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Updated: Mar 15, 2026

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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通过CNN-SOFTS-based Coupled Spatio-Temporal Features进行海上轨迹预测.

Yongfeng Suo1, Chunyu Yang1, Gaocai Li1

  • 1Navigation College, Jimei University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括

这项研究引入了海上轨迹预测的新框架,有效地整合了空间和时间数据. 拟议的方法提高了复杂船舶运动的预测准确性,提高了航行安全.

关键词:
这是一个CNN模型,CNN模型.基于 CNN 软件的框架.在SOFTS模型中,SOFTS模型是有曲线的水道.时空特征是时间空间特征.轨迹的预测和预测.

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