Machine Learning for Cyber Physical Systems (Record no. 80464)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04532nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-662-53806-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801222148.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 161130s2017 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783662538067 |
| -- | 978-3-662-53806-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning for Cyber Physical Systems |
| Sub Title | Selected papers from the International Conference ML4CPS 2016 / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2017. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | VII, 72 p. 24 illus., 19 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Technologien für die intelligente Automation, Technologies for Intelligent Automation, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | A Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths -- Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection -- Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment -- A Modular Architecture for Smart Data Analysis using AutomationML, OPC-UA and Data-driven Algorithms -- Cloud-based event detection platform for water distribution networks using machine-learning algorithms -- A Generic Data Fusion and Analysis Platform for Cyber-Physical Systems -- Agent Swarm Optimization: Exploding the search space -- Anomaly Detection in Industrial Networks using Machine Learning. . |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Prof. Dr. Oliver Niggemann is Professor for Embedded Software Engineering. His research interests are in the field of Distributed Real-time Software and in the fields of analysis and diagnosis of distributed systems. He is a board member of the inIT and a senior researcher at the Fraunhofer Application Center Industrial Automation INA located in Lemgo. Dr. Christian Kühnert is a senior researcher at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data-driven condition monitoring. . |
| 700 1# - AUTHOR 2 | |
| Author 2 | Beyerer, Jürgen. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Niggemann, Oliver. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Kühnert, Christian. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-662-53806-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Berlin, Heidelberg : |
| -- | Springer Berlin Heidelberg : |
| -- | Imprint: Springer Vieweg, |
| -- | 2017. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| 337 ## - | |
| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Knowledge management. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Knowledge Management. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2522-8587 |
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| -- | ZDB-2-ENG |
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| -- | ZDB-2-SXE |
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