Chinese Journal of Ship Research, Volume. 19, Issue 6, 303(2024)

Ship track prediction based on Bayesian optimization in temporal convolutional networks

Jinyuan LI1, Faxin ZHU1, Xianbin TENG2, and Qilin BI2
Author Affiliations
  • 1School of Ship and Marine, Zhejiang Ocean University, Zhoushan 316022, China
  • 2College of Marine Engineering, Guangzhou Maritime College, Guangzhou 510725, China
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    References(12)

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    [11] [11] BAI S J, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional recurrent wks f sequence modeling[JOL]. arXiv preprint arXiv (2018034)[ 20240323]. https:arxiv.gabs1803.01271.

    [17] [17] HU C J, ZHAO Y, JIANG H, et al. Prediction of ultrashtterm wind power based on CEEMDANLSTMTCN[J]. Energy Repts, 2022, 8 Suppl 8: 483−492.

    [19] [19] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning f image recognition[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition. Las Vegas: IEEE, 2016: 770−778.

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    [21] [21] NOAA. AISDataHler[DBOL]. NOAA.(20200908)[20240124]. https:coast.noaa.govhtdataCMSPAISDataHler2020index.html.

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    Jinyuan LI, Faxin ZHU, Xianbin TENG, Qilin BI. Ship track prediction based on Bayesian optimization in temporal convolutional networks[J]. Chinese Journal of Ship Research, 2024, 19(6): 303

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    Paper Information

    Category: Weapon, Electronic and Information System

    Received: Jan. 24, 2024

    Accepted: --

    Published Online: Mar. 14, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03755

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