Data-Driven Modeling for Transferable Sea State Estimation Between Marine Systems

Abstract

Sea state estimation is beneficial for marine systems to enhance on-board decision-making and improve work efficiency. In the era of ship intelligence, artificial intelligence has greatly promoted the technology of sensing environment, such as by using the deep learning. However, it is difficult to collect enough motion data from a marine system to train a deep learning model. In addition, the model for sea state estimation is trained using the data from a specific marine system; applying the model directly to another marine system may result in performance degradation. In this paper, a supervised transfer learning based framework for sea state estimation (STLSSE) is proposed. The STLSSE focuses on knowledge transfer when the collected data for the source marine system is sufficient but the collected data of the target marine system is scarce. In STLSSE, a data pairing algorithm is proposed to determine the relationship of the source and the target marine system. Based on these paired data, a Siamese convolutional neural network, including a new proposed residual fully convolutional network and two novel attention modules, is designed for the semantic alignment. Moreover, the conventional contrastive loss is improved to characterize the distributions when there are only few samples in the target marine system. The extensive comparisons between STLSSE and state-of-the-art transfer learning approaches show its superior performance. The comparisons with state-of-the-art attention modules has verified the competitiveness of the proposed attention modules. The key parameters and each component of STLSSE are emphasized in the ablation and sensitivity studies.