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Machine Learning for Cyber Physical Systems

Overview of attention for book
Cover of 'Machine Learning for Cyber Physical Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project
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    Chapter 2 Deduction of time-dependent machine tool characteristics by fuzzy-clustering
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    Chapter 3 Unsupervised Anomaly Detection in Production Lines
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    Chapter 4 A Random Forest Based Classifier for Error Prediction of Highly Individualized Products
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    Chapter 5 Web-based Machine Learning Platform for Condition- Monitoring
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    Chapter 6 Selection and Application of Machine Learning- Algorithms in Production Quality
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    Chapter 7 Which deep artifical neural network architecture to use for anomaly detection in Mobile Robots kinematic data?
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    Chapter 8 GPU GEMM-Kernel Autotuning for scalable machine learners
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    Chapter 9 Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria
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    Chapter 10 A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance
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    Chapter 11 Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality
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    Chapter 12 Enabling Self-Diagnosis of Automation Devices through Industrial Analytics
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    Chapter 13 Making Industrial Analytics work for Factory Automation Applications
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    Chapter 14 Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems
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    Chapter 15 LoRaWan for Smarter Management of Water Network: From metering to data analysis
Attention for Chapter 14: Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems
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Citations

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Chapter title
Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems
Chapter number 14
Book title
Machine Learning for Cyber Physical Systems
Published by
Springer Vieweg, Berlin, Heidelberg, January 2019
DOI 10.1007/978-3-662-58485-9_14
Book ISBNs
978-3-66-258484-2, 978-3-66-258485-9
Authors

Andreas Kuhnle, Gisela Lanza

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Student > Master 4 12%
Professor > Associate Professor 4 12%
Student > Bachelor 2 6%
Unspecified 2 6%
Other 4 12%
Unknown 10 30%
Readers by discipline Count As %
Engineering 8 24%
Computer Science 4 12%
Business, Management and Accounting 3 9%
Unspecified 2 6%
Decision Sciences 2 6%
Other 3 9%
Unknown 11 33%

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