大葉大學圖書館 |

Machine learning for cyber physical systems[electronic resource] :selected papers from the International Conference ML4CPS 2015 /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.31
書名/作者:	Machine learning for cyber physical systems : selected papers from the International Conference ML4CPS 2015 // edited by Oliver Niggemann, Jurgen Beyerer.
其他作者:	Niggemann, Oliver.
團體作者:	Clark Conference
出版者:	Berlin, Heidelberg : : Springer Berlin Heidelberg :, 2016.
面頁冊數:	vi, 121 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Machine learning
標題:	Cooperating objects (Computer systems)
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Data Mining and Knowledge Discovery.
標題:	Knowledge Management.
ISBN:	9783662488386
ISBN:	9783662488362
內容註:	Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.
摘要、提要註:	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 Lemgo, October 1-2, 2015. 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.
電子資源:	http://dx.doi.org/10.1007/978-3-662-48838-6

Machine learning for cyber physical systems[electronic resource] :selected papers from the International Conference ML4CPS 2015 /
Machine learning for cyber physical systemsselected papers from the International Conference ML4CPS 2015 /[electronic resource] :edited by Oliver Niggemann, Jurgen Beyerer. - Berlin, Heidelberg :Springer Berlin Heidelberg :2016. - vi, 121 p. :ill., digital ;24 cm. - Technologien fur die intelligente automation, technologies for intelligent automation. - Technologien fur die intelligente automation, technologies for intelligent automation..

Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.

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 Lemgo, October 1-2, 2015. 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.

ISBN: 9783662488386

Standard No.: 10.1007/978-3-662-48838-6doiSubjects--Topical Terms:

135653
Machine learning

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for cyber physical systems[electronic resource] :selected papers from the International Conference ML4CPS 2015 /
LDR:03137nam a2200325 a 4500 001 456704
003 DE-He213
005 20160824103426.0
006 m d
007 cr nn 008maaau
008 161227s2016 gw s 0 eng d
020 $a 9783662488386 $q (electronic bk.)
020 $a 9783662488362 $q (paper)
024 7 $a 10.1007/978-3-662-48838-6 $2 doi
035 $a 978-3-662-48838-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M149 2016
111 2 $a Clark Conference $d (2005 : $c Sterling and Francine Clark Art Institute) $3 347558
245 1 0 $a Machine learning for cyber physical systems $h [electronic resource] : $b selected papers from the International Conference ML4CPS 2015 / $c edited by Oliver Niggemann, Jurgen Beyerer.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer Vieweg, $c 2016.
300 $a vi, 121 p. : $b ill., digital ; $c 24 cm.
490 1 $a Technologien fur die intelligente automation, technologies for intelligent automation
505 0 $a Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.
520 $a 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 Lemgo, October 1-2, 2015. 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.
650 0 $a Machine learning $3 135653
650 0 $a Cooperating objects (Computer systems) $3 611532
650 1 4 $a Engineering. $3 372756
650 2 4 $a Computational Intelligence. $3 463962
650 2 4 $a Data Mining and Knowledge Discovery. $3 464541
650 2 4 $a Knowledge Management. $3 633185
700 1 $a Niggemann, Oliver. $3 656298
700 1 $a Beyerer, Jurgen. $3 656299
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Technologien fur die intelligente automation, technologies for intelligent automation. $3 656300
856 4 0 $u http://dx.doi.org/10.1007/978-3-662-48838-6
950 $a Engineering (Springer-11647)