Produktbild: Machine Learning for Cyber-Physical Systems
Band 18

Machine Learning for Cyber-Physical Systems Selected papers from the International Conference ML4CPS 2023

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.06.2024

Abbildungen

VIII, 129 p. 39 illus., 32 illus. in color.

Herausgeber

Oliver Niggemann + weitere

Verlag

Springer

Seitenzahl

129

Maße (L/B/H)

24/16,8/0,8 cm

Gewicht

277 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-47061-5

Beschreibung

Portrait

Prof. Dr. Oliver Niggemann  held the professorship at the Institute for Industrial Information Technologies (inIT) in Lemgo (Germany) from 2008 to 2019 and was also deputy head of the Fraunhofer IOSB-INA until 2019. In 2019, he took over the university professorship "Computer Science in Mechanical Engineering" at the Helmut Schmidt University in Hamburg. His research at the Institute for Automation Technology is in the field of artificial intelligence and machine learning for cyber-physical systems. 

Prof. Dr.-Ing. Jürgen Beyerer  is a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology KIT and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. Research interests include automated visual inspection, signal and image processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation.

Dr. Maria Krantz  is a Postdoc at the Helmut Schmidt University in Hamburg. Her main research interests are causality in Cyber-Physical Systems and applications of diagnosis algorithms in production systems. 

Dr. Christian Kühnert  is senior scientist at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB. His research interests are in the field of machine-learning, data-fusion and data analytics for cyber-physical systems.







Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.06.2024

Abbildungen

VIII, 129 p. 39 illus., 32 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

129

Maße (L/B/H)

24/16,8/0,8 cm

Gewicht

277 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-47061-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: GPSR Kontakt

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  • Produktbild: Machine Learning for Cyber-Physical Systems
  • 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.