東華大學圖書館 |

Machine learning for cyber-physical systems = selected papers from the International Conference ML4CPS 2023 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for cyber-physical systems/ edited by Oliver Niggemann ... [et al.].
Reminder of title:	selected papers from the International Conference ML4CPS 2023 /
remainder title:	ML4CPS 2023
other author:	Niggemann, Oliver.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	viii, 129 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-031-47062-2
ISBN:	9783031470622

Machine learning for cyber-physical systems = selected papers from the International Conference ML4CPS 2023 /
Machine learning for cyber-physical systemsselected papers from the International Conference ML4CPS 2023 /[electronic resource] :ML4CPS 2023edited by Oliver Niggemann ... [et al.]. - Cham :Springer Nature Switzerland :2024. - viii, 129 p. :ill. (some col.), digital ;24 cm. - Technologien für die intelligente Automation, technologies for intelligent automation,v. 182522-8587 ;. - Technologien für die intelligente Automation, technologies for intelligent automation ;v. 18..

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.

Open access.

This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber- Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023. Cyber-Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors 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.

ISBN: 9783031470622

Standard No.: 10.1007/978-3-031-47062-2doiSubjects--Topical Terms:

576368
Machine learning
--Congresses.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for cyber-physical systems = selected papers from the International Conference ML4CPS 2023 /
LDR:04560nmm a2200385 a 4500 001 2372389
003 DE-He213
005 20240620125226.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031470622 $q (electronic bk.)
020 $a 9783031470615 $q (paper)
024 7 $a 10.1007/978-3-031-47062-2 $2 doi
035 $a 978-3-031-47062-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a TJF $2 bicssc
072 7 $a GPFC $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a GPFC $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M149 2024
245 0 0 $a Machine learning for cyber-physical systems $h [electronic resource] : $b selected papers from the International Conference ML4CPS 2023 / $c edited by Oliver Niggemann ... [et al.].
246 3 $a ML4CPS 2023
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a viii, 129 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Technologien für die intelligente Automation, technologies for intelligent automation, $x 2522-8587 ; $v v. 18
505 0 $a 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.
506 $a Open access.
520 $a This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber- Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023. Cyber-Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. The Editors 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.
650 0 $a Machine learning $x Congresses. $3 576368
650 0 $a Cooperating objects (Computer systems) $v Congresses. $3 3218484
650 1 4 $a Cyber-Physical Systems. $3 3591993
650 2 4 $a Computer Engineering and Networks. $3 3538504
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
700 1 $a Niggemann, Oliver. $3 2182964
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a ML4CPS (Conference) $n (6th : $d 2023 : $c Hamburg, Germany) $3 3719109
773 0 $t Springer Nature eBook
830 0 $a Technologien für die intelligente Automation, technologies for intelligent automation ; $v v. 18. $3 3719108
856 4 0 $u https://doi.org/10.1007/978-3-031-47062-2
950 $a Engineering (SpringerNature-11647)