Machine Learning for Cyber Physical Systems Selected papers from the International Conference ML4CPS 2015 /

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 ch...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Niggemann, Oliver (Editor ), Beyerer, Jürgen (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2016.
Edición:1st ed. 2016.
Colección:Technologien für die intelligente Automation, Technologies for Intelligent Automation,
Temas:
Computational intelligence.
Data mining.
Knowledge management.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Knowledge Management.
Acceso en línea:Texto Completo

MARC

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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. 
250 |a 1st ed. 2016. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer Vieweg,  |c 2016. 
300 |a VI, 121 p. 12 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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347 |a text file  |b PDF  |2 rda 
490 1 |a Technologien für die intelligente Automation, Technologies for Intelligent Automation,  |x 2522-8587 
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 Computational intelligence. 
650 0 |a Data mining. 
650 0 |a Knowledge management. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Knowledge Management. 
700 1 |a Niggemann, Oliver.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Beyerer, Jürgen.  |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783662488362 
776 0 8 |i Printed edition:  |z 9783662488379 
830 0 |a Technologien für die intelligente Automation, Technologies for Intelligent Automation,  |x 2522-8587 
856 4 0 |u https://doi.uam.elogim.com/10.1007/978-3-662-48838-6  |z Texto Completo 
912 |a ZDB-2-ENG 
912 |a ZDB-2-SXE 
950 |a Engineering (SpringerNature-11647) 
950 |a Engineering (R0) (SpringerNature-43712) 

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