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|a 9783662627464
|q (electronic bk.)
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|z 9783662627457
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|a (MiAaPQ)EBC6436125
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|a (Au-PeEL)EBL6436125
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|a (OCoLC)1231609193
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|a MiAaPQ
|b eng
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|c MiAaPQ
|d MiAaPQ
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|a TK7895.E42
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|a Beyerer, Jürgen.
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|a Machine Learning for Cyber Physical Systems :
|b Selected Papers from the International Conference ML4CPS 2020.
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|a 1st ed.
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|a Berlin, Heidelberg :
|b Springer Berlin / Heidelberg,
|c 2020.
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| 264 |
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|c {copy}2021.
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| 300 |
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|a 1 online resource (129 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
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|a Technologien Für Die Intelligente Automation Series ;
|v v.13
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| 505 |
0 |
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|a 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 & -- Related Work -- 2.1 Variational Autoencoder -- 2.2 Federated Learning -- 3 Results -- 3.1 Clustering and Visualization of Wafermap Patterns.
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| 505 |
8 |
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|a 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.
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|a 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.
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| 588 |
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|a Description based on publisher supplied metadata and other sources.
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| 590 |
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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| 655 |
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4 |
|a Electronic books.
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| 700 |
1 |
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|a Maier, Alexander.
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| 700 |
1 |
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|a Niggemann, Oliver.
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| 776 |
0 |
8 |
|i Print version:
|a Beyerer, Jürgen
|t Machine Learning for Cyber Physical Systems
|d Berlin, Heidelberg : Springer Berlin / Heidelberg,c2020
|z 9783662627457
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| 797 |
2 |
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|a ProQuest (Firm)
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| 830 |
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0 |
|a Technologien Für Die Intelligente Automation Series
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6436125
|z Click to View
|
