时间频率协作学习不平衡的船舶运动数据与缺失的标签在海状态估计
IEEE transactions on cybernetics
|September 25, 2025
概括
BalanceSSE通过解决类不平衡和缺失数据,通过使用半监督学习 (SSL) 改进了海洋状态估计. 这种新的方法提高了船舶运动数据集的准确性.
科学领域:
- 海洋工程和海洋学.
- 人工智能和机器学习
- 数据科学是数据科学.
背景情况:
- 半监督学习 (SSL) 对于海洋状态估计 (SSE) 至关重要,但与不平衡和不完整的船只运动数据作斗争.
- 现有的SSE伪标签方法受到诸如高阶层不平衡和缺失数据等挑战的限制.
- 需要强大的SSL方法来处理船舶运动分析中的数据缺陷是很重要的.
研究的目的:
- 引入BalanceSSE,这是一种针对类不平衡船舶运动数据的新型半监督学习方法,用于海水状态估计.
- 解决现有的SSL方法在处理缺失数据和船舶运动数据集中的类不平衡方面的局限性.
- 提高深度学习模型在海洋状态估计中的准确性和适用性.
主要方法:
- 动态推算 (DIT):通过权衡不同的数据维度,动态推算不完整的船舶运动数据.
- 不平衡时间频率学习 (ITFL):利用时间频率协作学习来生成伪标签,以及用于伪标签选择的自适应性信心策略.
- 集群Prox分类器 (CL):通过集群和基于近距离的分类来提高伪标签和估计的准确性.
主要成果:
- 与最先进的方法相比,BalanceSSE在UCR和船舶运动数据集上的表现优越.
- 废弃性研究证实了每个模块 (DIT,ITFL,CL) 对BalanceSSE的整体有效性的重大贡献.
- 提出的方法成功地处理了类不平衡和不完整的数据,改善了海水状态估计.
结论:
- BalanceSSE为半监督的海洋状态估计提供了强大而有效的解决方案,特别是在存在数据挑战的情况下.
- 集成动态归算,失衡学习和高级分类显著提高了估计性能.
- 这项工作通过为现实世界船舶运动数据提供更适用和更准确的SSL框架,推进了海洋状态估计领域.
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