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Related Experiment Video

Updated: Aug 28, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Optimized Ship-Radiated Noise Feature Extraction Approaches Based on CEEMDAN and Slope Entropy.

Yuxing Li^1,2, Bingzhao Tang¹, Shangbin Jiao^1,2

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

Entropy (Basel, Switzerland)

|September 23, 2022

Summary

This study enhances ship-radiated noise signal analysis by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Slope Entropy (Slopen). Optimized single- and dual-intrinsic mode function approaches significantly improve feature extraction accuracy.

Keywords:

CEEMDAN dispersion entropy feature extraction ship-radiated noise slope entropy

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Area of Science:

Signal Processing
Acoustics
Machine Learning

Background:

Slope Entropy (Slopen) effectively analyzes signal complexity for feature extraction.
Current Slopen methods are limited by analyzing undecomposed ship-radiated noise signals (S-NSs).

Purpose of the Study:

To improve the recognition ability of Slopen for S-NSs.
To propose optimized feature extraction approaches using CEEMDAN and Slopen.

Main Methods:

Combined Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) with Slopen.
Developed single-intrinsic mode function (IMF) and dual-IMF optimized feature extraction methods.
Conducted comparative experiments to validate the proposed approaches.

Main Results:

CEEMDAN effectively decomposed S-NSs into IMFs.
The proposed single-IMF and dual-IMF optimized approaches demonstrated superior performance.
Optimized methods significantly outperformed traditional S-NS feature extraction techniques.

Conclusions:

CEEMDAN-Slopen integration offers enhanced S-NS feature extraction.
Optimized single- and dual-IMF approaches provide a more effective solution for S-NS analysis.
The study validates the superiority of the proposed methods in improving recognition accuracy.