多通道三维视觉指导运动想象脑电信号特征选择算法

针对基于三维视觉指导的运动想象脑机接口多通道冗余信息较多、分类准确率差的问题,提出了一种基于小波包分解（WPD）—共空间滤波（CSP）—自适应差分进化（ADE）的模式脑电信号特征提取与选择分类方法。首先,对采集的多通道运动想象脑电信号进行WPD变化,划分出精细的子频带;然后,分别将WPD变换后的每个子空间作为CSP的输入,得到对应的特征向量;最后,使用ADE算法对特征向量进行选择,选择出用于分类的最佳特征子集。采用WPD-CSP-ADE模式进行特征提取与选择,较经典的WPD-CSP方法在分类正确率、特征个数方面有着更好的表现。同时,所提算法分类性能明显优于遗传算法、粒子群算法。实验结果表明,WPD-CSP-ADE方法能够有效地提高分类正确率,同时减少了用于分类的特征个数。     

一种基于WPT和LVQ神经网络的手部动作识别方法

针对表面肌电信号(SEMG)的手部动作识别,提出一种采用小波包变换(WPT)和学习向量量化(LVQ)算法的神经网络分类器。对SEMG信号进行基于熵准则的最优小波包基分解得到各个节点分解系数,计算信号各个节点相应子频段的系数能量,归一化处理后的特征向量输入LVQ神经网络,实现基于SEMG的手部动作识别。实验结果表明,采取两路SEMG信号,该分类器能有效识别伸腕、屈腕、展拳和握拳4种动作模式,达到96%的识别率,能可靠应用于2个自由度肌电假手的控制。     

基于模糊准则的小波特征选择在人脸识别中的应用

提出一种基于模糊准则的小波特征选择方法来实现人脸识别．首先，利用模糊准则得到最优小波包分解；其次，亦利用模糊准则对最优小波包分解中特征（小波系数）的分类能力进行评价并排序；再次，选择鉴别能力强的特征并将它们输入到EFM模型以实现降维，并使用基于最小二乘误差的线性鉴别函数实现分类．人脸识别实验结果表明基于模糊准则的小波特征选择方法的识别率要高于主元分析（PCA）算法．     

基于ERS/ERD的二级共空间模式的运动想象脑电信号特征提取

针对多类运动想象EEG信号在脑-机接口方面存在分类识别率低和被试者差异性的问题,提出了一种基于ERS/ERD现象的二级共空间模式特征提取的方法。首先对全部导联进行特定频段的小波包降噪和分解;其次对分解系数重构后的信号以手(左、右)和脚(脚、舌)这二类进行一级共空间模式获取空间滤波器并对其采用2-范数筛选准则,提取权重系数较大的N个导联;然后以优化导联的投影矩阵对手与脚进行空间滤波后的信号分别作为原始信号进行二级空间模式特征提取;最后采用支持向量机进行分类。采用BCI2005Ⅲa中三位被试者的数据进行仿真验证,得到分类正确率最高达到92.55%。结果表明,该方法对EEG信号的特征提取具有较好的效果。     

Non-stationary power signal processing for pattern recognition using HS-transform

A new approach to time-frequency transform and pattern recognition of non-stationary power signals is presented in this paper. In the proposed work visual localization, detection and classification of non-stationary power signals are achieved using hyperbolic S-transform known as HS-transform and automatic pattern recognition is carried out using GA based Fuzzy C-means algorithm. Time-frequency analysis and feature extraction from the non-stationary power signals are done by HS-transform. Various non-stationary power signal waveforms are processed through HS-transform with hyperbolic window to generate time-frequency contours for extracting relevant features for pattern classification. The extracted features are clustered using Fuzzy C-means algorithm and finally the algorithm is optimized using genetic algorithm to refine the cluster centers. The average classification accuracy of the disturbances is 93.25% and 95.75% using Fuzzy C-means and genetic based Fuzzy C-means algorithm, respectively.     

Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i. e. , slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.     

多层导电结构电涡流扫描检测缺陷自动识别和分类技术研究

多层导电结构涡流检测中,缺陷的自动识别和分类是急需解决的重要问题.提出了一种新的缺陷信号自动检测识别和分类方法,首先采用幅值中值预判和小波分析方法进行信号预处理,自动识别并提取包含缺陷的涡流检测信号片段;然后运用主分量分析法对含有缺陷的信号片段进行特征提取;接着构建最近均值、K近邻、BP网络和支持向量机四种分类器对缺陷信号进行分类;最后进行了实验研究,对多层导电结构三种形状缺陷的扫描检测信号进行识别和分类,验证了本文所提出方法的有效性,并比较了各分类器的性能,根据识别和分类错误率大小,可看出支持向量机分类器具有较好的鲁棒性和稳定性.     

Wear State Recognition of Drills Based on/（-means Cluster and Radial Basis Function Neural Network

Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective.     

A new optimum feature extraction and classification method for speaker recognition: GWPNN

Speech and speaker recognition is an important topic to be performed by a computer system. In this paper, an expert speaker recognition system based on optimum wavelet packet entropy is proposed for speaker recognition by using real speech/voice signal. This study contains both the combination of the new feature extraction and classification approach by using optimum wavelet packet entropy parameter values. These optimum wavelet packet entropy values are obtained from measured real English language speech/voice signal waveforms using speech experimental set. A genetic-wavelet packet-neural network (GWPNN) model is developed in this study. GWPNN includes three layers which are genetic algorithm, wavelet packet and multi-layer perception. The genetic algorithm layer of GWPNN is used for selecting the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the four different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet packet decomposition, wavelet packet decomposition – short-time Fourier transform, wavelet packet decomposition – Born–Jordan time–frequency representation, wavelet packet decomposition – Choi–Williams time–frequency representation. The wavelet packet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet packet decomposition and wavelet packet entropies. The multi-layer perceptron of GWPNN, which is a feed-forward neural network, is used for evaluating the fitness function of the genetic algorithm and for classification speakers. The performance of the developed system has been evaluated by using noisy English speech/voice signals. The test results showed that this system was effective in detecting real speech signals. The correct classification rate was about 85% for speaker classification.     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.     

Drill flank wear estimation using supervised vector quantization neural networks

Drill wear detection and prognosis is one of the most important considerations in reducing the cost of rework and scrap and to optimize tool utilization in hole making industry. This study presents the development and implementation of two supervised vector quantization neural networks for estimating the flank-land wear size of a twist drill. The two algorithms are; the learning vector quantization (LVQ) and the fuzzy learning vector quantization (FLVQ). The input features to the neural networks were extracted from the vibration signals using power spectral analysis and continuous wavelet transform techniques. Training and testing were performed under a variety of speeds and feeds in the dry drilling of steel plates. It was found that the FLVQ is more efficient in assessing the flank wear size than the LVQ. The experimental procedure for acquiring vibration data and extracting features in the time-frequency domain using the wavelet transform is detailed. Experimental results demonstrated that the proposed neural network algorithms were effective in estimating the size of the drill flank wear.     

Power signal classification using dynamic wavelet network

A new approach to classification of non-stationary power signals based on dynamic wavelet has been considered. This paper proposes a model for non-stationary power signal disturbance classification using dynamic wavelet networks (DWN). A DWN is a combination of two sub-networks consisting of a wavelet layer and adaptive probabilistic network. The DWN has the capability of automatic adjustment of learning cycles for different classes of signals, for minimizing error. DWN models are specifically suitable for application in dynamic environments with time varying non-stationary power signals. The test results showed accurate classification, fast and adaptive learning mechanism, fast processing time and overall model effectiveness in classifying various non-stationary power signals. The classification result of the DWN has been compared with that of the probabilistic neural network (PNN).     

基于EMD和LVQ的信号特征提取及分类方法

针对非平稳、非线性、微弱信号难以分析和处理的特点,本文提出了一种基于经验模式分解和学习向量量化神经网络的信号处理和分类方法,并在生物信号处理领域(左、右手运动想象的脑电信号)进行了研究和应用.首先通过经验模式分解算法对脑电信号分解,然后选取主要固有模态函数分量并计算其绝对均值作为特征值,最后使用学习向量量化网络进行分类,并分别与支持向量机和误差反向传播神经网络分类算法进行了对比研究.实验结果表明,所提出的算法分类正确率达到了87％,相比于其余两种对比算法在特定的信号处理领域优越,具有一定的参考和研究价值.     

Wavelet packet energy, Tsallis entropy and statistical parameterization for support vector-based and neural-based classification of mammographic regions

This work develops a support vector and neural-based classification of mammographic regions by applying statistical, wavelet packet energy and Tsallis entropy parameterization. From the first four wavelet packet decomposition levels, four different feature sets were evaluated using two-sample Kolmogorov-Smirnov test (KS-test) and, in one case, principal component analysis (PCA). Feature selection was performed applying a hybrid scheme integrating non-parametric KS-test, correlation analysis, a logistic regression (LR) model and sequential forward selection (SFS). The top selected features (depending on the selected wavelet decomposition level) produced the best classification performances in comparison to other well-known feature selection methods. The classification of the data was carried out using several support vector machine (SVM) schemes and multi-layer perceptron (MLP) neural networks. The new set of features improved significantly the classification performance of mammographic regions using conventional SVMs and MLPs.     

运动想象脑电信号特征提取与分类算法研究

针对运动想象脑电信号特征提取困难,分类正确率低的问题,提出了利用小波熵进行特征提取并采用支持向量机（SVM）来分类的算法。计算运动想象脑电信号的功率,通过理论分析选择小波包尺度,对信号功率进行小波包分解并计算其小波包熵（WPE）,提取C3、C4导联的小波包熵插值组成特征向量,将特征向量作为分类器的输入送入支持向量机进行分类。采用国际BCI竞赛2003中的Graz数据进行验证,算法的最高分类正确率达97.56%。算法特征向量维数低、数据量小、分类正确率高,对运动想象脑电信号特征提取及分类的任务可以提供参考方法。     

基于多通道sEMG小波包分解特征的人手动作模式识别方法

为了满足主动康复训练和人机交互等复杂应用场景对多样性的人手运动模式识别需求,提出了一种基于多通道表面肌电信号sEMG小波包分解特征的人手动作模式识别方法。通过实验对比分析,确定了最佳采样布局方案,通过采集前臂表面肌电信号,设计了基于数字滤波器的肌电信号活动段自动标识算法,能快速准确完成样本动作标签的制作。以原始肌电信号的小波包分解系数作为特征向量训练分类器。通过对比不同隐含层节点数对分类器模式识别准确率的影响,最终确定BP神经网络模式分类器的所有结构参数。设计并训练完成了BP神经网络人手运动模式分类器。对9种手部运动的平均识别率达到93.6%,计算时间小于150ms。     

基于WT和LVQ网络的多姿态人脸识别

提出了基于小波变换和学习矢量量化网络相结合的新方法进行人脸识别。小波变换具有良好的多尺度特征表达能力，能将图像的大部分能量集中到最低分辨率子图像，可以很好地对图像降维和表征人脸图像的特征。LVQ算法是在有教师状态下对竞争层进行训练的一种学习算法。LVQ网络结构简单，但却表现出比BP网络更强的有效性和鲁棒性。实验表明该方法对表情和姿态变化的人脸具有良好的分类性能和识别效率。     

Single-channel SEMG using wavelet deep belief networks for upper limb motion recognition

Surface electromyography (SEMG) has been widely used in different fields such as human machine interaction and motion recognition. A hybrid classification model based on singular value decomposition (SVD) and wavelet deep belief networks (WDBN) is firstly proposed in this paper, which allows the machine to recognize the single-joint motions of upper limb by using one channel. In this experiment, the three-joint SEMG signals of upper limb are respectively recorded through different two channels, which are employed for subsequent comparison to obtain the best single-channel of each joint. Afterwards, the collected raw signals are enhanced by SVD processing. Wavelet function is applied to replace sigmoid function as activation function for feature learning, and the spectrum signals processed by fast Fourier transform (FFT) are input to WDBN model. The results demonstrate that the recognition rates of three joint movements can be up to 100% by SVD-WDBN method, which is much better than support vector machine (SVM), back propagation (BP) neural network and extreme learning machine (ELM) model. The proposed method makes it more possible to control wearable devices with different single-channel SEMG signals, thereby the work efficiency of smart wearable devices can be improved, as well as the complexity of operations between human and machine can be reduced.     

基于模式识别的传感器故障诊断

为满足模式识别故障诊断算法的鲁棒性要求，在小波包分解提取特征向量的基础上，提出了有监督模式分类与无监督模式分类相结合的故障诊断方法．利用小波包分解提取各个频带的能量作为特征向量；采用LVQ神经网络作为有监督的模式分类器进行故障诊断；运用无监督的减法聚类方法对新型故障模式进行辨识．最后，通过动力系统管路流量传感器数据对算法进行检验，验证了所提出方法的实用性和有效性．     

基于小波和神经网络的异步电机转子故障诊断方法研究

基于小波包变换的频率划分特性．对定子电流的Park矢量模信号进行小波包分解，建立了转子断条的故障特征矢量，准确地提取了转子断条故障的特征信息．克服了传统基于FFT分析方法难以提取故障特征频率分量的难点，结合BP神经网络非线性映射及分类识别的优点，将BP神经网络应用于电机转子断务故障的识别，实验结果表明，该方法可实现转子断条故障的可靠诊断。