An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating
The detection and optimization of ocean environmental noise anomalies play a crucial role in enhancing the safety of marine engineering applications and ecological protection. Current anomaly detection methods for ocean environmental noise often suffer from issues of accuracy and robustness. To addr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/141 |
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| author | Libin Du Mingyang Liu Zhichao Lv Chuanhe Tan Junkai He Fei Yu |
| author_facet | Libin Du Mingyang Liu Zhichao Lv Chuanhe Tan Junkai He Fei Yu |
| author_sort | Libin Du |
| collection | DOAJ |
| description | The detection and optimization of ocean environmental noise anomalies play a crucial role in enhancing the safety of marine engineering applications and ecological protection. Current anomaly detection methods for ocean environmental noise often suffer from issues of accuracy and robustness. To address these challenges, this paper first proposes an end-to-end framework that combines time–frequency information and expert gating, significantly improving the precision of noise sequence generation. Secondly, a Gamma distribution-based residual analysis method for anomaly detection is designed, enhancing the robustness of anomaly detection. Finally, an anomaly optimization module is developed to improve data quality, enabling efficient noise anomaly detection and optimization. Our experimental results demonstrate that the proposed model significantly outperforms traditional models in multi-frequency noise prediction, with strong robustness in anomaly detection and high generalization performance. The proposed framework offers a novel approach for analyzing the causes of noise anomalies and optimizing models. Additionally, the research outcomes provide efficient technical support for deep-sea exploration, equipment optimization, and environmental protection. |
| format | Article |
| id | doaj-art-1cb57850d9aa4bb2b1e4b9b6c26d5abc |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-1cb57850d9aa4bb2b1e4b9b6c26d5abc2025-01-24T13:37:00ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113114110.3390/jmse13010141An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert GatingLibin Du0Mingyang Liu1Zhichao Lv2Chuanhe Tan3Junkai He4Fei Yu5College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaThe detection and optimization of ocean environmental noise anomalies play a crucial role in enhancing the safety of marine engineering applications and ecological protection. Current anomaly detection methods for ocean environmental noise often suffer from issues of accuracy and robustness. To address these challenges, this paper first proposes an end-to-end framework that combines time–frequency information and expert gating, significantly improving the precision of noise sequence generation. Secondly, a Gamma distribution-based residual analysis method for anomaly detection is designed, enhancing the robustness of anomaly detection. Finally, an anomaly optimization module is developed to improve data quality, enabling efficient noise anomaly detection and optimization. Our experimental results demonstrate that the proposed model significantly outperforms traditional models in multi-frequency noise prediction, with strong robustness in anomaly detection and high generalization performance. The proposed framework offers a novel approach for analyzing the causes of noise anomalies and optimizing models. Additionally, the research outcomes provide efficient technical support for deep-sea exploration, equipment optimization, and environmental protection.https://www.mdpi.com/2077-1312/13/1/141anomaly detectionocean environmental noise predictionLSTMexpert gating |
| spellingShingle | Libin Du Mingyang Liu Zhichao Lv Chuanhe Tan Junkai He Fei Yu An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating Journal of Marine Science and Engineering anomaly detection ocean environmental noise prediction LSTM expert gating |
| title | An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating |
| title_full | An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating |
| title_fullStr | An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating |
| title_full_unstemmed | An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating |
| title_short | An End-to-End Ocean Environmental Noise Anomaly Detection Framework Combining Time–Frequency Information and Expert Gating |
| title_sort | end to end ocean environmental noise anomaly detection framework combining time frequency information and expert gating |
| topic | anomaly detection ocean environmental noise prediction LSTM expert gating |
| url | https://www.mdpi.com/2077-1312/13/1/141 |
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