Machine learning for cyber physical systems : selected papers from the international conference ML4CPS 2020 ; Berlin, Germany, March 12-13, 2020

Machine learning for cyber physical systems selected papers from the international conference ML4CPS 2020 ; Berlin, Germany, March 12-13, 2020

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This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber P...

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Detalles Bibliográficos
Otros Autores: Beyerer, Jürgen (Editor ), Beyerer, Jürgen. editor (editor), Maier, Alexander. editor, Niggemann, Oliver. editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Nature 2021
2021.
Edición:1st edition 2021.
Colección:Technologies for Intelligent Automation, 13
Materias:
Machine learning > Congresses.
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
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009430291706719
Tabla de Contenidos:
  • 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.

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