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Record Nr. |
UNINA9911049216703321 |
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Autore |
Zhang Yuning |
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Titolo |
Advanced Signal Processing : Decomposition, Entropy, and Machine Learning / / by Yuning Zhang, Chenxin Yang, Peng Luo, Heng Zhang |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (93 pages) |
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Collana |
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SpringerBriefs in Energy, , 2191-5539 |
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Disciplina |
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Soggetti |
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Electric power distribution |
Water-power |
Electrical engineering |
Machine learning |
Energy Grids and Networks |
Hydroenergy |
Electrical and Electronic Engineering |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Signal decomposition methods -- Entropy analysis methods -- Machine learning methods -- Signal denoising applications -- Pattern recognition applications -- Conclusion. |
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Sommario/riassunto |
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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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