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逼近信号
相关语句
  approximation signal
     Splitting operation is unnecessary for ARSGW, and the approximation signal at each level is predicted and updated directly to retain the signal characteristics in time domain.
     第2代小波不需要剖分运算,利用冗余预测器和更新器直接对每层逼近信号进行预测和更新运算,能较好地保留信号的时域特征.
短句来源
     Experimental results show that it is very simple and accurate to determine the close time of sucking valve with the help of the approximation signal resulted from wavelet decomposition of the absolute vibration signals.
     研究结果表明 ,用振动信号绝对值小波分解后的逼近信号可较准确地判断吸入阀的关闭时刻 ,进而准确地判断泵的吸入状态。
短句来源
     Splitting operation is unnecessary for RSGW, and the approximation signal is predicted and updated directly, the length of approximation signal and detail signal for all scales remains identical, and the thresholds at different scales are selected according to the noise characteristics.
     冗余第2代小波不需要剖分运算,直接利用构造的算子对逼近信号进行对称预测和更新,可使逼近信号和细节信号的数据点数保持不变,并根据每层细节信号的噪声特点选取降噪阈值门限.
短句来源
     The main idea of this method builds on the principle that the approximation signal of multi-resolution analysis characterizes the baseline wander of ECG almost fully.
     该方法的基本思想是:通过对ECG信号进行多尺度分解,利用分解后所得的逼近信号充分逼近ECG中的基线漂移噪声的特性,从而滤除ECG中的基线漂移分量。
短句来源
     The demodulation waveform and periodic impulses from the rub fault vibration signal of a large air compressor gearbox are desirably extracted with ARSGW, the approximation signal is demodulated, and the rotating frequency of high speed axis is regarded as the modulation frequency.
     采用第2代小波较理想地提取出了齿轮箱发生摩擦故障时的时域调制波形和周期性冲击脉冲,并对得到的细节和逼近信号进一步进行包络解调,从而分离出了故障调制源频率.
短句来源
  approached signal
     The vibrational signal of compressor is monitored and analyzed by dyadic wavelet transform. The detail signal and approached signal at different frequency part of original signal of compressor is obtained by dyadic wavelet decomposition. The waterfall map of vibrational signal of compressor at different frequency part is presented.
     对压缩机振动信号进行了二进小波监测分析,得到了压缩机原始信号在不同频率段分解的细节信号的逼近信号,并给出了不同频率段压缩机振动信号的瀑布分析图,得到了满意的分析结果。
短句来源
     The study results show that fractal dimensions of approached signal and fine PD energy spectrum can be used as the PD pattern features with proper wavelet and scale functions.
     研究结果表明 :选用适当的小波函数和尺度函数 ,将局部放电信号的逼近信号能量谱和精细结构能量谱的分形维数作为局部放电模式特征 ,能够有效地应用于局部放电模式识别中。
短句来源
  approximation signals
     The LPC predictive error signal is decomposed layer by layer by the well-known Mallat algorithm in wavelet transformation and the peaks are searched for in the approximation signals of the lowest resolution. The relevant pitch period can be found by thepeaks determined with the LPC predictive error after all the approximation signals have beentracked backward layer by layer,
     该算法利用小波变换中著名的Mallat算法逐层分解LPC预测误差信号,在最低分辨率的逼近信号中寻找峰值,然后逐层回溯各个分辨率的逼近信号,最后在LPC预测误差中确定出峰值,从而求出相应的基音周期。
短句来源
     The approximation signals on level 4 of the coaxial signals,after wavelet decomposing,can intuitionisticly distinguish the 1st class welds,while detail signals on level 3 and level 4 of coaxial optical emission can clearly identify the 2nd class welds.
     同轴光信号小波分解后的第4层逼近信号,可以直观地识别出质量好的Ⅰ类焊缝。 同轴光信号的第3层、第4层细节信号可以清晰地辨识出质量低劣的Ⅱ类焊缝。
短句来源
     Moreover,for the purpose of getting the time resolution that is the same as its original signal,a single-branch reconstruction method is used to process approximation signals and detail signals respectively,which produces the time domain response features of rotor rub-impact faults. The method provides a new approach to the separation of time domain features from vibration signals during fault diagnosis and prognosis.
     另外,为了获得与原始信号相同的时间分辨率,采用单支重构的方法分别对逼近信号和细节信号处理,得到了转子碰摩故障的时域响应特征,为故障诊断和预示提供了一种分离故障时域特征的方法。
短句来源
  “逼近信号”译为未确定词的双语例句
     Sampling Method for Approaching Rate of Signal Bandwidth
     采样率逼近信号带宽的采样方法研究
短句来源
     Cj+1 = dε HCj+1Dj = dεGCjThe above formula indicate that signal doesn’t be downsampled in the process ofthe stationary wavelet transform, the length of the approaching signal and detailsignal is same as the original signal’s.
     Dj = dεGCj上式表明在信号平稳小波变换过程中不采用下抽样处理,每次平稳小波变换的逼近信号和细节信号长度与原信号长度相同。 平稳小波变换的逆变换为aj ε1,L,ε =
短句来源
     An iterative approach for tracking the signal subspace based on a novel principal component analysis technique is presented.
     提出一种改进的PCA(principal component analysis)迭代算法,来逼近信号子空间。
短句来源
     A Method Using Wavelet Approximation to Remove the Electrocardiogram Baseline Wander
     一种利用小波变换逼近信号滤除心电图基线漂移的方法
短句来源
     WPD was then employed for the discrete approximation coefficients and the discrete detail coefficients by different basis wavelets. Various rules were adopted for selecting thresholds to denoise. The denoised signal was finally reconstructed.
     该算法首先将带噪语音进行离散小波变换,并分别对离散逼近信号和离散细节信号采用不同的基小波进行小波包分解,再按照不同的规则选取阈值进行去噪,最后对去噪后的语音信号完成重构。
短句来源
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  approximation signal
Our technique is based upon viewing the projection data as a set of one-dimensional functions of the space variablet and decomposing each one into an approximation signal and a set of detail signals using MRA.
      
The approximation signal and detiil signals associated with each projection are filtered using the ramp filter |ω| of the standard reconstruction technique filtered back projection to generate the set of filtered projections.
      
A residual correlation still exists between the approximation signal and the details signals.
      
In the algorithm, a discrete signal is decomposed in a lower scale approximation signal A and detail signal D.
      
One can see that the adaptive scheme yields a smoothed approximation signal except near the discontinuities.
      
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Based on the LPC analysis of speech signals an effective method for the detection of thepitch is proposed.The LPC predictive error signal is decomposed layer by layer by the well-known Mallat algorithm in wavelet transformation and the peaks are searched for in the approximation signals of the lowest resolution. The relevant pitch period can be found by thepeaks determined with the LPC predictive error after all the approximation signals have beentracked backward layer by layer,

在对语音信号进行LPC分析的基础上,提出了一种有效的语音基音周期检测算法。该算法利用小波变换中著名的Mallat算法逐层分解LPC预测误差信号,在最低分辨率的逼近信号中寻找峰值,然后逐层回溯各个分辨率的逼近信号,最后在LPC预测误差中确定出峰值,从而求出相应的基音周期。

The advantages of wavelet analysis are introduced.The iterative expressions of dyadic discrete transformation are presented.The engineering implementation technique of wavelet analysis is studied from engineering application aspect.The vibrational signal of compressor is monitored and analyzed by dyadic wavelet transform.The detail signal and approached signal at different frequency part of original signal of compressor is obtained by dyadic wavelet decomposition.The waterfall map of vibrational signal of compressor...

The advantages of wavelet analysis are introduced.The iterative expressions of dyadic discrete transformation are presented.The engineering implementation technique of wavelet analysis is studied from engineering application aspect.The vibrational signal of compressor is monitored and analyzed by dyadic wavelet transform.The detail signal and approached signal at different frequency part of original signal of compressor is obtained by dyadic wavelet decomposition.The waterfall map of vibrational signal of compressor at different frequency part is presented.The analysis results are well.

介绍了小波分析的优越性,给出了二进离散小波变换的迭代公式,从工程应用角度研究了小波分析的工程实现技术。对压缩机振动信号进行了二进小波监测分析,得到了压缩机原始信号在不同频率段分解的细节信号的逼近信号,并给出了不同频率段压缩机振动信号的瀑布分析图,得到了满意的分析结果。

In recent years,the researches on combination of wavelet analysis and neural networks have attracted much attention. This paper reviews the development and status about, this field. The combination of wavelet and neural networks can be categorized into the ancillary type and the embedded type, the latter of which is referred to wavelet neural nctworks(WNN). WNN including its main models, algorithms and other issues are discussed. The applications and prospects of WNN are also given, which showed that WNN have...

In recent years,the researches on combination of wavelet analysis and neural networks have attracted much attention. This paper reviews the development and status about, this field. The combination of wavelet and neural networks can be categorized into the ancillary type and the embedded type, the latter of which is referred to wavelet neural nctworks(WNN). WNN including its main models, algorithms and other issues are discussed. The applications and prospects of WNN are also given, which showed that WNN have great competence and potential in the applications of function approximation, signal classification,system identification and image compression.

目前,小波与神经网络的结合是一个十分活跃的研究领域。本文综述了这一领域的研究进展和现状,从两者结合方式的不同将其分为辅助式及嵌套式两种结合方式,重点阐述了嵌套式的结合方式—小波神经网络,并对其主要模型、算法和其它相关问题进行了论述。本文还讨论了小波网络的各种应用,从中可以看到它在函数逼近、信号分类、系统辨识、图像压缩等应用领域有极大的潜力,最后展望了今后的研究方向。

 
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