 6
Book title
Machine Learning for Cyber Physical Systems
Published by
Springer Vieweg, Berlin, Heidelberg, December 2020
DOI 10.1007/978-3-662-62746-4_6
Book ISBNs
978-3-66-262745-7, 978-3-66-262746-4
Authors

Paolo Graniero, Marco Gärtler, Graniero, Paolo, Gärtler, Marco

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