In view of energy, the different frequency band is chosen corresponding to the distribution system with the different neutral grounding mode.
对于中性点接地方式不同的配电网，按照能量的观点，选择不同的频带来实现故障选线。
Pressure data were analyzed by wavelet transform in wavelet domain. Frequency band energy spectrum and intensity index of ISW based on wavelet coefficient were obtained.
通过基于子波的分析方法，对压力数据进行了分析，获得了基于子波系数的频带能量谱和冲击波强度指数。
In view of energy, using waveform recognition, the different frequency band is chosen corresponding to the distribution system with the different neutral grounding mode in SFB.
对于中性点接地方式不同的配电网，按照能量的观点，在SFB频段内选择不同的频带利用波形识别技术来实现故障选线。
The characteristic frequency band of the fault can be identified by wavelet packet decomposition and its energy spectrum conveniently, and the quantification analysis are then performed.
应用小波包分解及其能量谱直观地识别出故障的特征频带，并进行了量化分析。
Therefore, this article will take the signal energy in each frequency band as the feature vector to characterize the operation state of rolling bearing.
因此本文接下来采用每个频带里包含的信号能量作为特征向量，来表征滚动轴承的运行状态。
The characteristic frequency band of the fault could be identified by wavelet packet decomposition and its energy spectrum conveniently, at the same time, quantification analysis were performed.
应用小波包分解及其能量谱直观地识别出故障的特征频带，并进行了量化分析。
The circular region F-norms in low frequency band of wavelet transform is used as color feature of the image and the energy ratio of high frequency band is used to form texture descriptor.
颜色特征采用整数小波低频系数矩阵中环形区域的F -范数，纹理特征则采用高频部分的能量比值。
At last, the energy of the bubble formation frequency band was compared with the bubble model which shows the reliability of the analysis methods mentioned above.
将气泡生成脉动能量与气泡模型进行比较，结果再次论证本文分析方法的可靠性，本文的研究工作为稠密气固两相流动的研究揭开了一个崭新的篇章。
The frequency parameters are set in highly denseness in high energy frequency band, but in sparse denseness in low energy frequency band.
在能量高的频段密集选取频率参数，相反在能量低的频段稀疏选取频率参数。
Signal sample is analyzed through wavelet packet theory, best base is chose, and the average energy of every sub frequency band is calculated, at last 14 links of characteristic vectors are get.
将信号样本进行小波包变换，并选择最好基，计算各子频带的平均能量，得到14维特征向量。
Signal sample is analyzed through wavelet packet theory, best base is chose, and the average energy of every sub frequency band is calculated, at last 14 links of characteristic vectors are get.
将信号样本进行小波包变换，并选择最好基，计算各子频带的平均能量，得到14维特征向量。
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