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| Link zu diesem Datensatz | https://d-nb.info/1333620012 |
| Art des Inhalts | Konferenzschrift, 2017, Lemgo |
| Titel | Machine Learning for Cyber-Physical Systems : Selected papers from the International Conference ML4CPS 2023 / edited by Oliver Niggemann, Jürgen Beyerer, Maria Krantz, Christian Kühnert |
| Person(en) |
Niggemann, Oliver (Herausgeber) Beyerer, Jürgen (Herausgeber) Krantz, Maria (Herausgeber) Kühnert, Christian (Herausgeber) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2024 |
| Verlag | Cham : Springer Nature Switzerland, Imprint: Springer |
| Zeitliche Einordnung | Erscheinungsdatum: 2024 |
| Umfang/Format | Online-Ressource, VIII, 129 p. 39 illus., 32 illus. in color. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-47061-5 Printed edition:: ISBN: 978-3-031-47063-9 |
| Inhalt | 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 |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2406210409107.648991776686 DOI: 10.1007/978-3-031-47062-2 |
| URL | https://doi.org/10.1007/978-3-031-47062-2 |
| ISBN/Einband/Preis | 978-3-031-47062-2 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Technologien für die intelligente Automation, Technologies for Intelligent Automation ; 18 |
| Schlagwörter | Cyber-physisches System ; Maschinelles Lernen ; Anomalieerkennung ; Data Mining ; Datenanalyse |
| DDC-Notation | 006.31 [DDC23ger] |
| Sachgruppe(n) | 004 Informatik ; 620 Ingenieurwissenschaften und Maschinenbau ; 650 Management |
| Online-Zugriff | Archivobjekt öffnen |
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