采用经验模式分解(EMD)与小波分析相结合的方法探讨结构响应数据信号，进行建筑结构损伤检测诊断。
The use of empirical mode decomposition (EMD) method and wavelet analysis in combination is explored for the detection of changes in the structural response data from structural damage diagnosis.
经验模式分解的主要思想类似小波变换，分解结果是由高频到低频分布的，即噪声主要集中在前几层。
EMD similars the wavelet transforms, the decomposition result is from high frequency to the low frequency distribution, namely the noise mainly concentrates in first several.
针对复杂背景下机车走行部齿轮箱齿轮裂纹故障微弱特征的提取问题，提出了总体平均经验模式分解(EEMD)与1。
To extract the gear crack fault weak feature of locomotive running gear box on complex background, a new method of ensemble empirical mode decomposition (EEMD) and 1.
提出了一种基于经验模式分解的气液两相流流型识别方法。
A method of flow regime identification based on empirical mode decomposition was proposed.
对经验模式分解算法中的滤波停止条件和端点延拓问题进行了研究。
The stopping criteria for sifting and boundary effect are studied for Empirical Mode Decomposition(EMD) algorithm.
提出一种基于二维经验模式分解(BEMD)的图像水印嵌入算法，完成水印的嵌入和提取。
This paper proposes an image processing method which is based on Bidimensional Empirical Mode Decomposition (BEMD). BEMD is applied in watermark embedding and extraction.
虽然HVD方法和希尔伯特黄变换（HHT）方法这两者均以希尔伯特变换为基础，但HVD方法避免了复杂的经验模式分解（EMD）过程。
The proposed HVD method was based on the Hilbert transform(HT), just as Hilbert-Huang transform(HHT), but did not involve complicated empirical mode decomposition(EMD).
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.
将经验模式分解和多层前向网络的交叉覆盖算法相结合，提出一种时间序列相似模式的匹配算法。
This paper proposes an effective time series matching method by combining the empirical mode decomposition (EMD) with the alternative covering algorithm.
本文基于经验模式分解理论提出了一种新的分割方法。
This paper proposes a new image segmentation method based on the theory of EMD.
本论文研究了以图像为载体，基于经验模式分解算法的信息隐藏。
This thesis researches on the hiding information which held the image as the carrier and based on empirical mode decomposition algorithm.
由于现实中的信号多为非平稳非线性信号，经验模式分解及其应用已成为近年来国内外热门的研究课题。
As the magority of reality signals is non-stationary and nonlinear signals, empirical mode decomposition and its application has become a hot research topic at home and abroad in recent years.
研究结果表明：基于经验模式分解的时频分析方法可以很有效地提取到非平稳故障特征信号，是一种适合于非线性信号处理的方法。
The experiment result shows that the time-frequency method based on EMD can effectively extract the feature of unbalanced fault signal and is proper for non-frequency modulation signal procession.
为了提高风电场风速短期预测的精度，提出了将经验模式分解与数据挖掘方法相结合对风速时间序列进行建模预测。
In order to improve the forecast precision, a forecasting method based on empirical mode decomposition (EMD) and data mining method is proposed.
在分析经验模式分解存在问题的基础上，改进了经验模式分解的算法，提出用窗口平均法取代原极值包络法来计算局部均值。
Based on the analysis of some problems in the method of empirical mode decomposition, the algorithm of empirical mode decomposition is improved in this paper.
经验模式分解（EMD）通过筛分过程将原始信号分解成若干个基本模式分量（IMF），可看作无需预设带宽的自适应高通滤波方法。
Empirical mode decomposition(EMD) is a signal processing technique to decompose data set into several intrinsic mode functions(IMF) by a sifting process.
讨论了由于间歇信号嵌入导致经验模式分解结果出现模态混叠的现象，指出极值点序列幅值突变是产生模态混叠的根本原因。
The reason to mode mixing in the result of empirical mode decomposition is discussed in respect of the variation of extremum series.
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.
应用经验模式分解(E MD)方法分析抽油机系统效率变化的趋势项。讨论了EMD方法的端点效应。
We apply the empirical mode decomposition (EMD) method to analyzing the trend of change in the efficiency of an oil well system and deal with the end effects of the EMD.
在论述了经验模式分解（EMD）信号分解原理的基础上，分析了其存在的边缘效应，并提出了通过添加极值点抑制边缘效应的思路和策略。
The end effects of empirical mode decomposition (EMD) are discussed and the end effects are restrained by the method. The results of EMD in both signals are analyzed by three ways.
研究了强噪声混合条件下微弱信号的经验模式分解（EMD）问题，提出了一种基于随机共振降噪的EMD分解方法。
To deal with this problem, comparison is made between the empirical mode decomposition(EMD) and the wavelet method in terms of signal trend extraction.
在已有经验模式分解的过程中，由于常用三次样条插值来拟合信号的上下包络，因此时常会出现边缘效应，从而影响了信号处理的质量。
Cubic spline interpolation is used to create the upper and lower envelopes in traditional EMD process, which has worse behavior at the edges, and influences the quality of signal process.
讨论了由于间歇信号嵌入导致经验模式分解结果出现模态混叠的现象，指出极值点序列幅值突变是产生模态混叠的根本原因。
Aiming at the mode mixing problem caused by intermittency signal in the sifting process of empirical mode decomposition (EMD), a new solution is presented.
然后应用SVR方法对系统效率测试原始数据进行双边延拓，对延拓后的数据信号进行经验模式分解。
The comparison of the test data with measurement data shows that the regression and prediction with the SVR method are highly accurate.
然后应用SVR方法对系统效率测试原始数据进行双边延拓，对延拓后的数据信号进行经验模式分解。
The comparison of the test data with measurement data shows that the regression and prediction with the SVR method are highly accurate.
应用推荐