A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum
Due to the short duration and complexity of ship shock responses, the shock response spectrum(SRS) is commonly used as a tool for analyzing these responses. To address the conflict between calculation speed and accuracy inherent in traditional SRS solving methods, this paper proposed a deep learning...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Science Press (China)
2025-06-01
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| Series: | 水下无人系统学报 |
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| Online Access: | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0144 |
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| author | Shuang WANG Feng LÜ Feng MA Si CHEN Wei ZHU Feng HAN Qinyi HUANG |
| author_facet | Shuang WANG Feng LÜ Feng MA Si CHEN Wei ZHU Feng HAN Qinyi HUANG |
| author_sort | Shuang WANG |
| collection | DOAJ |
| description | Due to the short duration and complexity of ship shock responses, the shock response spectrum(SRS) is commonly used as a tool for analyzing these responses. To address the conflict between calculation speed and accuracy inherent in traditional SRS solving methods, this paper proposed a deep learning-based fast solver for the SRS. An adaptive threshold selection mechanism tailored to the characteristics of the SRS was designed to improve the solver’s calculation accuracy. A comparison between the SRS obtained by the proposed solver and the results calculated using traditional methods demonstrated a high degree of consistency, validating the effectiveness of the solver. Additionally, L2 regularization was introduced in the solution process, effectively preventing overfitting and further enhancing the robustness of the solver. |
| format | Article |
| id | doaj-art-a72c91e4ba454c4b9acda75eebfa8b13 |
| institution | Kabale University |
| issn | 2096-3920 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Science Press (China) |
| record_format | Article |
| series | 水下无人系统学报 |
| spelling | doaj-art-a72c91e4ba454c4b9acda75eebfa8b132025-08-20T03:29:47ZzhoScience Press (China)水下无人系统学报2096-39202025-06-0133354555110.11993/j.issn.2096-3920.2024-01442024-0144A Deep Learning-Based Solver for Underwater Explosion Shock Response SpectrumShuang WANG0Feng LÜ1Feng MA2Si CHEN3Wei ZHU4Feng HAN5Qinyi HUANG6State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaUnit 32398th, the Liberation Army of China, Beijing 100026, ChinaState Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaState Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaDue to the short duration and complexity of ship shock responses, the shock response spectrum(SRS) is commonly used as a tool for analyzing these responses. To address the conflict between calculation speed and accuracy inherent in traditional SRS solving methods, this paper proposed a deep learning-based fast solver for the SRS. An adaptive threshold selection mechanism tailored to the characteristics of the SRS was designed to improve the solver’s calculation accuracy. A comparison between the SRS obtained by the proposed solver and the results calculated using traditional methods demonstrated a high degree of consistency, validating the effectiveness of the solver. Additionally, L2 regularization was introduced in the solution process, effectively preventing overfitting and further enhancing the robustness of the solver.https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0144underwater explosionshock response spectrumdeep learning |
| spellingShingle | Shuang WANG Feng LÜ Feng MA Si CHEN Wei ZHU Feng HAN Qinyi HUANG A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum 水下无人系统学报 underwater explosion shock response spectrum deep learning |
| title | A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum |
| title_full | A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum |
| title_fullStr | A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum |
| title_full_unstemmed | A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum |
| title_short | A Deep Learning-Based Solver for Underwater Explosion Shock Response Spectrum |
| title_sort | deep learning based solver for underwater explosion shock response spectrum |
| topic | underwater explosion shock response spectrum deep learning |
| url | https://sxwrxtxb.xml-journal.net/cn/article/doi/10.11993/j.issn.2096-3920.2024-0144 |
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