Machine Learning for Cyber Physical Systems [recurso electrónico] : Selected papers from the International Conference ML4CPS 2015 / edited by Oliver Niggemann, Jürgen Beyerer.

Colaborador(es): Niggemann, Oliver [editor.] | Beyerer, Jürgen [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Technologien für die intelligente Automation, Technologies for Intelligent AutomationEditor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer Vieweg, 2016Edición: 1st ed. 2016Descripción: VI, 121 p. 12 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783662488386Tema(s): Engineering | Knowledge management | Data mining | Computational intelligence | Engineering | Computational Intelligence | Data Mining and Knowledge Discovery | Knowledge ManagementFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónicoTexto
Contenidos:
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 MonitoringSystems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for anIndustrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functionalcategorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in HybridSettings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of MaintenanceKnowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and CooperatingVehicles in Cyber-Physical Production Systems.
En: Springer eBooksResumen: 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.
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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 MonitoringSystems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for anIndustrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functionalcategorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in HybridSettings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of MaintenanceKnowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and CooperatingVehicles 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.

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