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| Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications |
| Haesang Yang, Sung-Hoon Byun, Keunhwa Lee, Youngmin Choo, Kookhyun Kim |
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J. Ocean Eng. Technol.. 2020;34(4):277-284. Published online July 31, 2020 DOI: https://doi.org/10.26748/KSOE.2020.018 |
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Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications Journal of Ocean Engineering and Technology. 2020;34(4):277-284  Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications Journal of Ocean Engineering and Technology. 2020;34(3):227-236 Active Sonar Target Channel Underwater Acoustic Channel. 2022;193-202 Underwater Acoustic Research Trends with Machine Learning: Ocean Parameter Inversion Applications Journal of Ocean Engineering and Technology. 2020;34(5):371-376 Seafloor Habitat Mapping Using Machine Learning and Underwater Acoustic Sonar Computational Intelligence in Machine Learning. 2022;281-287 Sonar Engineering and Applications Underwater Acoustic Systems. 1990;153-170 Similar Object Identification in Sonar Images by Machine Learning 2023 IEEE Underwater Technology (UT). 2023; Underwater Acoustic Research Trends with Machine Learning: General Background Journal of Ocean Engineering and Technology. 2020;34(2):147-154 Evolving Machine Learning-Based Classifiers by Metaheuristic Approach for Underwater Sonar Target Detection and Recognition Computational Intelligence Methods and Applications. 2022;135-155 Active Acoustic Signature Extraction by Machine Learning Data Analysis for Underwater Fish Counters Global Oceans 2020: Singapore – U.S. Gulf Coast. 2020; |
