Advanced Signal Processing : Decomposition, Entropy, and Machine Learning / by Yuning Zhang, Chenxin Yang, Peng Luo, Heng Zhang.

Format:

Book

Author/Creator:

Zhang, Yuning.

Series:

SpringerBriefs in Energy, 2191-5539

Language:

English

Subjects (All):

Electric power distribution.

Water-power.

Electrical engineering.

Machine learning.

Energy Grids and Networks.

Hydroenergy.

Electrical and Electronic Engineering.

Machine Learning.

Local Subjects:

Energy Grids and Networks.

Hydroenergy.

Electrical and Electronic Engineering.

Machine Learning.

Physical Description:

1 online resource (93 pages)

Edition:

1st ed. 2025.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2025.

Summary:

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.

Contents:

Introduction

Signal decomposition methods

Entropy analysis methods

Machine learning methods

Signal denoising applications

Pattern recognition applications

Conclusion.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-032-11854-9

9783032118547

OCLC:

1572215563