Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2018: Technologien für die intelligente Automation, cartea 9
Editat de Jürgen Beyerer, Christian Kühnert, Oliver Niggemannen Limba Engleză Paperback – 18 dec 2018
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (3) | 210.45 lei 6-8 săpt. | |
| Saint Philip Street Press – 7 oct 2020 | 210.45 lei 6-8 săpt. | |
| Springer Berlin, Heidelberg – 18 dec 2018 | 347.46 lei 6-8 săpt. | |
| Springer Berlin, Heidelberg – 6 dec 2016 | 957.29 lei 6-8 săpt. | |
| Hardback (1) | 284.00 lei 6-8 săpt. | |
| Saint Philip Street Press – 7 oct 2020 | 284.00 lei 6-8 săpt. |
Preț: 347.46 lei
Preț vechi: 434.32 lei
-20% Nou
Puncte Express: 521
Preț estimativ în valută:
66.49€ • 70.11$ • 55.35£
66.49€ • 70.11$ • 55.35£
Carte tipărită la comandă
Livrare economică 10-24 ianuarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783662584842
ISBN-10: 3662584840
Pagini: 127
Ilustrații: VII, 136 p.
Dimensiuni: 168 x 240 mm
Greutate: 0.25 kg
Ediția:1st ed. 2019
Editura: Springer Berlin, Heidelberg
Colecția Springer Vieweg
Seria Technologien für die intelligente Automation
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3662584840
Pagini: 127
Ilustrații: VII, 136 p.
Dimensiuni: 168 x 240 mm
Greutate: 0.25 kg
Ediția:1st ed. 2019
Editura: Springer Berlin, Heidelberg
Colecția Springer Vieweg
Seria Technologien für die intelligente Automation
Locul publicării:Berlin, Heidelberg, Germany
Cuprins
Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project.- Deduction of time-dependent machine tool characteristics by fuzzy-clustering.- Unsupervised Anomaly Detection in Production Lines.- A Random Forest Based Classifer for Error Prediction of Highly Individualized Products.- Web-based Machine Learning Platform for Condition-Monitoring.- Selection and Application of Machine Learning-Algorithms in Production Quality.- Which deep artifificial neural network architecture to use for anomaly detection in Mobile Robots kinematic data.- GPU GEMM-Kernel Autotuning for scalable machine learners.- Process Control in a Press Hardening Production Line with Numerous Process Variables and Quality Criteria.- A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance.- Detection of Directed Connectivities in Dynamic Systems for Different Excitation Signals using Spectral Granger Causality.- Enabling Self-Diagnosis of AutomationDevices through Industrial Analytics.- Making Industrial Analytics work for Factory Automation Applications.- Application of Reinforcement Learning in Production Planning and Control of Cyber Physical Production Systems.- LoRaWan for Smarter Management of Water Network: From metering
to data analysis.
Notă biografică
Prof. Dr.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.
Dr. Christian Kühnert is a senior researcher 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-driven condition monitoring.
Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
Dr. Christian Kühnert is a senior researcher 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-driven condition monitoring.
Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
Textul de pe ultima copertă
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 Karlsruhe, October 23-24, 2018.
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.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.
Dr. ChristianKühnert is a senior researcher 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-driven condition monitoring.
Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
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.-Ing. Jürgen Beyerer is Professor at the Department for Interactive Real-Time Systems at the Karlsruhe Institute of Technology. In addition he manages the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB.
Dr. ChristianKühnert is a senior researcher 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-driven condition monitoring.
Prof. Dr. Oliver Niggemann is Professor for Artificial Intelligence in Automation. His research interests are in the fields of machine learning and data analysis for Cyber-Physical Systems and in the fields of planning and diagnosis of distributed systems. He is a board member of the research institute inIT and deputy director at the Fraunhofer Application Center Industrial Automation INA located in Lemgo.
Caracteristici
Includes the full proceedings of the 2018 ML4CPS – Machine Learning for Cyber Physical Systems Conference Presents recent and new advances in automated machine learning methods Provides an accessible and succinct overview on machine learning for cyber physical systems, industry 4.0 and IOT