Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network

• Published in: Sensors
• Main Authors: Yuxing Li, Zhaoyu Gu, Xiumei Fan
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: sea state signal; slope entropy; beluga whale optimization; one-dimensional convolutional neural network; feature extraction; Chemical technology; TP1-1185; Beluga; Beluga whale; Beluga*
• Online Access: https://doi.org/10.3390/s24051680; https://doaj.org/article/fff908b5aef64284a0562d74fa9b12b4

This study introduces a novel nonlinear dynamic analysis method, known as beluga whale optimization–slope entropy (BWO-SlEn), to address the challenge of recognizing sea state signals (SSSs) in complex marine environments. A method of underwater acoustic signal recognition based on BWO-SlEn and one-dimensional convolutional neural network (1D-CNN) is proposed. Firstly, particle swarm optimization–slope entropy (PSO-SlEn), BWO-SlEn, and Harris hawk optimization–slope entropy (HHO-SlEn) were used for feature extraction of noise signal and SSS. After 1D-CNN classification, BWO-SlEn were found to have the best recognition effect. Secondly, fuzzy entropy (FE), sample entropy (SE), permutation entropy (PE), and dispersion entropy (DE) were used to extract the signal features. After 1D-CNN classification, BWO-SlEn and 1D-CNN were found to have the highest recognition rate compared with them. Finally, compared with the other six recognition methods, the recognition rates of BWO-SlEn and 1D-CNN for the noise signal and SSS are at least 6% and 4.75% higher, respectively. Therefore, the BWO-SlEn and 1D-CNN recognition methods proposed in this paper are more effective in the application of SSS recognition.