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Advanced signal processingdecomposition, entropy, and machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced signal processingby Yuning Zhang ... [et al.].
Reminder of title:	decomposition, entropy, and machine learning /
other author:	Zhang, Yuning.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	viii, 83 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Signal processing.
Online resource:	https://doi.org/10.1007/978-3-032-11854-7
ISBN:	9783032118547$q(electronic bk.)

Advanced signal processingdecomposition, entropy, and machine learning /
Advanced signal processingdecomposition, entropy, and machine learning /[electronic resource] :by Yuning Zhang ... [et al.]. - Cham :Springer Nature Switzerland :2025. - viii, 83 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in energy,2191-5539. - SpringerBriefs in energy..

Introduction -- Signal decomposition methods -- Entropy analysis methods -- Machine learning methods -- Signal denoising applications -- Pattern recognition applications -- Conclusion.

This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering.

ISBN: 9783032118547$q(electronic bk.)

Standard No.: 10.1007/978-3-032-11854-7doiSubjects--Topical Terms:

182382
Signal processing.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Advanced signal processingdecomposition, entropy, and machine learning /
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505 0 $a Introduction -- Signal decomposition methods -- Entropy analysis methods -- Machine learning methods -- Signal denoising applications -- Pattern recognition applications -- Conclusion.
520 $a This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering.
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