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Format: 1 Online-Ressource

ISBN: 9783031470622

Series Statement: Technologien für die intelligente Automation volume 18

Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-47061-5

Language: English

Keywords: Konferenzschrift

DOI: 10.1007/978-3-031-47062-2

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

Author information: Beyerer, Jürgen 1961-

Author information: Niggemann, Oliver 1971-

UID:

Format: 1 online resource (130 pages)

Edition: 1st ed. 2024.

ISBN: 3-031-47062-1

Series Statement: Technologien für die intelligente Automation, Technologies for Intelligent Automation, 18

Content: 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.

Note: 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.

Additional Edition: ISBN 3-031-47061-3

Language: English

DOI: 10.1007/978-3-031-47062-2

UID:

Format: 1 online resource (130 pages)

Edition: 1st ed.

ISBN: 9783031470622

Series Statement: Technologien Für Die Intelligente Automation Series v.18

Content: Intro -- Preface -- Contents -- Causal Structure Learning Using PCMCI+ and Path Constraints from Wavelet-Based Soft Interventions -- 1 Introduction -- 2 Related Work -- 3 Fundamentals -- 3.1 Causal Graphs -- 3.2 Causal Structure Learning -- 4 Wavelet-Based Soft Interventions -- 5 Applying Wavelet Injections -- 6 Summary and Conclusion -- Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining -- 1 Introduction -- 2 The Potential of Pretraining -- 3 Discussion and Conclusion -- Using ML-Based Models in Simulation of CPPSs: A Case Study of Smart Meter Production -- 1 Introduction and Problem Statement -- 2 Use Case -- 3 Proposed Approach -- 4 Experiments -- 5 Conclusions and Future Work -- Deploying Machine Learning in High Pressure Resin Transfer Molding and Part Post Processing: A Case Study -- 1 Introduction -- 1.1 Composite Manufacturing by RTM -- 1.2 Knowledge Extraction in a Complex Network of Cyber-Physical Systems -- 2 Implemented Approach -- 2.1 Data Management and Analysis -- 2.2 Process Monitoring and Predictive Maintenance for serial HP-RTM Production -- 2.3 Process Monitoring and Quality Assurance in Post-Processing -- 3 Preliminary Results -- 3.1 Comparison of Physical to Date-Centric Modelling -- 4 Conclusions & -- Outlook -- References -- Development of a Robotic Bin Picking Approach Based on Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Research Issue -- 2.2 Selection of a Machine Learning Technique -- 3 Approach -- 3.1 Robotic Bin Picking Based on Reinforcement Learning -- 3.2 Training Procedure -- 3.3 Training Environment -- 4 Conclusion -- Control Reconfiguration of CPS via Online Identification Using Sparse Regression (SINDYc) -- 1 Introduction -- 2 Related Work -- 2.1 Model-Based Fault Tolerant Control.

Note: Description based on publisher supplied metadata and other sources

Additional Edition: ISBN 9783031470615

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783031470615

Language: English

Keywords: Electronic books.

URL: lizenzpflichtig

URL: SpringerLink

URL: https://public.ebookcentral.proquest.com/choice/PublicFullRecord.aspx?p=31502925

URL: EBSCOhost

UID:

Format: 1 online resource (130 pages)

Edition: 1st ed. 2024.

ISBN: 3-031-47062-1

Series Statement: Technologien für die intelligente Automation, Technologies for Intelligent Automation, 18

Content: 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.

Note: 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.

Additional Edition: ISBN 3-031-47061-3

Language: English

DOI: 10.1007/978-3-031-47062-2

UID:

Format: VIII, 129 p. 39 illus., 32 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031470622

Series Statement: Technologien für die intelligente Automation, Technologies for Intelligent Automation, 18

Content: 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.

Note: 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.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031470615

Additional Edition: Printed edition: ISBN 9783031470639

Language: English

DOI: 10.1007/978-3-031-47062-2

URL: https://doi.org/10.1007/978-3-031-47062-2

UID:

Format: 1 online resource (130 pages)

Edition: 1st ed.

ISBN: 9783031470622

Series Statement: Technologien Für Die Intelligente Automation Series ; v.18

Note: Intro -- Preface -- Contents -- Causal Structure Learning Using PCMCI+ and Path Constraints from Wavelet-Based Soft Interventions -- 1 Introduction -- 2 Related Work -- 3 Fundamentals -- 3.1 Causal Graphs -- 3.2 Causal Structure Learning -- 4 Wavelet-Based Soft Interventions -- 5 Applying Wavelet Injections -- 6 Summary and Conclusion -- Reinforcement Learning from Human Feedback for Cyber-Physical Systems: On the Potential of Self-Supervised Pretraining -- 1 Introduction -- 2 The Potential of Pretraining -- 3 Discussion and Conclusion -- Using ML-Based Models in Simulation of CPPSs: A Case Study of Smart Meter Production -- 1 Introduction and Problem Statement -- 2 Use Case -- 3 Proposed Approach -- 4 Experiments -- 5 Conclusions and Future Work -- Deploying Machine Learning in High Pressure Resin Transfer Molding and Part Post Processing: A Case Study -- 1 Introduction -- 1.1 Composite Manufacturing by RTM -- 1.2 Knowledge Extraction in a Complex Network of Cyber-Physical Systems -- 2 Implemented Approach -- 2.1 Data Management and Analysis -- 2.2 Process Monitoring and Predictive Maintenance for serial HP-RTM Production -- 2.3 Process Monitoring and Quality Assurance in Post-Processing -- 3 Preliminary Results -- 3.1 Comparison of Physical to Date-Centric Modelling -- 4 Conclusions & -- Outlook -- References -- Development of a Robotic Bin Picking Approach Based on Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 2.1 Research Issue -- 2.2 Selection of a Machine Learning Technique -- 3 Approach -- 3.1 Robotic Bin Picking Based on Reinforcement Learning -- 3.2 Training Procedure -- 3.3 Training Environment -- 4 Conclusion -- Control Reconfiguration of CPS via Online Identification Using Sparse Regression (SINDYc) -- 1 Introduction -- 2 Related Work -- 2.1 Model-Based Fault Tolerant Control. , 2.2 Online, Closed-Loop System Identification -- 3 System Description and Modeling -- 4 Closed-Loop System Identification with SINDYc -- 4.1 Sparse Identification-SINDYc -- 4.2 Identifiability in Closed-Loop Systems -- 5 Control Reconfiguration -- 6 Results -- 6.1 Closed-Loop Identification Parameter Study -- 6.2 Closed-Loop Identification and Control Reconfiguration -- 7 Limitations and Outlook -- Using Forest Structures for Passive Automata Learning -- 1 Introduction -- 2 Preliminaries -- 3 Algorithms for Learning of Automata Forests -- 3.1 Forest Structure -- 3.2 Forest with Cross Validation (ForestCV) -- 3.3 Forest with Majority Voting (ForestMV) -- 4 Experimental Evaluation -- 4.1 Hyperparameter Tuning -- 4.2 Analyzing DFAs -- 4.3 Analyzing Mealy Machines -- 5 Conclusion -- Domain Knowledge Injection Guidance for Predictive Maintenance -- 1 Introduction -- 2 Related Work -- 3 Guidance Development -- 3.1 Knowledge Injection Framework -- 3.2 Literature Study and Construction of the Knowledge Base -- 3.3 Guidance Creation -- 4 Examples for the Application of the Guidance -- 5 Discussion -- 6 Conclusion -- Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories -- 1 Introduction -- 2 State of the Art -- 2.1 Formalization of Data Analytics Use Cases in Smart Factories -- 2.2 Product, Process and Resource in Smart Factories -- 3 Structuring Prescriptive Analytics in a Smart Factory Environment -- 3.1 Data Analytics View on Use Cases -- 3.2 Smart Manufacturing View on Use Cases -- 4 Conclusion -- References -- Development of a Standardized Data Acquisition Prototype for Heterogeneous Sensor Environments as a Basis for ML Applications in Pultrusion -- 1 Introduction -- 2 Industrial Communication - State of the Art -- 3 Concept Development for Machine Data Acquisition -- 3.1 Requirements for a Standardized Data Acquisition. , 3.2 Selection of Preferred Standards -- 3.3 Retrofitting a Standardized Data Acquisition System -- 3.4 Concept Evaluation -- 4 Summary and Outlook -- References -- A Digital Twin Design for Conveyor Belts Predictive Maintenance -- 1 Introduction -- 2 Related Work -- 3 Framework -- 3.1 Data Flow -- 3.2 PLC and Sensors-Physical Twin -- 3.3 Data Connectivity and Collection-Cyber-Physical System -- 3.4 Virtual Twin -- 4 Discussion and Future Work -- Augmenting Explainable Data-Driven Models in Energy Systems: A Python Framework for Feature Engineering -- 1 Introduction -- 1.1 Main Contribution -- 2 Method -- 3 Case Study -- 4 Conclusion.

Additional Edition: Print version: Niggemann, Oliver Machine Learning for Cyber-Physical Systems Cham : Springer,c2024 ISBN 9783031470615

Language: English

Keywords: Electronic books.

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