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The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

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  • Yuxing Li

    (Faculty of Information Technology and Equipment Engineering, Xi’an University of Technology, Xi’an 710048, Shaanxi, China)

  • Xiao Chen

    (College of Electrical & Information Engineering, ShaanXi University of Science & Technology, Xi’an 710021, Shaanxi, China)

  • Jing Yu

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China)

  • Xiaohui Yang

    (School of Art and Design, Inner Mongolia University of Science & Technology, Baotou 014010, Inner Mongolia, China)

  • Huijun Yang

    (College of Information Engineering, Northwest A&F University, Yang’ling 712100, Shaanxi, China)

Abstract

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

Suggested Citation

  • Yuxing Li & Xiao Chen & Jing Yu & Xiaohui Yang & Huijun Yang, 2019. "The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy," Energies, MDPI, vol. 12(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:359-:d:200294
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    References listed on IDEAS

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    1. Jianzhong Zhou & Na Sun & Benjun Jia & Tian Peng, 2018. "A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 11(7), pages 1-27, July.
    2. Dechang Yang & Wenlong Liao & Yusen Wang & Keqing Zeng & Qiuyue Chen & Dingqian Li, 2018. "Data-Driven Optimization Control for Dynamic Reconfiguration of Distribution Network," Energies, MDPI, vol. 11(10), pages 1-18, October.
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