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IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency [[electronic resource] ] : Intelligent Methods for the Factory of the Future / / edited by Oliver Niggemann, Peter Schüller
| IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency [[electronic resource] ] : Intelligent Methods for the Factory of the Future / / edited by Oliver Niggemann, Peter Schüller |
| Autore | Niggemann Oliver |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Berlin, Heidelberg, : Springer Nature, 2018 |
| Descrizione fisica | 1 online resource (VII, 129 p. 52 illus., 29 illus. in color.) |
| Disciplina | 658.56 |
| Collana | Technologien für die intelligente Automation, Technologies for Intelligent Automation |
| Soggetto topico |
Quality control
Reliability Industrial safety Robotics Automation Input-output equipment (Computers) Quality Control, Reliability, Safety and Risk Robotics and Automation Input/Output and Data Communications |
| Soggetto non controllato |
Engineering
Quality control Reliability Industrial safety Robotics Automation Input-output equipment (Computers) |
| ISBN | 3-662-57805-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems -- Social Science Contributions to Engineering Projects: Looking Beyond Explicit Knowledge Through the Lenses of Social Theory -- Enable learning of Hybrid Timed Automata in Absence of Discrete Events through Self-Organizing Maps -- Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps -- A Sampling-Based Method for Robust and Efficient Fault Detection in Industrial Automation Processes -- Validation of similarity measures for industrial alarm flood analysis -- Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause. |
| Record Nr. | UNINA-9910299919103321 |
Niggemann Oliver
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| Berlin, Heidelberg, : Springer Nature, 2018 | ||
| 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 2020 ; Berlin, Germany, March 12-13, 2020 / / editors, Jürgen Beyerer, Alexander Maier, Oliver Niggemann
| 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 |
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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 [[electronic resource] ] : Selected papers from the International Conference ML4CPS 2017 / / edited by Jürgen Beyerer, Alexander Maier, Oliver Niggemann
| Machine Learning for Cyber Physical Systems [[electronic resource] ] : 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 | ||
| 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 2018 / / edited by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann
| Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2018 / / edited by Jürgen Beyerer, Christian Kühnert, Oliver Niggemann |
| Edizione | [First edition, 2019.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2019 |
| Descrizione fisica | 1 online resource (VII, 136 pages) |
| Disciplina | 006.3 |
| 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 | 3-662-58485-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project -- Deduction of time-dependent machine tool characteristics by fuzzy-clustering -- Unsupervised Anomaly Detection in Production Lines -- A Random Forest Based Classifer for Error Prediction of Highly Individualized Products -- Web-based Machine Learning Platform for Condition-Monitoring -- Selection and Application of Machine Learning-Algorithms in Production Quality -- Which deep artifificial neural network architecture to use for anomaly detection in Mobile Robots kinematic data -- GPU GEMM-Kernel Autotuning for scalable machine learners -- Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria -- A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance -- Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality -- Enabling Self-Diagnosis of AutomationDevices through Industrial Analytics -- Making Industrial Analytics work for Factory Automation Applications -- Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems -- LoRaWan for Smarter Management of Water Network: From metering to data analysis. |
| Record Nr. | UNINA-9910372753003321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Cyber Physical Systems [[electronic resource] ] : Selected papers from the International Conference ML4CPS 2016 / / edited by Jürgen Beyerer, Oliver Niggemann, Christian Kühnert
| Machine Learning for Cyber Physical Systems [[electronic resource] ] : Selected papers from the International Conference ML4CPS 2016 / / edited by Jürgen Beyerer, Oliver Niggemann, Christian Kühnert |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2017 |
| Descrizione fisica | 1 online resource (VII, 72 p. 24 illus., 19 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Technologien für die intelligente Automation, Technologies for Intelligent Automation |
| Soggetto topico |
Computational intelligence
Data mining Knowledge management Computational Intelligence Data Mining and Knowledge Discovery Knowledge Management |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Concept for the Application of Reinforcement Learning in the Optimization of CAM-Generated Tool Paths -- Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection -- Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment -- A Modular Architecture for Smart Data Analysis using AutomationML, OPC-UA and Data-driven Algorithms -- Cloud-based event detection platform for water distribution networks using machine-learning algorithms -- A Generic Data Fusion and Analysis Platform for Cyber-Physical Systems -- Agent Swarm Optimization: Exploding the search space -- Anomaly Detection in Industrial Networks using Machine Learning. . |
| Record Nr. | UNINA-9910155297303321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Cyber Physical Systems [[electronic resource] ] : Selected papers from the International Conference ML4CPS 2015 / / edited by Oliver Niggemann, Jürgen Beyerer
| Machine Learning for Cyber Physical Systems [[electronic resource] ] : Selected papers from the International Conference ML4CPS 2015 / / edited by Oliver Niggemann, Jürgen Beyerer |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2016 |
| Descrizione fisica | 1 online resource (124 p.) |
| Disciplina | 006.31 |
| Collana | Technologien für die intelligente Automation, Technologies for Intelligent Automation |
| Soggetto topico |
Computational intelligence
Data mining Knowledge management Computational Intelligence Data Mining and Knowledge Discovery Knowledge Management |
| ISBN | 3-662-48838-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNINA-9910253965803321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer Vieweg, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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