一种基于变压器的波参数检索新方法

Shuai Chen, C. Zheng, Liguo Sun
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引用次数: 0

摘要

提出了一种利用深度学习检索海浪主要参数的新方法,包括有效波高、峰值波周期、峰值波长和峰值波向。目前,有效波高的提取主要采用带注意的卷积长短期记忆(ConvLSTM)模型。然而,该方法的精度有待提高,并且只能检索到单一的波参数。我们提出了一种基于变压器的网络结构,有效地提高了精度,并可以同时检索多个波参数。最后,我们使用Pearson相关系数作为评价指标与基线进行比较。实验结果表明,该网络的性能得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Method of Wave Parameter Retrieve Based on Transformer
A new method using deep learning to retrieve the main parameters of ocean waves which include significant wave heights, peak wave periods, peak wave lengths and peak wave directions. At present, retrieve of significant wave height can use the model of Convolutional Long Short-Term Memory(ConvLSTM) with attention. However, the accuracy of this method needs to be improved, and it can only retrieve a single wave parameter. We propose a Transformer-based network structure, which improves the accuracy effectively and can simultaneously retrieve multiple wave parameters. Finally, we use Pearson's correlation coefficient as an evaluation index to compare with the baseline. Experimental results show that the performance of the network has been improved.
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