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Machine learning for cyber physical systems [electronic resource] : selected papers from the international conference ML4CPS 2015

Machine learning for cyber physical systems [electronic resource] : selected papers from the international conference ML4CPS 2015

Material type
E-Book(소장)
Personal Author
Niggemann, Oliver. Beyerer, Jürgen.
Title Statement
Machine learning for cyber physical systems [electronic resource] : selected papers from the international conference ML4CPS 2015 / edited by Oliver Niggemann, Jürgen Beyerer.
Publication, Distribution, etc
Berlin ;   Heidelberg :   Springer Berlin Heidelberg :   Imprint: Springer Vieweg,   2016.  
Physical Medium
1 online resource (vi, 121 p.) : col. ill.
Series Statement
Technologien für die intelligente Automation ; Technologies for intelligent automation
ISBN
9783662488386
요약
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.
General Note
Title from e-Book title page.  
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
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URL
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007 cr
008 200601s2016 gw a ob 000 0 eng d
020 ▼a 9783662488386
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.31 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31
111 2 ▼a Machine Learning for Cyber Physical Systems (Conference) ▼d (2015 : ▼c Lemgo, Germany).
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, Jürgen Beyerer.
260 ▼a Berlin ; ▼a Heidelberg : ▼b Springer Berlin Heidelberg : ▼b Imprint: Springer Vieweg, ▼c 2016.
300 ▼a 1 online resource (vi, 121 p.) : ▼b col. ill.
490 1 ▼a Technologien für die intelligente Automation ; ▼a Technologies for intelligent automation
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
700 1 ▼a Niggemann, Oliver.
700 1 ▼a Beyerer, Jürgen.
830 0 ▼a Technologien für die intelligente Automation.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-662-48838-6
911 ▼a ML4CPS (Conference). ▼d (2015 : ▼c Lemgo, Germany).
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 006.31 Accession No. E14023564 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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