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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 Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control
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    Chapter 2 Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks
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    Chapter 3 Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach
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    Chapter 4 Towards Optimized Machine Operations by Cloud Integrated Condition Estimation
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    Chapter 5 Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission
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    Chapter 6 Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases
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    Chapter 7 Towards a novel learning assistant for networked automation systems
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    Chapter 8 Efficient Image Processing System for an Industrial Machine Learning Task
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    Chapter 9 Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation
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    Chapter 10 Kognitive Architektur zum Konzeptlernen in technischen Systemen
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    Chapter 11 Implementation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation
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    Chapter 12 Machine-specific Approach for Automatic Classification of Cutting Process Efficiency
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    Chapter 13 Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems
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    Chapter 14 Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems
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Title
Machine Learning for Cyber Physical Systems
Published by
Springer Berlin Heidelberg, February 2016
DOI 10.1007/978-3-662-48838-6
ISBNs
978-3-66-248836-2, 978-3-66-248838-6
Editors

Niggemann, Oliver, Beyerer, Jürgen

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 30%
Student > Master 13 26%
Student > Bachelor 5 10%
Professor 2 4%
Student > Doctoral Student 1 2%
Other 4 8%
Unknown 10 20%
Readers by discipline Count As %
Engineering 17 34%
Computer Science 16 32%
Business, Management and Accounting 2 4%
Linguistics 1 2%
Materials Science 1 2%
Other 1 2%
Unknown 12 24%

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