洪维强
个人信息
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教授 博士生导师
教师英文名称：Hong Wei-Chiang
教师拼音名称：hwq
所在单位：船舶工程学院
职务：Professor
性别：男
学位：管理学博士学位
在职信息：在职
主要任职：Doctoral supervisor
其他任职：Associate Editor for Applied Soft Computing
毕业院校：Da Yeh University
学科：船舶与海洋结构物设计制造曾获荣誉
2026 全球 2% 科学家(年度与终身)
2025 全球 2% 科学家(年度与终身)
2024 全球 2% 科学家(年度与终身)
2023 全球 2% 科学家(年度与终身)
2022 全球 2% 科学家(年度与终身)
2021 全球 2% 科学家(年度与终身)
2020 全球 2% 科学家(年度与终身)
2019 全球 2% 科学家(年度与终身)
2026 ScholarGPS®全球前 0.05%预测专业学者
2025 ScholarGPS®全球前 0.05%预测专业学者
2024 ScholarGPS®全球前 0.05%预测专业学者
2023 ScholarGPS®全球前 0.05%预测专业学者
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2026 GlobalAuthorID.com全球前十万科学家
2023 GlobalAuthorID.com全球前十万科学家
2023 第21届徐有庠基金会杰出教授奖
2014 第12届徐有庠基金会杰出教授奖
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影响因子：6.3
DOI码：10.1016/j.oceaneng.2024.119560
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Ocean Engineering
刊物所在地：英国
项目来源：The work is supported by the following project grants, Science and Technology special fund of Hainan
关键字：Ship motion forecast; Deep learning model; Fourier transform; Bi-LSTM; Whale optimization algorithm
摘要：Ship motion forecasting is of great significance to assist ship navigation and ensure ship operation. However, due to the coupling influence of wind, waves, currents and other factors, the ship motion has a strong time variation, which brings great challenges to the improvement of the ship motion forecasting accuracy. This paper proposes a novel approach for ship motion forecasting. Firstly, Fourier Transform (FT) is used for data pre-processing to deal with the data noise. Secondly, the Bi-LSTM is applied to simulate the nonlinear dynamical system of ship motion, and the Dropout mechanism and l2 regularization method are used to improve the generalization performance of the network, and a novel ship motion prediction model (FRB model) is constructed. Thirdly, in view of the inherent defects of Whale Optimization Algorithm (WOA), based on chaotic mapping and quantum computing, a new Chaotic Quantum Adaptive Whale Optimization Algorithm (CQAWOA) is proposed to construct a new ship motion forecasting approach, namely FRB&CQAWOA. Finally, based on the measured pitch and roll data of ship, the feasibility and superiority of the established FRB model and the proposed CQAWOA are tested. The test results indicate that the proposed model have better prediction accuracy than other algorithms selected in this paper.
备注：The work is supported by the following project grants, Science and Technology special fund of Hainan Province (ZDYF2023GXJS017); National Natural Science Foundation of China (No.52371315); Heilongjiang Excellent Youth Fund Project (YQ2021E015); and National Science and Technology Council, Taiwan (NSTC 113-2410-H-161-003).
合写作者：徐瑞喆,耿敬,He Li
第一作者：李明伟
论文类型：期刊论文
通讯作者：洪维强
论文编号：119560
学科门类：工学
文献类型：J
卷号：313
ISSN号：0029-8018
是否译文：否
发表时间：2024-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0029801824028981?via%3Dihub