基于子波变换的烟台市降水变化特征的多时间尺度研究

为研究烟台市年降水量变化的多时间尺度结构特征,采用Morlet子波变换分析法,对烟台市1960~2004年的年降水量进行了小波分析。结果表明:近40年来,烟台市年平均降水量整体呈下降趋势,且存在明显的4年、13年和28年的3类特征时间尺度和相应的震荡周期;小波方差进一步分析发现,烟台市年降水量变化具有4年和13年的主周期,且以13年周期最为显著;由小波方差检验出主要周期的小波系数实部可知,在未来的15年内,烟台市降水将会处于偏丰期。     

基于小波分析的故障选线仿真研究

文章主要解决将小波变换应用于小电流接地系统选线问题,仿真结果表明,本文提出的选线方法应用于中性点不接地系统和经消弧线圈接地系统且不受暂态电阻和故障相影响均可快速准确地选出故障线路.     

电压骤降暂态扰动点检测方法研究

电压骤降是电网中一种典型的暂态电压扰动信号,为了检测出其发生电压扰动的位置,现在多采用小波变换或小波包变换对扰动信号进行检测。利用Maflab仿真对电网中的暂降信号进行检测,对比小波包变换与小波变换对扰动信号突变点检测的效果。仿真结果表明：在不去噪直接进行检测的情况下,使用相同的采样频率和相同信噪比强度,小波变换对扰动点的检测更准确。     

基于小波的木材纹理分频信息提取与分析

通过引入小波方法 ,对木材纹理进行了多尺度的频谱分解 ,并利用所得到的特征向量分析了水平、垂直和对角方向上的木材纹理频率分布特点 ,比较了针叶树材与阔叶树材、径向切面与弦向切面木材纹理的统计差异。并在试验基础上 ,提出了以小波分解子图像能量值的标准差进行木材纹理最佳分解尺度的筛选 ,探索出滤波长度取 8、分解尺度取 2对充分表现木材纹理特征最为适宜。同时还发现可将垂直中高频分量HL和低频分量LL的能量值作为木材纹理区别与归类的重要参数 ,将EHL ELH值作为木材纹理的方向性量度     

小波包分析在木材超声检测中的应用

小波包分解能够准确地把超声检测信号划分到不同的频带范围内，实现了不同频带范围内能量变化量的提取。试验说明缺陷试件的信号能量变化量在一些特定的频带内显示出显著的不同，为下一步应用神经网络等模式识别方法辨别试件缺陷类型提供了比较理想的特征向量。     

木材纹理分析中小波基的选择和分解级数的确定

针对小波多分辨率下木材纹理分析中小波基的类型和分解级数进行了研究.用小波基重构误差和小波基的性质确定小波基;用信息熵和重构图像能量确定分解级数.实验证明,选用Symlets4小波基对木材纹理图像进行2级分解就能够获得较高的分类识别率.     

Parameter optimized Morlet wavelet de noising method based on cross validation method

A new de noising method based on parameter optimized Morlet wavelet is put forward. The Morlet wavelet is chosen as the mother wavelet because its shape is similar to the mechanical shock signals. The mother Morlet wavelet is improved by adding two parameters which decide the shape of the mother wavelet in time domain. The added parameters and the appropriate scale parameter for the wavelet transformation are designed by the cross validation method. Finally, the useful components of the signal can be obtained by the improved Morlet wavelet de noising method. The gear fault diagnosis experimental result shows that the proposed method has a good de nosing performance and it is effective in fault feature extraction.     

Real time monitoring for multivariate statistical process with on line multiscale filtering

By analyzing shortages of current MSPCA model, an on line multi variable statistical process monitoring method is proposed, which uses some concepts from online multi scale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixed length data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an on line multi scale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multi scale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.     

Multi Time Scale Characteristics of Urban Heat Islandin Xi’an’s Summer and Winter

Based on the hourly data of air temperature observed from automatic weather stations in Xi’an, the time series of urban heat island (UHI) intensity in the summer and winter of year 2008 were set up. Complex Morlet wavelet transform was employed to analyze the time scale (variation period) structure and characteristics of UHI. And wavelet variance was used to diagnose the oscillation intensity of UHI periods. It was found that the time scale structure of UHI was simple in winter while complex in summer. The primary UHI periods were composed of 24 h and 144 192 h in winter and 24 h and 96~120 h in summer. Diurnal and weekly variations of UHI were the dominant UHI periods in Xi’an. For weekly periods, intensive UHI occurred on weekdays, while weak UHI on weekend. Moreover, the causes of the diurnal and weekly periods of UHI were also employed.     

A Meta-synthetic Approach for Lower-constrained Handwritten Character Recognition

A meta-synthetic network approach for conbination of multiple classifier is proposed aimed at solving the problems of character recognition. It composes of two B-Wavelet neural network classifiers and a Morlet-B-Wavelet neural network classifier.It is shown that the method is effective used in handwritten capital English character recongnizing.