Li Yuxing, Gu Zhaoyu, Fan Xiumei
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel). 2024 Mar 5;24(5):1680. doi: 10.3390/s24051680.
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.
本研究引入了一种新颖的非线性动力学分析方法，即白鲸优化-斜率熵（BWO-SlEn），以应对在复杂海洋环境中识别海况信号（SSS）的挑战。提出了一种基于BWO-SlEn和一维卷积神经网络（1D-CNN）的水下声学信号识别方法。首先，利用粒子群优化-斜率熵（PSO-SlEn）、BWO-SlEn和哈里斯鹰优化-斜率熵（HHO-SlEn）对噪声信号和海况信号进行特征提取。经过1D-CNN分类后，发现BWO-SlEn具有最佳的识别效果。其次，利用模糊熵（FE）、样本熵（SE）、排列熵（PE）和离散熵（DE）提取信号特征。经过1D-CNN分类后，发现BWO-SlEn和1D-CNN与它们相比具有最高的识别率。最后，与其他六种识别方法相比，BWO-SlEn和1D-CNN对噪声信号和海况信号的识别率分别至少高出6%和4.75%。因此，本文提出的BWO-SlEn和1D-CNN识别方法在海况信号识别应用中更有效。
