Data-Driven Diffusion-Augmented Network for Imbalanced Sea State Estimation with Dynamic Prototypes

With the advent of Industry 4.0, which emphasizes automation and data-driven decision-making, sea state estimation (SSE) has become a critical component in marine engineering and autonomous vessels. However, traditional SSE methods face significant challenges due to poor real-time performance and high manual costs. Additionally, existing deep learning (DL) approaches struggle to handle imbalanced sea state data effectively, which hinders their generalization ability. To address these issues, this paper proposes a novel DL-based SSE model. The model enhances the expressive capacity of the extracted features through a feature augmentation module and utilizes a diffusion model to extract more robust features from the data. Furthermore, a dynamic prototype update module is designed to effectively address class imbalance and overcome the limitations of traditional prototype classifiers in handling boundary samples. Experimental results show that the proposed method outperforms existing baseline models on two imbalanced sea state datasets, achieving F1-score improvements of 4.5% and 3.5%, respectively, compared to the current state-of-the-art methods. Additionally, the model demonstrates superior performance on several publicly available multivariate time series classification datasets.