基于LDA和小波分解的肺音特征提取方法

针对用小波分解提取肺音特征后特征向量维数较高的问题,提出了一种结合线性判别分析和小波分解的肺音特征提取方法。在该方法中,首先对肺音信号进行小波分解,然后将小波分解得到的小波系数转化成小波能量特征向量,接着使用线性判别分析法对该特征向量进行降维处理,得到新的低维特征向量,最后用SVM对低维特征进行识别。在实验中,选取了三种肺音信号:正常肺音、爆裂音、哮鸣音,用所提出的方法将8维的小波能量特征降为2维特征,在2维特征上进行了分类识别,并和降维之前的结果进行了比较,实验结果表明利用线性判别分析对小波能量特征降维后极大地提高了识别精度。同时,和其他几种典型的肺音特征提取方法进行了比较,实验结果表明结合线性判别分析与小波分解的特征提取方法得到了更高的识别精度。     

呼吸音信号的包络特征提取方法

针对时变宽带的呼吸音信号,在分析传统Hilbert变换方法提取包络的缺点基础上,提出基于复小波变换的呼吸音信号包络特征提取方法。选取Morlet复小波,以适当的尺度对预处理后的呼吸音数据进行变换得到包络,提取包络的统计量和能量作为特征,构造BP分类神经网络的输入矢量,经训练识别取得较好分类效果。研究表明该文的特征提取方法是行之有效的。     

基于优选小波基和模糊SOM网络的模拟电路故障诊断

为了解决模拟电路故障诊断中的特征提取困难并实现对模拟电路故障模式准确的分类,提出一种优选小波基、模糊理论和自组织特征映射网络(SOM,self-organizing feature map)相结合的模拟电路故障诊断方法.该方法首先对模拟电路故障响应信号进行小波分解、提取能量值、均值和方差组成输入特征向量,同时采用余弦分离度评价小波变换在不同小波基函数下获取故障特征的有效性,据此选择余弦分离度最小的小波基分解的特征向量输入到自组织特征映射网络进行故障分类.仿真实验表明,利用余弦分离度选择的最优小波基能有效提高模拟电路故障特征提取,模糊神经网络能对故障模式进行精确分类.     

最佳能量小波包技术在海洋水声信号处理中的应用

对随机时间序列的特征提取与聚类分析，提出了基于分类距离标准的小波包基能量方法。该方法应用小波包变换提取信号各子空间的能量，以能量分类距离标准选取最佳小波包基，最佳小波包基上距离系数大的能量作为特征值。实验结果表明此方法比固定尺度小波包能量法有着更好的分类效果，并且特征值维数低。     

基于小波包最优基的语音信号压缩方法

提出了基于方差准则的小波包最优基压缩方法.首先选择一个合适的小波函数及分解层次对语音信号进行小波包分解.然后基于方差准则确定小波包最优基,保留语音信号的重要特征,实现信号压缩.最后对小波包最优基中的小波系数进行量化、编码,以便于信号在信道中传输.在接收端进行译码,重构压缩后的语音信号,观察压缩效果.实验中对语音信号采用不同方法进行了压缩实验,验证了本文算法的可行性.实验结果表明,本文算法的搜索量小,易于实现,压缩效果比较明显.     

基于模糊剪枝的最优小波包基图像压缩算法

提出一种基于模糊剪枝的最优小波包基图像压缩算法。图像经完全小波包分解后,以每个小波包分解的高频系数占所有高频系数的比值及每个节点子女熵值与原节点熵值的比值作为模糊系统的2个输入;通过构造模糊规则,将小波包系数分为裁剪类和保留类,由保留类构造最优小波包基;根据最优小波包基,在不同的小波包分解层次上采用不同的阈值对图像进行压缩。仿真实验结果表明,该算法与比特-失真率优化算法和小波包最优树全局阈值压缩算法相比性能更优。     

基于Daubechies(dbN)的飞行器音频特征提取

本文主要研究了基于Daubechies(dbN)小波分析的飞行器音频特征提取。对特征提取采用db2小波,对信号进行了10层分解,提取出了各层的尺度系数,据此计算出了不同分解尺度上信号的能量,并进行了归一化。飞行器被动音频探测与识别技术作为一种传统的侦察手段,是空中预警系统的重要组成部分。基于两种战场目标的音频频谱特性采用基于小波理论的小波分解尺度细节信号时域能量的特征提取算法,利用这种算法获得较低维的特征向量。     

基于小波包的频带能量特征提取及智能诊断

提出一种基于小波包和BRF神经网络的智能故障诊断方法。对滚动轴承故障信号进行小波包分解,选择合适的小波基函数和尺度,将故障信号分解到八个不同的频段上,提取这八个频段上的能量信息,组成特征问量,作为RBF神经网络的输入;建立RBF神经网络模型并进行训练,对三种滚动轴承故障信号进行智能分类与识别。实验结果表明这种智能诊断方法有效可行。     

基于奇异值分解和小波包分析的液压泵振动信号特征提取方法

针对液压泵故障特征提取问题,提出了一种基于奇异值分解和小波包变换的液压泵振动信号特征提取方法.通过奇异值分解将噪声非均匀分布的液压泵振动信号正交分解为噪声分布相对均匀的分量,对各分量进行小波包阈值去噪,重构去噪后分量,对去噪后信号进行小波包分解,提取各频带能量特征.以齿轮泵为例,将该方法对齿轮泵的气穴故障、齿轮磨损和侧板磨损3种常见故障和正常状态的振动信号进行特征提取分析,结果表明,该方法可有效提取齿轮泵故障特征.     

煤岩声发射信号识别研究

针对煤岩破裂的声发射信号难以在复杂的噪声环境中识别的问题,提出了一种基于小波包分析和小波特征能谱系数分析的煤岩声发射信号识别方法。选取Symlets小波作为煤岩声发射信号分析的小波基函数,采用混合阈值算法对该信号进行去噪处理,提取出有用声发射信号,并采用Matlab软件分别对有用声发射信号和噪声信号的小波包分解进行仿真,得到两者的小波特征能谱系数和小波包特征向量。仿真结果表明,有用声发射信号特征向量的各级能量变化程度较大,噪声信号特征向量的能量变化较为稳定,从而可实现煤岩声发射信号的识别。     

Classification of power system disturbances using support vector machines

This paper presents an effective method based on support vector machines (SVM) for identification of power system disturbances. Because of its advantages in signal processing applications, the wavelet transform (WT) is used to extract the distinctive features of the voltage signals. After the wavelet decomposition, the characteristic features of each disturbance waveforms are obtained. The wavelet energy criterion is also applied to wavelet detail coefficients to reduce the sizes of data set. After feature extraction stage SVM is used to classify the power system disturbance waveforms and the performance of SVM is compared with the artificial neural networks (ANN).     

基于小波包分析和BP神经网络的中医脉象识别方法*

利用小波变换具有揭示信号时频两域细节和局部特征的能力,提出了将脉象信号的小波包分析和BP神经网络相结合以达到识别中医脉象的新方法。首先对脉象信号作三层小波包分解,利用小波包分解系数重构信号。然后计算第三层从低频至高频八个频带的信号能量,以此能量构造出脉象信号的特征向量送入改进的BP神经网络进行训练。大量样本的实验证实该方法具有识别正确率高、速度快的优点。     

Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition

The aim of this paper is to estimate the fault location on transmission lines quickly and accurately. The faulty current and voltage signals obtained from a simulation are decomposed by wavelet packet transform (WPT). The extracted features are applied to artificial neural network (ANN) for estimating fault location. As data sets increase in size, their analysis become more complicated and time consuming. The energy and entropy criterion are applied to wavelet packet coefficients to decrease the size of feature vectors. The test results of ANN demonstrate that the applying of energy criterion to current signals after WPT is a very powerful and reliable method for reducing data sets in size and hence estimating fault locations on transmission lines quickly and accurately.     

基于小波系数聚类的特征提取分类方法

神经网络是一种普遍采用的模式分类方法,当对样本的抽样数目较大时,神经网络结构复杂,训练时间激增,分类性能下降,针对这一问题,提出一种基于快速小波变换特征提取的分类方法。着先对婆婆以系数矩阵的每行进行聚类,表达重要频率范围内小波系数矩阵的行有较多的聚类数,从而大大减少了神经网络的输入数,而同时保留了有用的信息。特征提取后,采用小波系数的能量值特征量,应用径向基函数网络识别肺发出的各种不同的声音,实验证明：该方法有较高的识别率。     

Feature extraction from wavelet coefficients for patternrecognition tasks

An efficient feature extraction method based on the fast wavelet transform is presented. The paper especially deals with the assessment of process parameters or states in a given application using the features extracted from the wavelet coefficients of measured process signals. Since the parameter assessment using all wavelet coefficients will often turn out to be tedious or leads to inaccurate results, a preprocessing routine that computes robust features correlated to the process parameters of interest is highly desirable. The method presented divides the matrix of computed wavelet coefficients into clusters equal to row vectors. The rows that represent important frequency ranges (for signal interpretation) have a larger number of clusters than the rows that represent less important frequency ranges. The features of a process signal are eventually calculated by the Euclidean norms of the clusters. The effectiveness of this new method has been verified on a flank wear estimation problem in turning processes and on a problem of recognizing different kinds of lung sounds for diagnosis of pulmonary diseases     

基于小波变换的肌电信号识别方法研究

针对肌电信号的非平稳特性,采用小波变换方法对表面肌电信号进行分析,提取小波系数最大值构造特征矢量输入神经网络分类器进行模式识别,经过训练能够成功地从掌长肌和肱桡肌采集的两道表面肌电信号中识别展拳、握拳、前臂内旋、前臂外旋四种运动模式。实验表明,基于小波变换的神经网络分类方法所需的数据短、运算快,对于肌电假肢的控制具有良好的应用前景。     

Automatic recognition of sleep spindles in EEG via radial basis support vector machine based on a modified feature selection algorithm

This paper presents an application of a radial basis support vector machine (RB-SVM) to the recognition of the sleep spindles (SSs) in electroencephalographic (EEG) signal. The proposed system comprises of two stages. In the first stage, for feature extraction, a set of raw amplitude values, a set of discrete cosine transform (DCT) coefficients, a set of discrete wavelet transform (DWT) approximation coefficients and a set of adaptive autoregressive (AAR) parameters are calculated and extracted from signals separately as four different sets of feature vectors. Thus, four different feature vectors for the same data are comparatively examined. In the second stage, these features are then selected by a modified adaptive feature selection method based on sensitivity analysis, which mainly supports input dimension reduction via selecting the most significant feature elements. Then, the feature vectors are classified by a support vector machine (SVM) classifier, which is relatively new and powerful technique for solving supervised binary classification problems due to its generalization ability. Visual evaluation, by two electroencephalographers (EEGers), of 19 channel EEG records of six subjects showed that the best performance is obtained with an RB-SVM providing an average sensitivity of 97.7%, an average specificity of 97.4% and an average accuracy of 97.5%.