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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 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
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Title
Machine Learning for Cyber Physical Systems
Published by
Springer Berlin Heidelberg, January 2019
DOI 10.1007/978-3-662-58485-9
ISBNs
978-3-66-258484-2, 978-3-66-258485-9
Editors

Beyerer, Jürgen, Kühnert, Christian, Niggemann, Oliver

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Engineering 1 100%