几种短时电能质量扰动分类和检测的双小波分析法

提出了短时电能质量扰动分类和检测的双小波分析法。利用双小波 (db1和db2 4 )各自的优点 ,把电能质量 5种扰动 (电压凹陷、电压凸起、电压间断、暂态脉冲和暂态振荡 )有效地从含有噪声的采样信号中鉴别出来 ,并能实现扰动的各项指标测定。该方法弥补了以往小波检测方法中 ,当噪声污染严重或扰动发生、终止在工频相角为 0或π附近时 ,可能检测不到或误判断的不足。仿真计算结果表明 ,该方法对扰动的分类简单、有效 ,对扰动各项指标测定尤其是电压凹陷、凸起和间断的时刻及幅度的确定 。

基于概率神经网络和双小波的电能质量扰动自动识别

对电能质量(PQ)扰动的自动识别是找出引起PQ问题根本原因的前提。提出了一种基于概率神经网络PNN(Probabilistic Neural Networks)和db10,db1双小波的PQ扰动自动识别方法。首先,利用db10小波对信号进行分解,将各层小波变换系数的能量和第1高频层模极大值情况作为PNN的输入矢量,判断扰动类型;然后,对信号进行傅里叶变换以检测信号中是否含有谐波;最后,对判断存在电压下降的信号进行db1小波分解,根据其低频层的模值区分电压下陷和电压中断信号。测试结果表明,该方法提高了识别正确率,且实现简单,能有效检测幅值较小的谐波。

双小波神经网络迭代的漏磁缺陷轮廓重构技术

在二维漏磁缺陷重构中,建立基于径向基小波神经网络(RWBF)的正演和反演模型,提出了一个反馈形式的双小波神经网络迭代模型,通过迭代使目标函数最小化,实现对缺陷轮廓的快速逼近。用仿真和实验获取的训练样本分别对正演和反演模型的RWBF进行训练。为了提高径向基神经网络的适应性和精度,提出了一种新的训练算法。首先确定最优分解层数,然后利用梯度下降法修正网络的权值。对不同分辨率和不同信噪比下的漏磁信号进行了重构,并与其他方法进行了比较。结果表明,双小波神经网络迭代模型能够实现漏磁缺陷的精确逼近,具有良好的鲁棒性,是有效的二维轮廓重构方法。

改进的双小波空域相关混沌信号降噪方法

基于小波系数尺度间的相关性原理,提出一种改进的基于双小波的空域相关混沌信号降噪方法。首先将单个离散小波变换扩展为两个,然后对每个小波变换后的近似系数进行奇异谱分析,对细节系数进行尺度间的相关性分析,最后将处理后的近似部分和细节部分分别取平均再重构得到降噪后的信号;同时还引入了一个判别系数,对相关量进行自适应的选取。通过对Lorenz模型和月太阳黑子混沌信号进行仿真分析,证实了所提方法具有实现简单、重构误差小的优点,能够对实际观测混沌信号进行有效的降噪。

基于双小波变换的抗干扰电压暂降检测方法研究

针对目前电压暂降检测时存在的由于噪声和谐波等干扰导致的分析精度差、实时性不高等问题，提出一种基于双小波变换的电压暂降检测方法。该方法首先采用db20小波对信号进行多尺度分解，提取含暂降信息的基波信号。然后采用db10小波对基波信号进行单尺度分解，检测高频分量的模极大值对电压暂降实现定位，从而降低了噪声和谐波等干扰信号的影响，提高了小波分析的检测性能。

一种双小波基的构造方法

根据双尺度方程导出的尺度函数的Fourier变换无穷乘积公式,结合函数与函数的Hilbert变换之间的Fourier变换关系,提出了由2个共扼正交滤波器所构造的2个小波基是双小波基的一个条件,并通过满足该条件的例子来设计了满足该条件的具有最小长度的这种类型的2个滤波器的求解方法.

二元半规范双小波框架滤波器的构造

多元双小波框架的构造由于其较大的自由度和计算的复杂性,一直是小波框架研究内容的难点问题之一。本文提出了一类具有规范滤波器的二元双小波框架的构造方案。具体地,对给定的一对低通滤波器组,利用混合酉扩展原理,首先建立可以生成半规范双小波框架的高通滤波器对的最小数目,然后根据滤波器和多相矩阵之间的关系,给出了构造双小波框架滤波器的具体方案。

双小波非凸稀疏正则化去噪算法研究

针对航空发动机ECT滑油监测数据在采集传输过程中易受噪声干扰而影响数据有效特征提取的问题,提出了一种双小波去噪算法。在2个小波域下对数据进行分解,通过阈值函数进行滤波,利用小波系数的分布差异,迫使2个小波域下的去噪信号相同,反正切函数作为罚函数,得到更加稀疏的系数表达。实验结果表明:与传统的小波去噪方法相比,连续信号和阶跃信号的平均信噪比提高了约2.3 d B和4.2 d B,去噪效果得到优化。

基于双小波变换的负载电流微分干扰补偿仿真

电力系统的负载电流极易受到外界干扰信号的影响,导致负载电流的不稳定输出,为此提出基于双小波变换的负载电流微分干扰补偿法。利用双小波变换技术实时跟踪检测负载电流的输出情况,判断负载电流是否处于平稳状态。通过分析电流微分干扰,确定负载电流不稳定的原因。搭建负载电流干扰补偿方程,求解补偿系数与电流偏移量,并安装负载电流干扰补偿控制器,分别从总量、分量和残量三个方面实现对负载电流的直接补偿。通过仿真得出结论,与传统补偿方法相比,基于双小波变换的负载电流微分干扰补偿方法的补偿速度提升了50%,极大程度上减少了外界信号干扰的影响,保证了电流的稳定输出。在实际应用中,提高了电力系统的运行效能和稳定性。

基于CS算法的结构光图像融合技术研究

结构光图像广泛应用于工业自动化机器人的机器视觉。由于光学传感器的性能限制,测量精度与检测范围存在矛盾,光线的对焦不同、存在遮挡物会导致成像困难。本文运用基于压缩感知理论(Compressed sensing,CS)的多源图像融合技术解决了数据量大和工件成像难的问题。构建了最优小波稀疏基和最优小波融合基,对比了基于正交匹配追踪(Orthogonal matching pursuit,OMP)和经典的梯度投影(Gradient projection for sparse reconstruction,GPSR)的双小波变换压缩感知算法,成功进行超稀疏的正交匹配追踪工件图像融合与准确定位。实验表明,本文算法在结构光视觉成像时,可去除遮挡物并避免传统融合的信息损失和计算繁琐现象,提高了目标图像的空间分辨率和清晰度。这有利于机器视觉对工件位置、形状和类型的判断,成功进行精准定位放置等自动化机械操作。

Integrated interpretation of dual frequency induced polarization measurement based on wavelet analysis and metal factor methods

In mineral exploration, the apparent resistivity and apparent frequency （or apparent polarizability） parameters of induced polarization method are commonly utilized to describe the induced polarization anomaly. When the target geology structure is significantly complicated, these parameters would fail to reflect the nature of the anomaly source, and wrong conclusions may be obtained. A wavelet approach and a metal factor method were used to comprehensively interpret the induced polarization anomaly of complex geologic bodies in the Adi Bladia mine. Db5 wavelet basis was used to conduct two-scale decomposition and reconstruction, which effectively suppress the noise interference of greenschist facies regional metamorphism and magma intrusion, making energy concentrated and boundary problem unobservable. On the basis of that, the ore-induced anomaly was effectively extracted by the metal factor method.

城轨直流牵引系统短路故障早期检测研究

城轨直流牵引系统分布狭长密集,一旦发生故障,快速上升的电流和所产生的短路能量极易引起火灾事故,威胁列车安全运行和乘客人身安全。目前,直流短路检测研究存在短路故障特性研究不足且缺乏实验验证等问题。鉴于此,从电力电子换流器内部电路结构出发,分析换流器发生短路故障时,其内部暂态过程及其特性,并以此特性开发相应的直流牵引系统短路实验平台。引入Mallat快速小波算法,提出电压、电流双小波分解的直流短路故障早期检测方法,经实验验证表明,该方法不仅可区分正常工况直流暂态过程与短路故障,且可在短路故障发生300μs内实现有效的故障早期检测及辨识。对比传统直流馈线电流上升率及电流增量(DDL)保护法,该方法故障检测速度提高10余倍,检测故障时短路电流不足DDL方法的10%,可为早期检测后续的故障早期抑制与快速开断的深入研究奠定良好的技术前提与基础。

Application of Image Fusion Methods to Cell Imaging Processing

Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.

A quantitative analysis method for GPR signals based on optimal biorthogonal wavelet

Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.

AN APPLIED RESEARCH ON APPROACH OF DYADIC WAVELET TRANSFORM FOR REMOTE SENSING IMAGE EDGE DETECTION

In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection.

Monitoring of Wind Turbine Blades Based on Dual-Tree Complex Wavelet Transform

Structural health monitoring(SHM)in-service is very important for wind turbine system.Because the central wavelength of a fiber Bragg grating(FBG)sensor changes linearly with strain or temperature,FBG-based sensors are easily applied to structural tests.Therefore,the monitoring of wind turbine blades by FBG sensors is proposed.The method is experimentally proved to be feasible.Five FBG sensors were set along the blade length in order to measure distributed strain.However,environmental or measurement noise may cover the structural signals.Dual-tree complex wavelet transform(DT-CWT)is suggested to wipe off the noise.The experimental studies indicate that the tested strain fluctuate distinctly as one of the blades is broken.The rotation period is about 1 s at the given working condition.However,the period is about 0.3 s if all the wind blades are in good conditions.Therefore,strain monitoring by FBG sensors could predict damage of a wind turbine blade system.Moreover,the studies indicate that monitoring of one blade is adequate to diagnose the status of a wind generator.

Selective image enhancement method for low energy target information area

A selective subband enhancement method based on biorthogonal wavelet base is proposed. This novel image enhancement method is just for those images in which the energy of target information area is relatively lower. It includes two parts: one is enhancing the low frequency subband by wavelet decomposition and the other is building a new criterion based on entropy window to image evaluation. Experimental results show that this new scheme may result in a perfect image processing.

Compression and reconstruction of speech signals based on compressed sensing

Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.

Phase estimation in bispectral domain based on conformal mapping and applications in seismic wavelet estimation

Seismic wavelet estimation is an important part of seismic data processing and interpretation, whose preciseness is directly related to the results of deconvolution and inversion. Wavelet estimation based on higher-order spectra is an important new method. However, the higher-order spectra often have phase wrapping problems, which lead to wavelet phase spectrum deviations and thereby affect mixed-phase wavelet estimation. To solve this problem, we propose a new phase spectral method based on conformal mapping in the bispectral domain. The method avoids the phase wrapping problems by narrowing the scope of the Fourier phase spectrum to eliminate the bispectral phase wrapping influence in the original phase spectral estimation. The method constitutes least-squares wavelet phase spectrum estimation based on conformal mapping which is applied to mixed-phase wavelet estimation with the least-squares wavelet amplitude spectrum estimation. Theoretical model and actual seismic data verify the validity of this method. We also extend the idea of conformal mapping in the bispectral wavelet phase spectrum estimation to trispectral wavelet phase spectrum estimation.

Matrix Approach in Wavelet Analysis

In this paper, we give a survey of matrix approach in wavelet theory , and describe some related results which were obtained by ourselves.