Machine Learning for Cyber Physical Systems : : Selected Papers from the International Conference ML4CPS 2020.

• Superior document: Technologien Für Die Intelligente Automation Series ; v.13
• Author: Beyerer, Jürgen.
• TeilnehmendeR: Maier, Alexander.; Niggemann, Oliver.
• Place / Publishing House: Berlin, Heidelberg : : Springer Berlin / Heidelberg,, 2020.; {copy}2021.
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Series: Technologien Für Die Intelligente Automation Series
• Physical Description: 1 online resource (129 pages)

Table of Contents

• Intro
• Preface
• Table of Contents
• 1 Energy Profile Prediction of Milling Processes Using Machine Learning Techniques
• 1 Einleitung
• 2 Methode
• 3 Datenerhebung und -aufbereitung
• 3.1 Gewinnung der Zielwerte Energie- und Zeitbedarf
• 3.2 Gewinnung der Inputparameter für die Regressionsmodelle
• 3.3 Feature Engineering
• 4 Modellbildung
• 5 Ergebnisse und Validierung
• 6 Diskussion und Ausblick
• References
• 2 Improvement of the prediction quality of electrical load profiles with artificial neural networks
• 1 Introduction
• 2 Analysis of the load profiles
• 2.1 Primary data preparation and plausibility check
• 2.2 Data analysis and creation load profile classes
• 2.3 Parameter estimation
• 2.4 Splitting the data sets
• 3 Artificial neural network as prediction model
• 3.1 Research studies
• 3.2 Basic specifications of the model
• 3.3 Investigation scenarios
• 4 Simulation and evaluation of the results
• 5 Conclusion and Outlook
• References
• 3 Detection and localization of an underwater docking station in acoustic images using machine learning and generalized fuzzy hough transform
• 1 Introduction
• 2 Methodology
• 3 Experimental results
• 4 Conclusions and future work
• 5 Acknowledgements
• References
• 4 Deployment architecture for the local delivery of ML-Models to the industrial shop floor
• 1 Introduction
• 2 Aim of the presented work
• 3 Related Work
• 4 Architecture
• 5 Data connectivity and collection
• 6 ML-Model Serving
• 7 Monitoring Strategies
• 8 Lifecycle Management
• 9 Discussion
• 10 Acknowledgement
• References
• 5 Deep Learning in Resource and Data Constrained Edge Computing Systems
• 1 Introduction
• 2 Methods &amp
• Related Work
• 2.1 Variational Autoencoder
• 2.2 Federated Learning
• 3 Results
• 3.1 Clustering and Visualization of Wafermap Patterns.
• 3.2 Anomaly Detection for Sensor Data of a Furnace
• 3.3 Predictive Maintenance using Federated Learning on Edge Devices
• 4 Conclusion
• References
• 6 Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis
• 1 Introduction
• 2 Dynamic Time Warping
• 3 Survival Analysis
• 4 Data
• 5 Proposed System
• 6 Results
• 7 Conclusion
• References
• 7 Proposal for requirements on industrial AI solutions
• 1 Introduction
• 1.1 Usage of AI in Industrial Production
• 1.2 Industrial AI
• 2 Requirements on industrial AI
• 2.1 Adaption of Industrial AI systems
• 2.2 Engineering of Industrial AI systems
• 2.3 Embedding of Industrial AI system in existing production system landscape
• 2.4 Safety and Security of Industrial AI systems
• 2.5 Trust in functionality of Industrial AI systems
• 3 Discussion
• 4 Conclusion
• Acknowledgements
• References
• 8 Information modeling and knowledge extraction for machine learning applications in industrial production systems
• 1 Introduction
• 2 Information modeling
• 3 Tool chain for knowledge extraction
• 4 Conclusion
• 5 Acknowledgement
• Appendix: Entities of the proposed information model
• References
• 9 Explanation Framework for Intrusion Detection
• 1 Introduction
• 2 Explanations for Intrusion Detection
• 3 The Modular Phases of Explanations
• 4 Experiment
• 5 Summary
• References
• 10 Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning
• 1 Introduction
• 2 Related Works
• 3 Hypothesis
• 4 Evaluation
• 5 Conclusion And Future Works
• References
• 11 Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks
• 1 Introduction
• 2 Related work
• 3 Solution
• 4 Results
• 5 Conclusion
• References.
• 12 First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems
• 1 Introduction
• 2 State of the Art
• 3 The multiple-tank model
• 4 Diagnosing Hybrid Systems
• 5 Reconfiguration after faults occurred
• 6 Conclusion and future work
• 7 Acknowledgement
• References
• 13 Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data
• 1 Introduction
• 2 Network architecture and and training the model
• 3 Results
• 4 Conclusion
• References.