Machine Learning for Cyber-Physical Systems Selected papers from the International Conference ML4CPS 2023
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 31...
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| Main Authors | , , , |
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| Format | eBook |
| Language | English |
| Published |
Cham
Springer Nature
2024
Springer |
| Edition | 1 |
| Series | Technologien für die intelligente Automation |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. This is an open access book. |
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| AbstractList | 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. This is an open access book. |
| Author | Niggemann, Oliver Kühnert, Christian Beyerer, Jürgen Krantz, Maria |
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| Editor | Niggemann, Oliver Kühnert, Christian Beyerer, Jürgen Krantz, Maria |
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| Snippet | This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. It contains some selected papers... |
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| SubjectTerms | Applied mathematics Artificial intelligence Automatic validation Computer hardware Computer science Computing and Information Technology Cyber-physical systems Cybernetics and systems theory Electronics and communications engineering Electronics engineering Information theory Machine learning Mathematical modelling Mathematics Mathematics and Science Network architecture Neural networks Reference, Information and Interdisciplinary subjects Research and information: general Technology, Engineering, Agriculture, Industrial processes |
| Subtitle | Selected papers from the International Conference ML4CPS 2023 |
| TableOfContents | 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 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 -- Correction to: Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories -- Correction to: Chapter 9 in: O. Niggemann et al. (eds.), Machine Learning for Cyber-Physical Systems, Technologien für die intelligente Automation, https://doi.org/10.1007/978-3-031-47062-2_9 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 |
| Title | Machine Learning for Cyber-Physical Systems |
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