지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.4
- 수록면
- 147 - 154 (8page)
이용수
초록· 키워드
오류제보하기
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 background of several related machine learning techniques is introduced in this paper.
목차
ABSTRACT
1. Introduction
2. Machine Learning Theory
3. Conclusions
References
참고문헌 (31)
참고문헌 신청
Allen, N., Hines, P.C., & Young, V.W. (2011). Performances of Human Listeners and an Automatic Aural Classifier in Discriminating between Sonar Target Echoes and Clutter. The Journal of the Acoustical Society of America, 130(3), 1287-1298. https://doi.org/10.1121/1.3614549
Guest Blog. (2016). The Evolution and Core Concepts of Deep Learning & Neural Networks. Analytics Vidhya. Retrieved 06 February 2020 from https://www.analyticsvidhya.com/blog/2016/08/evolution-core-concepts-deep-learning-neural-networks
Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer.
Buscombe, D., & Grams, P.E. (2018). Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models. Geoscience, 8(11), 395. https://doi.org/10.3390/geosciences8110395
Choi, J., Choo, Y., & Lee, K. (2019). Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning. Sensors, 19(16), 3492. https://doi.org/10.3390/s19163492
Guest Blog. (2016). The Evolution and Core Concepts of Deep Learning & Neural Networks. Analytics Vidhya. Retrieved 06 February 2020 from https://www.analyticsvidhya.com/blog/2016/08/evolution-core-concepts-deep-learning-neural-networks
Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer.
Buscombe, D., & Grams, P.E. (2018). Probabilistic Substrate Classification with Multispectral Acoustic Backscatter: A Comparison of Discriminative and Generative Models. Geoscience, 8(11), 395. https://doi.org/10.3390/geosciences8110395
Choi, J., Choo, Y., & Lee, K. (2019). Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning. Sensors, 19(16), 3492. https://doi.org/10.3390/s19163492
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