본문 바로가기
KERD
논문

Underwater Acoustic Research Trends with Machine Learning: General Background

2

조회

0

다운로드

7

인용

0

참고문헌

키워드

주제분류해양공학

초록

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.

Abstract

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.

참고문헌 (31)

  1. Allen, N., "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), pp.1287~1298 (2011)[DOI]
  2. "The Evolution and Core Concepts of Deep Learning & Neural Networks" (2016)[URL]
  3. Bishop, C. M., "Pattern Recognition and Machine Learning" (2006), Springer
  4. Buscombe, D., "Probabilistic Substrate Classification with Multispectral Acoustic Backscatter : A Comparison of Discriminative and Generative Models", Geoscience, 8(11), pp.395~ (2018)[DOI]
  5. Choi, J., "Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning", Sensors, 19(16), pp.3492~ (2019)[DOI]
  6. Das, A., "Theoretical and Experimental Comparison of Off-grid Sparse Bayesian Direction-of-arrival Estimation Algorithms", IEEE Access, 5, pp.18075~18087 (2017)[DOI]
  7. Das, A., "Narrowband and Wideband Off-grid Direction-of-arrival Estimation via Sparse Bayesian Learning", IEEE Journal of Oceanic Engineering, 43(1), pp.108~118 (2017)[DOI]
  8. Dempster, A. P., "Maximum Likelihood from Incomplete Data via the EM Algorithm", Journal of the Royal Statistical Society, B, pp.1~38 (1977)[DOI]
  9. Diesing, M., "Mapping Seabed Sediment : Comparison of Manual, Geostatistical, Object-based Image Analysis and Machine Learning Approaches", Continental Shelf Research, 84, pp.107~119 (2014)[DOI]
  10. Elad, M., "Sparse and Redundant Representations" (2010), Springer
  11. Fischell, E. M., "Classification of Underwater Targets from Autonomous Underwater Vehicle Sampled Bistatic Acoustic Scattered Fields", The Journal of the Acoustical Society of America, 138(6), pp.3773~3784 (2015)[DOI]
  12. Fukushima, K., "Neocognition : A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position", Biological Cybernetics, 36(4), pp.193~202 (1980)[DOI]
  13. Gemba, K. L., "Robust Ocean Acoustic Localization with Sparse Bayesian Learning", IEEE Journal of Selected Topics in Signal Processing, 13(1), pp.49~60 (2019)[DOI]
  14. Gerstoft, P., "Multisnapshot Sparse Bayesian Learning for DOA", IEEE Signal Processing Letters, 23(10), pp.1469~1473 (2016)[DOI]
  15. Goodfellow, I., "Deep Learning" (2016), MIT Press
  16. Hastie, T., "The Elements of Statistical Learning: Data Mining, Inference and Prediction" (2009), Springer
  17. Ke, X., "Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm", Sensors, 18(12), pp.4318~ (2018)[DOI]
  18. Kohavi, R., "A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model-selection", Proceedings of the International Joint Conference on Artificial Intelligence, 14(2), pp.1137~1145 (1995)
  19. "Support Vector Machines (SVM)" (2018)[URL]
  20. LeCun, Y., "Gradient-Based Learning Applied to Document Recognition", Proceedings of the IEEE, 86(11), pp.2278~2324 (1998)[DOI]
  21. MacQueen, J, "Some methods for classification and analysis of multivariate observations", Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1(14), pp.281~297 (1967)
  22. McLachlan, G. J., "Finite Mixture Models", Annual Review of Statistics and Its Application, 6, pp.355~378 (2019)[DOI]
  23. Murphy, K., "Machine Learning: a Probabilistic Perspective" (2012), MIT Press
  24. Murphy, S. M., "Examining the Robustness of Automated Aural Classification of Active Sonar Echoes", The Journal of the Acoustical Society of America, 135(2), pp.626~636 (2014)[DOI]
  25. Nannuru, S., "Sparse Bayesian Learning with Multiple Dictionaries", Signal Processing, 159, pp.159~170 (2019)[DOI]
  26. Rumelhart, D. E., "Learning Representations by Back-propagating Errors", Nature, 323, pp.533~536 (1986)[DOI]
  27. Tosic, I., "Dictionary Learning", IEEE Signal Process. Magazine, 28(2), pp.27~38 (2011)[DOI]
  28. "A Comprehensive Guide to Convolutional Neural Networks - the ELI5 Way" (2018)[URL]
  29. Wang, X., "Underwater Acoustic Target Recognition : A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network", Remote Sensing, 11(16), pp.1888~ (2019)[DOI]
  30. Wipf, D. P., "Sparse Bayesian Learning for Basis Selection", IEEE Transactions on Signal Processing, 52(8), pp.2153~2164 (2004)[DOI]
  31. Young, V. W., "Perception-based Automatic Classification of Impulsive-source Active Sonar Echoes", The Journal of the Acoustical Society of America, 122(3), pp.1502~1517 (2007)[DOI]

관련 논문AI추천

이 간행물의 다른 논문 (9건)