Yuzhe Zhuang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yuzhe Zhuang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Caiquan Xiong
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
1 shared publication - Jiaming Li
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
1 shared publication - Mao Luo
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
1 shared publication - Qi Wang
Key Laboratory of Quantitative Remote Sensing Information Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
1 shared publication - Xinyun Wu
School of Computer Science, Hubei University of Technology, Wuhan, China
1 shared publication
Latest Publications
- Open Access
- 8 November 2025
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 13 (11) , 2119
MKAIS: A Hybrid Mamba–KAN Neural Network for Vessel Trajectory Prediction
Vessel trajectory prediction (VTP) plays a critical role in maritime safety and intelligent navigation. Existing methods struggle to simultaneously capture long-term dependencies and nonlinear dynamic patterns in vessel movements. To address this challenge, we propose MKAIS, a novel trajectory prediction model that integrates the selective state space modeling capability of Mamba with the strong nonlinear representation power of Kolmogorov–Arnold Networks (KAN). Specifically, we design a feature-separated embedding strategy for AIS inputs (longitude, latitude, speed over ground, course over ground), followed by an MKAN module that jointly models global temporal dependencies and nonlinear dynamics. Experiments on the public ct_dma dataset demonstrate that MKAIS outperforms state-of-the-art baselines (LSTM, Transformer, TrAISformer, Mamba), achieving up to 16.65% improvement in the Haversine distance over 3 h prediction horizons. These results highlight the effectiveness and robustness of MKAIS for both short-term and long-term vessel trajectory prediction.
- 8 November 2025
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 13 (11) , 2119
Open AccessMKAIS: A Hybrid Mamba–KAN Neural Network for Vessel Trajectory Prediction
Vessel trajectory prediction (VTP) plays a critical role in maritime safety and intelligent navigation. Existing methods struggle to simultaneously capture long-term dependencies and nonlinear dynamic patterns in vessel movements. To address this challenge, we propose MKAIS, a novel trajectory prediction model that integrates the selective state space modeling capability of Mamba with the strong nonlinear representation power of Kolmogorov–Arnold Networks (KAN). Specifically, we design a feature-separated embedding strategy for AIS inputs (longitude, latitude, speed over ground, course over ground), followed by an MKAN module that jointly models global temporal dependencies and nonlinear dynamics. Experiments on the public ct_dma dataset demonstrate that MKAIS outperforms state-of-the-art baselines (LSTM, Transformer, TrAISformer, Mamba), achieving up to 16.65% improvement in the Haversine distance over 3 h prediction horizons. These results highlight the effectiveness and robustness of MKAIS for both short-term and long-term vessel trajectory prediction.