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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 2023 / / edited by Oliver Niggemann, Jürgen Beyerer, Maria Krantz, Christian Kühnert
| Machine Learning for Cyber-Physical Systems : Selected papers from the International Conference ML4CPS 2023 / / edited by Oliver Niggemann, Jürgen Beyerer, Maria Krantz, Christian Kühnert |
| Autore | Niggemann Oliver |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (130 pages) |
| Disciplina | 621.38 |
| Altri autori (Persone) |
BeyererJürgen
KrantzMaria KühnertChristian |
| Collana | Technologien für die intelligente Automation, Technologies for Intelligent Automation |
| Soggetto topico |
Cooperating objects (Computer systems)
Computer engineering Computer networks Artificial intelligence Neural networks (Computer science) Cyber-Physical Systems Computer Engineering and Networks Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 3-031-47062-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Causal Structure Learning using PCMCI+ and Path Constraints from Wavelet-based Soft Interventions -- Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining -- Using ML-based Models in Simulation of CPPSs: A Case Study of Smart Meter Production -- Deploying machine learning in high pressure resin transfer molding and part post processing: a case study -- Development of a Robotic Bin Picking Approach based on Reinforcement Learning -- Control Reconfiguration of CPS via Online Identification using Sparse Regression (SINDYc) -- Using Forest Structures for Passive Automata Learning -- Domain Knowledge Injection Guidance for Predictive Maintenance -- Towards a systematic approach for Prescriptive Analytics use cases in smart factories -- Development of a standardized data acquisition prototype for heterogeneous sensor environments as a basis for ML applications in pultrusion -- A Digital Twin Design for conveyor belts predictive maintenance -- Augmenting explainable data-driven models in energy systems: A Python framework for feature engineering. |
| Record Nr. | UNINA-9910866569003321 |
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Niggemann Oliver
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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