该一维有限元列式只需对扇形区域在角度方向上离散，最后的总体方程为一个二次特征根方程。

Discretization in angular coordinate is needed only and the global equation is a second order characteristic matrix equation.

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针对该问题，采用核典型相关分析方法进行原始特征的二次提取，得到简约而重要的二次特征。

Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.

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在第二次试验中，采用模糊外部特征以及特征切换的方法后，EvoFIT产生的图像正确辨认率达到24.5%。

In a second experiment, EvoFIT images led to correct identifications 24.5% of the time with the blurring and feature toggling added.

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