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Research on Wave Period Level Detection Based on 3D Convolutional Network

Published: 18 August 2021 Publication History

Abstract

Wave observation is very important in the construction of ocean engineering. However, current research only advocates the use of two-dimensional convolutional networks to extract the spatial information of ocean waves, without considering the benefits of the time information of ocean wave motion. Therefore, in this paper, we propose a new network structure model (Wave-3Dcnn) based on a three-dimensional convolutional network, which can simultaneously learn the spatial and time information of the waves, and realize the research on the detection of wave levels. The simulation experiment verifies that the model proposed in this paper can obtain the best detection with 96.8% classification accuracy, and has a great advantage in real-time with a small amount of calculation.

References

[1]
D. Z. Feng, S. F. Xia, Z. M. Zhang. “Optical detection and information processing of ocean wave spectrum” [J]. Applied laser, 1991 (05): 193-197.
[2]
B. X. Shi, H. M. Yan, C. F. Li, L. Zhang. “Design of optical measurement device for sea surface microstructure” [J]. Optoelectronic engineering, 2001, 28 (003): 21-24.
[3]
F. Y. Wang, G. N. Yuan, Y. L. Hao, Z. Z. Lu. “Research progress of ocean wave measurement based on radar at home and abroad” [C] / / 5th National Conference on information acquisition and processing. 2007.
[4]
Z. W. Li, A. S. Hu, X. F. Wang. “Overview of vision based target detection methods” [J]. Computer engineering and applications, 2020 (8): 1-9.
[5]
A. S. Mironov, V. A. Dulov. “Detection of wave breaking using sea surface video records”[J]. Measurement ence & Technology, 2008, 19(1):015405.
[6]
G. Li, Y. Y. Xiong, K. K. Liu, J. H.Wang. “A wave detection method based on image texture features” [J]. Computer application research, 2013, 30 (004): 1226-1229.
[7]
Z. S. Zheng, J. B. Hao, D. M. Huang, G. L.Zou. “Nearshore wave grade video monitoring based on deep learning”[J]. Marine environmental science, 36 (6).
[8]
W. Song, X. Zhou, F. Bi, D. L. Guo, S. Gao, Q. He, “Automatic detection of wave height in inshore waves by video” [J]. Chinese Journal of Image and Graphics, 2020, V.25; no.287 (03): 93-105.
[9]
X. A. Liu, T. Peng. “Chinese scenic spot recognition based on convolutional neural network” [J]. Computer engineering and applications, 2020, v.56; no.947 (04): 145-150.
[10]
K. Fukushima. Neocognitron: “A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.” Biol. Cybernetics 36, 193–202 (1980). https://doi.org/10.1007/BF00344251
[11]
S. Ji, W. Xu, M. Yang, K. Yu. “3D Convolutional Neural Networks for Human Action Recognition”[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(1):221-231.
[12]
A. Waibel, T. Hanazawa, G. E. Hinton, K. Shikano, K. J. Lang “Phoneme recognition using time-delay neural networks[J]. Readings in Speech Recognition”, 1990, 1(3):393-404.
[13]
J. Y. Zhang, H. L. Wang, Y. Guo, X. Hu. “Review of deep learning research” [J]. Computer application research, 2018, 035 (007): 1921-1928,1936.
[14]
D. Kingma, J. Ba. “Adam: A Method for Stochastic Optimization” [J]. Computer Science, 2014.
[15]
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskeve, R Salakhutdinov. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting” [J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.

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  • (2024)Comparison of model selection and data bias on the prediction performance of purpleback flying squid (Sthenoteuthis oualaniensis) fishing ground in the Northwest Indian OceanEcological Indicators10.1016/j.ecolind.2023.111526158(111526)Online publication date: Jan-2024

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cover image ACM Other conferences
ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems
May 2021
2053 pages
ISBN:9781450390200
DOI:10.1145/3469213
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 18 August 2021

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  • (2024)Comparison of model selection and data bias on the prediction performance of purpleback flying squid (Sthenoteuthis oualaniensis) fishing ground in the Northwest Indian OceanEcological Indicators10.1016/j.ecolind.2023.111526158(111526)Online publication date: Jan-2024

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