Machine learning for cyber physical systems : selected papers from the international conference ML4CPS 2020 ; Berlin, Germany, March 12-13, 2020 / / editors, Jürgen Beyerer, Alexander Maier, Oliver Niggemann |
Autore | Beyerer Jürgen |
Edizione | [1st edition 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 | Technologies for Intelligent Automation |
Soggetto topico | Machine learning |
Soggetto non controllato |
Cyber-physical systems, IoT
Communications Engineering, Networks Computer Systems Organization and Communication Networks Cyber-Physical Systems Computer Engineering and Networks Machine Learning Artificial Intelligence Cognitive Robotics Internet of Things Computational intelligence Computer-based algorithms Smart grid Open Access Industry 4.0 Electrical engineering Cybernetics & systems theory Communications engineering / telecommunications Computer networking & communications |
ISBN | 3-662-62746-9 |
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 Example Attacks -- 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 | ||
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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 |
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 organization Electrical engineering Data mining Computational Intelligence Computer Systems Organization and Communication 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 | ||
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Lo trovi qui: Univ. Federico II | ||
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