Multipoint Heave Motion Prediction Method for Ships Based on the PSO-TGCN Model

Author:

Ding Shi-feng,Ma Qun,Zhou Li,Han Sen,Dong Wen-bo

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Ocean Engineering,Renewable Energy, Sustainability and the Environment,Oceanography

Reference27 articles.

1. Akita, R., Yoshihara, A., Matsubara, T. and Uehara, U., 2016. Deep learning for stock prediction using numerical and textual information, Proceedings of IEEE/ACIS 15th International Conference on Computer and Information Science, IEEE, Okayama, Japan, pp. 1–6.

2. Bian, D.J., Qin, S.Q. and Wu, W., 2016. A hybrid AR-DWT-EMD model for the short-term prediction of nonlinear and non-stationary ship motion, Proceedings of 2016 Chinese Control and Decision Conference, IEEE, Yinchuan, China, pp. 4042–4047.

3. Chen, Y.Y., Lv, Y.S., Li, Z.J. and Wang, F.Y., 2016. Long short-term memory model for traffic congestion prediction with online open data, Proceedings of IEEE 19th International Conference on Intelligent Transportation Systems, IEEE, Rio de Janeiro, Brazil, pp. 132–137.

4. Gao, N., Hu, A.K., Hou, L.X. and Chang, X., 2023. Real-time ship motion prediction based on adaptive wavelet transform and dynamic neural network, Ocean Engineering, 280, 114466.

5. Ge, L., Li, H., Liu, J.L. and Zhou, A.L., 2019. Traffic speed prediction with missing data based on TGCN, Proceedings of 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, Leicester, UK, pp. 522–529.

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