Underwater Acoustic Research Trends with Machine Learning: General Background
| Autor: | Haesang Yang, Keunhwa Lee, Youngmin Choo, Kookhyun Kim |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | 한국해양공학회지, Vol 34, Iss 2, Pp 147-154 (2020) |
| Druh dokumentu: | article |
| ISSN: | 1225-0767 2287-6715 |
| DOI: | 10.26748/KSOE.2020.015 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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