针对船舶运动数据不平衡问题，提出动态图对比聚类模型提升海况估计精度。
Dynamic Graph-Like Learning with Contrastive Clustering on Temporally-Factored Ship Motion Data for Imbalanced Sea State Estimation in Autonomous Vessel
- 将时间维度解耦降冗余，构建动态图捕捉变量复杂交互
- 在14个数据集上9次最优，较EDI提升20.79%准确率
- 适合自动驾驶船舶实时海况判断与多场景泛化应用
精准的海况估计对自主船舶的实时控制与未来状态预测至关重要。然而，传统方法面临数据不平衡和船舶运动数据特征冗余的挑战，影响其有效性。为此，本文提出时间-图对比聚类海况估计算法（TGC-SSE），融合三个核心模块：时间维度因子分解模块以降低数据冗余，动态图学习模块捕捉复杂变量交互，以及对比聚类损失函数有效应对类别不平衡。实验表明，TGC-SSE在14个公开数据集上表现卓越，9次达到最高准确率，相比EDI提升20.79%。在海况估计领域，该模型超越5种基准方法及7种深度学习模型。消融实验证明各模块均有效提升性能。整体上，TGC-SSE不仅显著提高海况估计准确性，且具备强泛化能力，为自主船舶运行提供可靠支持。
原文摘要 · Abstract (English)
Accurate sea state estimation is crucial for the real-time control and future state prediction of autonomous vessels. However, traditional methods struggle with challenges such as data imbalance and feature redundancy in ship motion data, limiting their effectiveness. To address these challenges, we propose the Temporal-Graph Contrastive Clustering Sea State Estimator (TGC-SSE), a novel deep learning model that combines three key components: a time dimension factorization module to reduce data redundancy, a dynamic graph-like learning module to capture complex variable interactions, and a contrastive clustering loss function to effectively manage class imbalance. Our experiments demonstrate that TGC-SSE significantly outperforms existing methods across 14 public datasets, achieving the highest accuracy in 9 datasets, with a 20.79% improvement over EDI. Furthermore, in the field of sea state estimation, TGC-SSE surpasses five benchmark methods and seven deep learning models. Ablation studies confirm the effectiveness of each module, demonstrating their respective roles in enhancing overall model performance. Overall, TGC-SSE not only improves the accuracy of sea state estimation but also exhibits strong generalization capabilities, providing reliable support for autonomous vessel operations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。