UID:
almahu_9949845822602882
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.
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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.
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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.
