本文正是基于这种分层的思想，定义了凹多边形的模式分解方法和分层加权测度模型。
Exactly based on this idea, sub-model decomposition approach to concave polygon and hierarchical model weighted measure are presented.
本文通过分析经验模式分解方法的原理，对其关键技术进行研究并提出了一种改进算法。
This paper analyzes the principle of empirical mode decomposition method, and it analyzes key technologies and proposed an improved algorithm.
为了避免经验模式分解(EMD)过程中不同时间尺度函数间的模式混叠，采用基于高斯白噪声加入的经验模式分解方法，并将之应用于旋转机械故障诊断中。
The EMD added Gauss white noise is proposed to avoid mode mixing of different time-scale IMF, and is applied in fault diagnosis for rotating machine.
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