多类分类SVM在电能质量扰动识别中的应用

结合傅里叶变换良好的幅频特性、小波变换良好的时频特性和支持向量机优秀的统计学习能力,采用多类分类支持向量机进行电能质量扰动的分类识别。对电压骤升、电压骤降、电压中断、谐波、电压波动、暂态振荡、瞬时脉冲、频率偏差等八种常见电能质量扰动进行数学建模,利用傅里叶变换和小波变换对产生的样本波形进行特征提取,将特征量输入到osu_svm进行电能质量扰动多类分类。算例表明该方案具有识别正确率高,训练样本数少,训练时间短,实时性好,对噪声不敏感等优点,是电能质量扰动识别的有效方法。     

基于支持向量机的动态电能质量扰动分类方法

将支持向量机SVM(SupportVectorMachine)引入到动态电能质量分类问题中。在Matlab中编程建立了谐波、电压暂升、电压跌落、瞬时中断、电压波动、瞬变6种常见动态电能质量扰动数学模型,利用傅里叶变换和小波变换对产生的样本波形进行特征提取,产生训练和测试样本。给出了利用LIBSVM解决电能质量扰动分类问题的步骤,并根据分类结果对影响分类效果的参数进行了分析。对训练好的支持向量分类器进行测试,效果良好,当采用C-SVC,RBF核时调整参数可以得到最优分类效果,最高分类率可达到96.67%。     

基于STFT变换和DAGSVMs的电能质量扰动识别

提出了一种基于短时傅里叶变换和DAG(Directed Acyclic Graph)支持向量机的电能质量扰动检测和识别方法.将扰动信号通过Blackman窗短时傅里叶变换,得出时域最大幅值向量,然后把它作为特征向量输入到DAG支持向量机来实现电能质量扰动类型的识别.其中,时域最大幅值向量不仅能反映各种扰动的特征,还能显示电压突升、电压暂降、电压中断和暂态振荡等扰动的发生时刻和持续时间.仿真测试表明,该方法能有效识别各种电能质量扰动,而且识别正确率高,训练时间短,实时性能较好.     

基于STFT变换和DAGSVMs的电能质量扰动识别

提出了一种基于短时傅里叶变换和DAG(Directed Acyclic Graph)支持向量机的电能质量扰动检测和识别方法。将扰动信号通过Blackman窗短时傅里叶变换,得出时域最大幅值向量,然后把它作为特征向量输入到DAG支持向量机来实现电能质量扰动类型的识别。其中,时域最大幅值向量不仅能反映各种扰动的特征,还能显示电压突升、电压暂降、电压中断和暂态振荡等扰动的发生时刻和持续时间。仿真测试表明,该方法能有效识别各种电能质量扰动,而且识别正确率高,训练时间短,实时性能较好。     

电能质量复合扰动分类识别

电能质量扰动的分类分为信号特征提取和分类器2个阶段,采用S变换和支持向量机构造电能质量复合扰动的分类识别方案.利用S变换进行扰动信号特征提取,构造支持向量机静态分类树,再通过基于Mercer核的聚类方法对静态分类树进行动态扩展,形成动态分类树,实现对复合扰动的识别.给出了电能质量复合扰动分类算法的4个步骤:构建静态分类树;用基于Mercer核的聚类方法进行聚类分析;构建动态分类树;对新发现的扰动确定其具体类型,并给其命名.算例表明该方法不仅可以有效分类识别电压突降、电压突升、电压中断、暂态振荡、电压尖峰、电压缺口和谐波等7种电能质量扰动,还可以识别由其组合而成的电能质量复合扰动.     

基于S变换的电能质量扰动支持向量机分类识别

采用s变换和支持向量机进行电能质量扰动的分类识别。作为连续小波变换和短时傅立叶变换的发展，S变换引入了宽度与频率成反向变化的高斯窗，具有与频率相关的分辨率。由于S变换具有良好的时频特性，因而非常适合于进行电能质量扰动信号特征提取。首先通过S变换进行扰动信号特征提取，然后构造支持向量机分类树进行扰动分类。算例表明该方案具有分类准确率高，对噪声不敏感，训练样本少等优点，是电能质量扰动识别的有效方法。     

基于多标签Rank-WSVM的复合电能质量扰动分类

该文提出一种多标签排位小波支持向量机(rank wavelet support vector machine,Rank-WSVM),并将其应用于电能质量复合扰动分类中。Rank-WSVM将小波技术与多标签排位支持向量机(Rank-SVM)结合,利用小波的优良特性提高分类器的整体性能。首先,对电能质量扰动信号进行离散小波分解,计算 Tsallis 小波熵作为特征向量;然后利用所提出的 Rank-WSVM 多标签分类器进行分类。仿真结果表明,在不同噪声条件下,该方法有效改善了Rank-SVM的分类性能,可有效识别电压暂降、电压暂升、电压短时中断、脉冲暂态、振荡暂态、谐波和闪变等电能质量扰动及其组合而成的复合扰动。     

最小二乘支持向量机在电能质量扰动分类中的应用

提出一种基于最小二乘支持向量机和小波包分解的电能质量扰动分类方法.对正常电压和几种常见电能质量扰动(电压骤升、电压骤降、电压中断、暂态脉冲、暂态振荡、谐波和电压闪变)进行小波包分解,提取各终节点小波包系数的标准偏差作为特征向量;然后,用自适应优化算法对最小二乘支持向量机进行优化:最后,利用基于优化参数和最小输出编码的最小二乘支持向量机进行分类和识别.与BP神经网络分类方法相比,该方法能克服训练时间较长、容易陷入局部最小等问题,具有较快的训练速度和较高的分类准确率,在样本数较小时仍取得较好的效果.仿真实验验证了该方法对扰动分类的有效性.     

基于双密度双树小波变换的电能质量扰动识别方法

针对电能质量扰动的识别问题,提出一种基于双密度双树小波变换(DD-DT DWT)小波熵和支持向量机的扰动信号识别方法。该方法首先对电能信号进行DD-DT DWT变换,然后分别提取其小波能量熵和小波系数Shannon熵以描述不同扰动信号的特征,最后采用二元树结构支持向量机分别对提取的两类小波熵特征向量进行分类。仿真实验表明:所提出的基于DD-DT DWT小波熵的特征提取方法能有效识别常见的8种扰动信号,并具有正确识别率高及噪声鲁棒性强的优点。     

基于最小二乘支持向量机的电能质量扰动分类方法

提出一种基于最小二乘支持向量机和小波包分解的电能质量扰动分类方法。对正常电压和几种常见电能质量扰动(电压骤升、电压骤降、电压中断、暂态脉冲、暂态振荡、谐波和电压闪变)进行小波包分解,提取各终节点小波包系数的标准偏差作为特征向量;然后,用自适应优化算法对最小二乘支持向量机进行优化;最后,利用基于优化参数和最小输出编码的最小二乘支持向量机进行分类和识别。与BP神经网络分类方法相比,该方法能克服训练时间较长、容易陷入局部最小等问题,具有较快的训练速度和较高的分类准确率,在样本数较小时仍取得较好的效果。仿真实验验证了该方法对扰动分类的有效性。     

基于聚类分类算法的IGBT健康状态分类研究

绝缘栅双极型晶体管(IGBT)是功率变流器中最常用,也是故障率最高的元器件,因此其健康状态分类评估极为重要.文中基于聚类分类算法建立了IGBT模块的健康状态分类评估模型.首先根据聚类分类原理,简述了IGBT模块状态分类模型建立的步骤.然后以饱和压降和短路电流作为老化特征量,分析了老化过程特征量的变化趋势.最后搭建了IGBT模块健康状态分类评估模型,基于已有的数据集对模型进行了检测,模型分析结果与测试结果基本一致,验证了健康状态分类评估模型的准确性.     

一种改进的KNN文本分类算法

当今大数据时代,文本数据占相当大的比重,作为有效管理和组织文本数据的方法,分类逐渐成为关注的热点。KNN是一种经典的分类算法,针对其分类速度和分类精度无法同时兼顾的不足,采用改进的K Medoids聚类算法裁剪对KNN分类贡献小的训练样本,从而减少KNN相似度的计算量,并定义代表度函数有差别地处理测试文本的K个最近邻文本,以提高KNN的分类精度。实验结果表明,改进后的方法在分类速度上和分类精度上均有明显地提高。     

Detection and classification of vehicles

This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user     

基于S变换和分类树的电网暂态电能质量扰动分类辨识

针对暂态电能质量的检测分析,分别在强弱两种噪声背景下运用S变换的不同方法对暂态多扰动信号进行定位检测.对于暂态多扰动的分类辨识,运用了基于S变换和分类树相结合的暂态电能质量多扰动分类辨识方法,首先运用S变换对暂态多扰动信号进行时频分析,然后提取扰动信号的特征量,最后生成用于对暂态多扰动信号进行分类的决策树分类辨识方法,以此来实现对暂态多扰动信号的分类辨识.仿真计算结果表明,该方法对暂态多扰动信号能够进行有效的分类辨识,准确度高且抗噪能力强.     

基于S变换和分类树的电网暂态电能质量扰动分类辨识

针对暂态电能质量的检测分析,分别在强弱两种噪声背景下运用S变换的不同方法对暂态多扰动信号进行定位检测.对于暂态多扰动的分类辨识,运用了基于S变换和分类树相结合的暂态电能质量多扰动分类辨识方法,首先运用S变换对暂态多扰动信号进行时频分析,然后提取扰动信号的特征量,最后生成用于对暂态多扰动信号进行分类的决策树分类辨识方法,...     

Chromosome classification using backpropagation neural networks

The feasibility of an artificial neural network as a chromosome classifier was examined in this study, using the relative length, the centromeric index, and the density distribution of G-banded chromosome as feature vectors. The two-layer neural network trained with the error backpropagation training algorithm showed good potential in classification of Giemsa-banded human chromosomes. The minimum classification error was obtained with the configuration that had 27 input nodes and 24 PEs in the hidden layer. However, this study also showed some problems. Only two experiments, which had 25 and 50 density distribution samples, respectively, were carried out, due to the long computation time of the backpropagation neural network. Also, the centromere finding algorithm used in this study could not apply to telocentric chromosomes (group D and group G) because of their very small short arms; their centromere locations were determined manually. The algorithm must be modified so that it can be applied to all types of chromosomes to reduce the preprocessing time. Better training algorithms to reduce training time are needed. The error backpropagation training algorithm requires very long training times. Next, finding the optimal number of input nodes that gives the minimum classification error requires a trial and error experiment. Finally, other chromosome features that reduce the classification error need to be examined     

Medical image matching-a review with classification

A classification scheme for multimodal image matching is considered. The scope of the classification is restricted to methods that register data after acquisitions. The classification scheme may be used for any modality; not only for (2-D) projection images and (3-D) tomographic images, but also for other signal modalities that provide spatial insight into function or anatomy, e.g., EEG (electroencephalography) or MEG (magnetoencephalography) and for the real physical patient. The available literature on image matching is discussed and classified     

A classification of multiple-source feedback filters

A family of two-amplifier active-RC filters having a single positive and/or negative-feedback path is identified and evaluated. Four distinct categories emerge with respect to the input summing-amplifier arrangement. These are embedded in buffered, differential and allpass-pair networks to reveal many familiar and new configurations. the selectivity and sensitivity to gain-bandwidth product are then presented in general expressions capable of describing the performance of all such circuits. Conditions for zero first-order sensitivities and tuning flexibility are then developed.     

基于VOLL定价思想的用户重要性分类研究

为解决电力系统应对突发事件时的用户限电顺序问题,借鉴负荷损失定价法VOLL(Value ofLoad Loss)的思想,并将其经济的思想扩展到安全、政治和环境,构建了以安全、政治、环境、经济等4个指标为一类指标的电力用户重要性分类指标体系,并且根据所构建指标体系的特点和所选指标的数据特性.结合层次分析法和三角模糊数原理.采用改进的模糊层次分析法确定评价指标的权重.给出了一个电力用户重要性分类的算例,示范电力用户重要性分类的使用方法.最后.介绍了采用熵值法动态调整电力用户重要性指标权重.     

Modified center-based feature line classification approach

A novel classification approach called modified center-based feature line (MCFL) is proposed to reduce the computational cost of the nearest feature line (NFL) and maintain the advantages of NFL. Unlike NFL, MCFL defines a different type of feature line and utilizes both the query point’s local information and corresponding class-global information in training set. In experiments provided, the comparisons with the nearest neighbor (NN), NFL, and other NFL-refined approaches show that the computation time of MCFL can be shortened dramatically with less accuracy decreases. MCFL proposed is probably a better choice for the classification application tasks of large-scale dataset.