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Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020 / / edited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann
| Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020 / / edited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann |
| Autore | Beyerer Jürgen |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (VII, 130 p. 42 illus., 25 illus. in color.) |
| Disciplina | 621.38 |
| Collana | Technologien für die intelligente Automation, Technologies for Intelligent Automation |
| Soggetto topico |
Cooperating objects (Computer systems)
Telecommunication Computer engineering Computer networks Cyber-Physical Systems Communications Engineering, Networks Computer Engineering and Networks |
| ISBN | 3-662-62746-9 |
| Classificazione | COM043000TEC007000TEC041000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Energy Profile Prediction of Milling Processes Using Machine Learning Techniques -- Improvement of the prediction quality of electrical load profiles with artficial neural networks -- Detection and localization of an underwater docking station -- Deployment architecture for the local delivery of ML-Models to the industrial shop floor -- Deep Learning in Resource and Data Constrained Edge Computing Systems -- Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis -- Proposal for requirements on industrial AI solutions -- Information modeling and knowledge extraction for machine learning applications in industrial production systems -- Explanation Framework for Intrusion Detection -- Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning -- Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial ExampleAttacks -- First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems -- Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data. |
| Record Nr. | UNINA-9910433248603321 |
Beyerer Jürgen
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| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2017 / / edited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann
| Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2017 / / edited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2020 |
| Descrizione fisica | 1 online resource (87 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Technologien für die intelligente Automation, Technologies for Intelligent Automation |
| Soggetto topico |
Computational intelligence
Computer engineering Computer networks Telecommunication Data mining Computational Intelligence Computer Engineering and Networks Communications Engineering, Networks Data Mining and Knowledge Discovery |
| ISBN | 3-662-59084-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNINA-9910484573803321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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