地震动是典型的非平稳随机过程，其非平稳特性对结构响应影响极大。
Earthquake motions are regarded as the typical non-stationary stochastic processes, and such non-stationary characteristics influence the structural response greatly.
针对数控机床机械系统的非线性和振动信号的非平稳特性，引入局域波分析方法。
Then, Local Wave method is introduced in consideration of the non-stationarity characteristic of the vibrating signals from NC machines.
因此采用能反映时频非平稳特性的时变谱密度(即局部谱密度)来描述地震地面运动是非常必要的。
Consequently, it is necessary to describe earthquake ground motion with local spectral density, which is time-dependent and can reflect the non-stationary characteristics.
针对故障率时间序列的非线性与非平稳特性，提出一种基于支持向量经验模态分解(SVEMD)的预测方法。
A prediction method based on support vector empirical mode decomposition (SVEMD) is proposed to deal with the non-linearity and non-stationarity of failure rate data.
实验结果表明，该法是分析处理具有时变谱特性的非平稳信号的一种有效方法。
Experiments show that the technique is effective for processing unstable signals of time-variation spectra.
动态测量是随测量时间而变化的非平稳随机过程，动态测量数据具有时变性、随机性、相关性和动态性四个基本特性。
The dynamic measurement is a non stationary random process varying with the measuring time. The dynamic measuring data have the characteristics of time variation, randomness, correlation and dynamic.
构造了正交小波变换矩阵，分析了平稳模型和非平稳模型下正交小波变换的残余相关特性。
Orthogonal wavelet transform (OWT) matrix is constructed and the residual correlation property of OWT is analyzed under the stationary and nonstationary models.
这样，大大地简化了在一大类统计特性缓慢变化的非平稳随机载荷作用下的结构的寿命与可靠性估计问题。
Thus, the problem of estimating of the reliability of structures under a big class of nonstationary random loadings with slowly varying statistics can be simplified significantly.
该文利用小波包变换的时频局部分析能力，研究了非高斯分布平稳随机噪声的统计特性，揭示了非高斯噪声信号的信号结构。
By exploiting wavelet packet transform to analyze signals both in time and frequency space, this paper researches the statistic property of non-Gaussian stationary noise and its signal structure.
研究了实际环境语音信号的特性，结合语音信号的短时平稳性和长时非平稳性，给出了一种时频域盲分离算法。
Investigation characteristic of real world audio, combine stationary for short time-scale and non-stationary for longer time-scales, proposed a time frequency domain blind source separation algorithm.
介绍了小波变换的基本概念和滤波特性，给出了小波变换作为一种新的分析工具在非平稳信号分析中的应用方法。
The concepts and its filter of wavelet transform are introduced, the application of wavelet as a kind of new analysis tools in unstable signal analysis is presented.
对于一个典型的地震记录，如果地震平稳段持续时间较短，采用非平稳随机过程描述其地震动特性较为合理。
For a typical earthquake record, if the duration of its stationary portion is rather short, such a record should be described in terms of a non-stationary random process.
针对传统滤波方法处理非平稳信号的不足，提出利用经验模态分解法来处理转子启动信号，通过此方法的自适应滤波特性来提取这类信号中的低频分量。
This paper describes a method to extract the low frequency component from rotor startup signal based on empirical mode decomposition, which overcomes the difficulties of traditional filter methods.
EMD方法是对非平稳、非线性信号进行分析的一种新的时频分析方法。它比小波分析等方法具有更强的特性并能准确地处理非常短的数据序列。
EMD method is a new method for analyzing nonlinear and non-stationary data, which has more advantage than wavelet analysis, and it can process short time series precisely.
而小波具有“变焦距”特性，在时域和频域中具有良好的局部分析能力，适合于超声等非平稳信号处理。
The wavelet transform has a good performance of local analyzing in both time domain and frequency domain. It is fit for analyzing non-stationary signals, such as ultrasonic signal.
它的分解基是随动态信号波形的变化而变化 ，具有自调节自适应的特征 ，因此能在时频域内描述非平稳非线性信号的局部特性。
Since the decomposition bases can vary with the local features of dynamic signals, the method is adaptive , therefore, highly efficient for describing nonlinear and non?stationary signals.
实验结果表明，小波变换的多分辨率分析对于分析处理具有时变谱特性的非平稳信号是一种新的有效方法。
Experiments show that the multiresolution analysis of wavelet transform is an effective new method for processing unstable signals having time variant spectra.
实验结果表明，小波变换的多分辨率分析对于分析处理具有时变谱特性的非平稳信号是一种新的有效方法。
Experiments show that the multiresolution analysis of wavelet transform is an effective new method for processing unstable signals having time variant spectra.
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