基于时间尺度分配方法的改进经验模态分解和二维模态混合现象判断

IF 0.5 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Advances in Data Science and Adaptive Analysis Pub Date : 2017-06-14 DOI:10.1142/S2424922X17500024

Shu-Mei Guo, J. Tsai, Chin-Yu Chen, Tzu-Cheng Yang

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引用次数: 1

摘要

在传统经验模态分解(EMD)的筛选过程中，间歇性会导致模态混合现象。具有模态混合现象的本征模态函数(IMF)失去了其原有的真实物理意义。为了改进二维图像数据中模态混合现象的分解，对基于时间尺度分配方法和二维(2D)版本的改进EMD进行了扩展。实验结果表明，该方法不仅对一维信号和二维图像都有较好的改善，而且在质量和计算时间上都有较好的表现。

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An Improved Empirical Mode Decomposition Based on Time Scale Allocation Method and the 2D Mode Mixing Phenomenon Judgement

In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. An improved EMD based on time scale allocation method and the two-dimensional (2D) version of our method has been extended to improve the decomposition of the mode mixing phenomenon in 2D image data. Experimental results show that the method not only improves the phenomenon correctly both for 1D signal and 2D image, but also exhibits great performance in quality and computation time.

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Advances in Data Science and Adaptive Analysis MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-

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