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CNN-based Flow Field Feature Visualization Method

机译:基于CNN的流场特征可视化方法

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摘要

The feature-based visualization method can separate important areas for users from flow field data, which can better highlight the feature structure. However, most of the current feature extraction methods are only applicable to single typical features, and they need complex mathematical analysis. Based on the above reasons, this paper proposes a universal feature visualization method, recognizes demand in the region of flow data, shows the characteristics of structure protruding from the global visual effect in the design of a multi-dimension parallel convolution kernel that contains the recognition model, and further puts forward the method of feature visualization based on a convolutional neural network. Compared with the classical three level BP neural network model, our model gets a high accuracy rate. We verify the effectiveness of the method and solve the problem of insufficient expansion of existing methods.
机译:基于特征的可视化方法可以从流场数据中分离用户的重要区域,这可以更好地突出特征结构。 然而,大多数当前特征提取方法仅适用于单一典型特征,并且它们需要复杂的数学分析。 基于上述原因,本文提出了一种普遍的特征可视化方法,识别在流量数据区域中的需求,示出了从全局视觉效果突出的结构的特征,这些特性在包含识别的多维并行卷积内核的设计中突出 模型,并进一步提出了基于卷积神经网络的特征可视化方法。 与古典三级BP神经网络模型相比,我们的模型得到了高精度率。 我们验证了该方法的有效性,解决了现有方法扩大不足的问题。

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