Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network
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-...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1680 |
| _version_ | 1797263852376686592 |
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| author | Yuxing Li Zhaoyu Gu Xiumei Fan |
| author_facet | Yuxing Li Zhaoyu Gu Xiumei Fan |
| author_sort | Yuxing Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-25T00:19:35Z |
| format | Article |
| id | doaj.art-fff908b5aef64284a0562d74fa9b12b4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:19:35Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fff908b5aef64284a0562d74fa9b12b42024-03-12T16:55:37ZengMDPI AGSensors1424-82202024-03-01245168010.3390/s24051680Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural NetworkYuxing Li0Zhaoyu Gu1Xiumei Fan2School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThis 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.https://www.mdpi.com/1424-8220/24/5/1680sea state signalslope entropybeluga whale optimizationone-dimensional convolutional neural networkfeature extraction |
| spellingShingle | Yuxing Li Zhaoyu Gu Xiumei Fan Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network Sensors sea state signal slope entropy beluga whale optimization one-dimensional convolutional neural network feature extraction |
| title | Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network |
| title_full | Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network |
| title_fullStr | Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network |
| title_full_unstemmed | Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network |
| title_short | Research on Sea State Signal Recognition Based on Beluga Whale Optimization–Slope Entropy and One Dimensional–Convolutional Neural Network |
| title_sort | research on sea state signal recognition based on beluga whale optimization slope entropy and one dimensional convolutional neural network |
| topic | sea state signal slope entropy beluga whale optimization one-dimensional convolutional neural network feature extraction |
| url | https://www.mdpi.com/1424-8220/24/5/1680 |
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