東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning for cyber physical ...

ML4CPS (Conference) ((2017 :)

FindBook

Google Book

Amazon

博客來

Machine learning for cyber physical systems = selected papers from the International Conference ML4CPS 2017 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for cyber physical systems/ edited by Jurgen Beyerer, Alexander Maier, Oliver Niggemann.
其他題名:	selected papers from the International Conference ML4CPS 2017 /
其他題名:	ML4CPS 2017
其他作者:	Beyerer, Jurgen.
團體作者:	ML4CPS (Conference)
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2020.,
面頁冊數:	vii, 87 p. :ill., digital ;24 cm.
內容註:	Prescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks -- Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors -- Differential Evolution in Production Process Optimization of Cyber Physical Systems -- Machine Learning for Process-X: A Taxonomy -- Intelligent edge processing -- Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems -- Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis -- Verstehen von Maschinenverhalten mit Hilfe von Machine Learning -- Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Recongurable Architectures -- The Acoustic Test System for Transmissions in the VW Group.
Contained By:	Springer eBooks
標題:	Machine learning - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-662-59084-3
ISBN:	9783662590843

Machine learning for cyber physical systems = selected papers from the International Conference ML4CPS 2017 /
Machine learning for cyber physical systemsselected papers from the International Conference ML4CPS 2017 /[electronic resource] :ML4CPS 2017edited by Jurgen Beyerer, Alexander Maier, Oliver Niggemann. - Berlin, Heidelberg :Springer Berlin Heidelberg :2020. - vii, 87 p. :ill., digital ;24 cm. - Technologien fur die intelligente automation, technologies for intelligent automation,band 112522-8579 ;. - Technologien fur die intelligente automation, technologies for intelligent automation ;band 11..

Prescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks -- Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors -- Differential Evolution in Production Process Optimization of Cyber Physical Systems -- Machine Learning for Process-X: A Taxonomy -- Intelligent edge processing -- Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems -- Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis -- Verstehen von Maschinenverhalten mit Hilfe von Machine Learning -- Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Recongurable Architectures -- The Acoustic Test System for Transmissions in the VW Group.

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 25th-26th, 2017. 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. Jurgen 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. Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.

ISBN: 9783662590843

Standard No.: 10.1007/978-3-662-59084-3doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for cyber physical systems = selected papers from the International Conference ML4CPS 2017 /
LDR:03577nmm a2200349 a 4500 001 2213402
003 DE-He213
005 20200207103546.0
006 m d
007 cr nn 008maaau
008 201117s2020 gw s 0 eng d
020 $a 9783662590843 $q (electronic bk.)
020 $a 9783662590836 $q (paper)
024 7 $a 10.1007/978-3-662-59084-3 $2 doi
035 $a 978-3-662-59084-3
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
072 7 $a UYQ $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M685 2017
111 2 $a ML4CPS (Conference) $d (2017 : $c Lemgo, Germany) $3 3442837
245 1 0 $a Machine learning for cyber physical systems $h [electronic resource] : $b selected papers from the International Conference ML4CPS 2017 / $c edited by Jurgen Beyerer, Alexander Maier, Oliver Niggemann.
246 3 $a ML4CPS 2017
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer Vieweg, $c 2020.
300 $a vii, 87 p. : $b ill., digital ; $c 24 cm.
490 1 $a Technologien fur die intelligente automation, technologies for intelligent automation, $x 2522-8579 ; $v band 11
505 0 $a Prescriptive Maintenance of CPPS by Integrating Multi-modal Data with Dynamic Bayesian Networks -- Evaluation of Deep Autoencoders for Prediction of Adjustment Points in the Mass Production of Sensors -- Differential Evolution in Production Process Optimization of Cyber Physical Systems -- Machine Learning for Process-X: A Taxonomy -- Intelligent edge processing -- Learned Abstraction: Knowledge Based Concept Learning for Cyber Physical Systems -- Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis -- Verstehen von Maschinenverhalten mit Hilfe von Machine Learning -- Adaptable Realization of Industrial Analytics Functions on Edge-Devices using Recongurable Architectures -- The Acoustic Test System for Transmissions in the VW Group.
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 25th-26th, 2017. 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. Jurgen 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. Dr. Alexander Maier is head of group Machine Learning at Fraunhofer IOSB-INA. His focus is on the development of algorithms for big data applications in Cyber-Physical Systems (diagnostics, optimization, predictive maintenance) and the transfer of research results to industry. Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
650 0 $a Machine learning $x Congresses. $3 576368
650 0 $a Cooperating objects (Computer systems) $v Congresses. $3 3218484
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Computer Systems Organization and Communication Networks. $3 891212
650 2 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Beyerer, Jurgen. $3 2182965
700 1 $a Maier, Alexander. $3 3442838
700 1 $a Niggemann, Oliver. $3 2182964
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Technologien fur die intelligente automation, technologies for intelligent automation ; $v band 11. $3 3442839
856 4 0 $u https://doi.org/10.1007/978-3-662-59084-3
950 $a Intelligent Technologies and Robotics (Springer-42732)