Spatio-Temporal Learning and Monitoring for Complex Dynamic Processes with Irregular Data - Softcover

Zhao

Softcover

ISBN 10: 044333675X ISBN 13: 9780443336751

Verlag: Academic Press, 2025

Inhaltsangabe

Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modeling, and monitoring methods for highly complex dynamic processes with irregular data. Two classes of robust modeling methods are highlighted, including low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly, the Laplace distribution is explored, which is adopted to measure the process uncertainty to develop robust monitoring models.

The book not only discusses the complex models but also their real-world applications in industry.

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Über die Autorinnen und Autoren

Chunhui Zhao is a Qiushi distinguished professor at Zhejiang University in China, and an expert in intelligent industrial monitoring with 20 years of experience in this field. She has authored or co-authored more than 400 papers in peer-reviewed international journals and conferences. Her research interests include statistical machine learning and data mining for industrial applications.

Wanke Yu is a research fellow at the School of Electrical & Electronic Engineering, Nanyang Technological University in Singapore. Wanke Yu received his Ph.D. degree in automatic control from Zhejiang University, Hangzhou, China, in 2020. His research interests include probabilistic graphic model, deep neural network, and nonconvex optimization, and their applications to process control.

Von der hinteren Coverseite

Process monitoring is used to analyse the operational status of the process system and provide an early warning in order to prevent industrial accidents. It is therefore an effective means to facilitate efficient, safe and optimal operation of industrial processes. Spatio-Temporal Learning Using Irregular Data for Complex Dynamic Processes introduces learning, modelling and monitoring methods for highly complex dynamic processes that have irregular data. Two classes of robust modelling methods are included in the book: firstly, low-rank characteristic of matrices and heavy-tailed characteristic of distributions. In this class, the missing data, ambient noise, and outlier problems are solved using low-rank matrix complement for monitoring model development. Secondly: the Laplace distribution, which is adopted to measure the process uncertainty to develop robust monitoring models. The book not only discusses the complex models but also their real-world applications in industry

Details zur Ausgabe

• Verlag: Academic Press
• Erscheinungsdatum: 2025
• Sprache: Englisch
• ISBN 10: 044333675X
• ISBN 13: 9780443336751
• Einband: Taschenbuch
• Auflage: 1
• Anzahl der Seiten: 258
• Kontakt zum Hersteller: Academic Press; https://shop.elsevier.com/; ; 230 PARK AVENUE, SUITE 800; NY; NY; 10169; USA
• Verantwortliche Person: eVatmaster Consulting GmbH; 497000000000; contact@evatmaster.com; ; Bettinastr. 30,60325; Frankfurt am Main; 60325; Deutschland