Machine Learning for Cyber Physical Systems
Latest Publications

TOTAL DOCUMENTS

14

(FIVE YEARS 0)

H-INDEX

3

(FIVE YEARS 0)

Published By Springer Berlin Heidelberg

9783662488362, 9783662488386

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Efficient Image Processing System for an Industrial Machine Learning Task

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_8 ◽

2016 ◽

pp. 59-66

Author(s):

Kristijan Vukovic ◽

Kristina Simonis ◽

Helene Dörksen ◽

Volker Lohweg

Keyword(s):

Machine Learning ◽

Image Processing ◽

Processing System ◽

Learning Task ◽

Image Processing System ◽

Industrial Machine

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Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_6 ◽

2016 ◽

pp. 45-50 ◽

Author(s):

S. Windmann ◽

J. Eickmeyer ◽

F. Jungbluth ◽

J. Badinger ◽

O. Niggemann

Keyword(s):

Condition Monitoring ◽

Industrial Application ◽

Monitoring Systems ◽

Model Based ◽

Condition Monitoring Systems

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Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_3 ◽

2016 ◽

pp. 15-22 ◽

Author(s):

Christoph Ide ◽

Michael Nick ◽

Dennis Kaulbars ◽

Christian Wietfeld

Keyword(s):

Machine Learning ◽

Cyber Physical Systems ◽

Learning Approach ◽

Physical Systems ◽

Machine Learning Approach

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Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_13 ◽

2016 ◽

pp. 103-110 ◽

Author(s):

Fazel Ansari ◽

Madjid Fathi

Keyword(s):

Production Systems ◽

Meta Analysis ◽

Knowledge Assets ◽

Cost Controlling ◽

Cyber Physical Production Systems

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Implementation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_11 ◽

2016 ◽

pp. 87-93 ◽

Author(s):

André Mueß ◽

Jens Weber ◽

Raphael-Elias Reisch ◽

Benjamin Jurke

Keyword(s):

Efficient Approximation

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Kognitive Architektur zum Konzeptlernen in technischen Systemen

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_10 ◽

2016 ◽

pp. 75-85 ◽

Author(s):

Alexander Diedrich ◽

Andreas Bunte ◽

Alexander Maier ◽

Oliver Niggemann

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Towards a novel learning assistant for networked automation systems

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_7 ◽

2016 ◽

pp. 51-57 ◽

Author(s):

Yongheng Wang ◽

Michael Weyrich

Keyword(s):

Automation Systems

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Machine-specific Approach for Automatic Classification of Cutting Process Efficiency

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_12 ◽

2016 ◽

pp. 95-102 ◽

Author(s):

Christian Walther ◽

Frank Beneke ◽

Luise Merbach ◽

Hubertus Siebald ◽

Oliver Hensel ◽

...

Keyword(s):

Automatic Classification ◽

Cutting Process ◽

Process Efficiency ◽

Specific Approach

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Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_14 ◽

2016 ◽

pp. 111-121 ◽

Author(s):

Adrian Böckenkamp ◽

Frank Weichert ◽

Jonas Stenzel ◽

Dennis Lünsch

Keyword(s):

Production Systems ◽

Cyber Physical Production Systems

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Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control

Machine Learning for Cyber Physical Systems ◽

10.1007/978-3-662-48838-6_1 ◽

2016 ◽

pp. 1-8 ◽

Author(s):

Javier Diaz ◽

Concha Bielza ◽

Jose L. Ocaña ◽

Pedro Larrañaga

Keyword(s):

Heat Treatment ◽

Process Control ◽

Physical System ◽

Treatment Process ◽

Surface Heat ◽

Laser Surface ◽

Cyber Physical System ◽

Heat Treatment Process ◽

Bayes Model ◽

Naïve Bayes Model

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