Underwater Acoustic Research Trends with Machine Learning: General Background
Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientifi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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The Korean Society of Ocean Engineers
2020-04-01
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| Series: | 한국해양공학회지 |
| Subjects: | |
| Online Access: | https://doi.org/10.26748/KSOE.2020.015 |
| _version_ | 1818807977522495488 |
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| author | Haesang Yang Keunhwa Lee Youngmin Choo Kookhyun Kim |
| author_facet | Haesang Yang Keunhwa Lee Youngmin Choo Kookhyun Kim |
| author_sort | Haesang Yang |
| collection | DOAJ |
| description | Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical backgroundof several related machine learning techniques is introduced in this paper. |
| first_indexed | 2024-12-18T19:34:14Z |
| format | Article |
| id | doaj.art-f4912ef5a2ca417bad002b06bf2e379e |
| institution | Directory Open Access Journal |
| issn | 1225-0767 2287-6715 |
| language | English |
| last_indexed | 2024-12-18T19:34:14Z |
| publishDate | 2020-04-01 |
| publisher | The Korean Society of Ocean Engineers |
| record_format | Article |
| series | 한국해양공학회지 |
| spelling | doaj.art-f4912ef5a2ca417bad002b06bf2e379e2022-12-21T20:55:40ZengThe Korean Society of Ocean Engineers한국해양공학회지1225-07672287-67152020-04-0134214715410.26748/KSOE.2020.015Underwater Acoustic Research Trends with Machine Learning: General BackgroundHaesang Yang0https://orcid.org/0000-0001-7101-5195Keunhwa Lee1https://orcid.org/0000-0003-4827-3983Youngmin Choo2https://orcid.org/0000-0002-9100-9494Kookhyun Kim3https://orcid.org/0000-0002-4214-4673 Seoul National UniversitySejong UniversitySejong UniversityTongmyong UniversityUnderwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical backgroundof several related machine learning techniques is introduced in this paper.https://doi.org/10.26748/KSOE.2020.015underwater acousticssonar systemmachine learningdeep learningsignal processingprobabilistic model |
| spellingShingle | Haesang Yang Keunhwa Lee Youngmin Choo Kookhyun Kim Underwater Acoustic Research Trends with Machine Learning: General Background 한국해양공학회지 underwater acoustics sonar system machine learning deep learning signal processing probabilistic model |
| title | Underwater Acoustic Research Trends with Machine Learning: General Background |
| title_full | Underwater Acoustic Research Trends with Machine Learning: General Background |
| title_fullStr | Underwater Acoustic Research Trends with Machine Learning: General Background |
| title_full_unstemmed | Underwater Acoustic Research Trends with Machine Learning: General Background |
| title_short | Underwater Acoustic Research Trends with Machine Learning: General Background |
| title_sort | underwater acoustic research trends with machine learning general background |
| topic | underwater acoustics sonar system machine learning deep learning signal processing probabilistic model |
| url | https://doi.org/10.26748/KSOE.2020.015 |
| work_keys_str_mv | AT haesangyang underwateracousticresearchtrendswithmachinelearninggeneralbackground AT keunhwalee underwateracousticresearchtrendswithmachinelearninggeneralbackground AT youngminchoo underwateracousticresearchtrendswithmachinelearninggeneralbackground AT kookhyunkim underwateracousticresearchtrendswithmachinelearninggeneralbackground |