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Related Experiment Video

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Research on Sea State Signal Recognition Based on Beluga Whale Optimization-Slope Entropy and One

Yuxing Li1,2, Zhaoyu Gu1, Xiumei Fan1

  • 1School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

A new beluga whale optimization-slope entropy (BWO-SlEn) method enhances sea state signal (SSS) recognition in marine environments. This approach, combined with a one-dimensional convolutional neural network (1D-CNN), significantly improves accuracy over existing methods.

Keywords:
beluga whale optimizationfeature extractionone-dimensional convolutional neural networksea state signalslope entropy

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Area of Science:

  • Marine acoustics
  • Signal processing
  • Artificial intelligence

Background:

  • Recognizing sea state signals (SSSs) in complex marine environments is challenging.
  • Existing methods for underwater acoustic signal recognition require improvement in accuracy and robustness.

Purpose of the Study:

  • To introduce a novel nonlinear dynamic analysis method, beluga whale optimization-slope entropy (BWO-SlEn), for SSS recognition.
  • To propose an underwater acoustic signal recognition method integrating BWO-SlEn with a one-dimensional convolutional neural network (1D-CNN).

Main Methods:

  • Feature extraction using BWO-SlEn, particle swarm optimization-slope entropy (PSO-SlEn), and Harris hawk optimization-slope entropy (HHO-SlEn).
  • Signal feature extraction using fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE).
  • Classification using a one-dimensional convolutional neural network (1D-CNN).

Main Results:

  • BWO-SlEn demonstrated superior feature extraction performance compared to PSO-SlEn and HHO-SlEn for SSS recognition.
  • The combination of BWO-SlEn and 1D-CNN achieved the highest recognition rate among tested entropy methods (FE, SE, PE, DE).
  • The proposed BWO-SlEn and 1D-CNN method showed at least 6% and 4.75% higher recognition rates for noise and SSS, respectively, compared to six other methods.

Conclusions:

  • The BWO-SlEn method is effective for feature extraction in underwater acoustic signal recognition.
  • The integration of BWO-SlEn with 1D-CNN offers a highly effective approach for sea state signal recognition.
  • This novel method provides a significant advancement for SSS recognition applications in challenging marine settings.