基于馈线自动化与级差保护的配电线路故障特征量自识别方法

针对永久故障和瞬时故障的判断能力偏低、故障特征量的识别误差过大的问题,提出一种基于馈线自动化与级差保护的配电线路故障特征量自识别方法。确定引起配电线路故障的因素,在时域与频域上分析不同配电线路故障的馈线特征,并提取配电线路故障相数、配电线路电流衰减程度作为配电线路故障特征量,分析电流的衰减程度,根据分析结果得到配电线路故障相电压和电流。建立配电线路故障等效模型,通过三相电流与残余电流进行故障特征量识别,定义配电线路故障特征量,提取配电线路发生故障时的最大残余电压与残余电流,再与三相电压、三相电流进行比较,实现配电线路故障特征量自识别。实验结果表明,所提方法能够很好地判断永久故障和瞬时故障,降低判别误差。

从放电能量分布特征量识别PD类型的方法

研究了去噪后的局部放电信号特点 ,提出了从能量—相位分布图中提取局部放电特征量的方法。现场实测数据处理结果表明该方法有效、可行。

VoIP模式地空通信数据链信号特征量识别算法

地空通信数据链在地面与空中信息传递中具有重要作用,为准确分析数据链信息交换情况,提出基于粒子群和减法聚类的数据链信号特征量识别算法。将包络改变情况作为数据链信号划分依据,利用求平方谱策略提取信号载频、带宽和码元速度等基础特征。使用小波变换明确信号小波能量谱,引入能量聚点理念分析信号特征量性质,选择能量谱差别明显的频段,创建数据链信号特征量模型。融合粒子群和减法聚类方法搜索重建星座图,明确粒子最佳解与全局种群最佳解,计算星座图最佳减法聚类半径,输出最佳聚类为最终的信号特征量识别结果。实验结果表明,所提方法信号特征量识别精度较高,抗干扰性强,可实现快速准确的VoIP(Voice over Internet Protocol)模式地空通信数据链实时信息交互。

考虑系统振荡的具备识别潮流转移能力的广域后备保护研究

为有效预防距离Ⅲ段后备保护因潮流转移引起误动冲击电网,避免连锁跳闸事故的发生,基于PMU量测信息提出了一种具备识别潮流转移能力的广域后备保护方案。该方案充分考虑了系统振荡过程,引入了能反映潮流转移与各类型短路故障的明显差别的潮流转移识别特征量(Flow transfer identification characteristic,FTIC)的概念,详细地推导了系统功角从0°到180°变化的过程中分别发生潮流转移与各类短路故障时FTIC的不同取值范围,并以此为依据设置识别判据、整定识别延时,详细阐述了方案的实施流程。该方案能够实现无论系统振荡与否,发生潮流转移时及时闭锁距离Ⅲ段后备保护,并保证发生短路故障时使距离Ⅲ段元件继续开放。通过IEEE10机系统的仿真算例验证了所提方案的可行性和有效性。

矿用千米定向钻机动作识别方法

目前矿用千米定向钻机的行走、钻进等各项操作均由司钻工人手动操作实现,智能化水平低,且缺少对千米定向钻机动作类型与液压泵站振动状态二者关联性的研究,远程识别千米定向钻机动作类型困难。针对上述问题,提出了一种基于经验小波变换(EWT)和模糊C均值(FCM)聚类算法的矿用千米定向钻机动作识别方法。首先利用EWT方法分析千米定向钻机执行5种不同动作(千米定向钻机启动和动力头不带钻杆旋转、带钻杆旋转、带钻杆向前慢速钻进和带钻杆向前快速钻进)时液压泵站3个关键部位(电动机、液压泵和联轴器)的频率特征信息,分别选取每处振动特征最明显方向上的振动信号构成动作识别原信号组。然后结合EWT分解和相关系数选取规则提取动作识别原信号组中包含钻机动作信息的特征量,并确认不同特征量的权重,构建标准识别特征量。最后利用FCM聚类算法得到待识别动作特征量与5种动作识别标准特征量之间的隶属度,实现对千米定向钻机动作类型的智能识别。以ZYL-17000D型矿用千米定向钻机为研究对象,对基于EWT和FCM聚类算法的矿用千米定向钻机动作识别方法的可靠性进行实验验证,实验采集了电动机、液压泵、联轴器的轴向、水平径向、垂直径向等方向在5种动作下的振动数据,结果表明:钻机执行不同动作时,其电动机、液压泵和联轴器振动信号的经验小波函数表现出了不同的特征,其中液压泵轴向振动信号特征量的聚类性能最好,根据提取的特征量在不同动作下的差异性可实现对动作类型的识别。基于测试数据的动作识别结果表明,该方法能够有效识别千米定向钻机的动作类型,且在隶属度大于0.9的条件下,识别准确率达96.8%。

OFDM雷达信号子载波调制方式识别方法

针对多径信道下传统方法识别OFDM雷达信号子载波调制方式存在识别正确率较低,识别子载波调制方式不完备,判决门限不易确定等问题,提出一种新颖的OFDM雷达信号子载波调制方式识别方法。利用OFDM雷达信号的瞬时幅度绝对值标准偏差,实现子载波多进制正交振幅调制(MQAM)和多进制相位调制(MPSK)的类间识别,利用组合高阶累积量作为识别特征量,对MQAM和MPSK两类调制方式中的子类间进行分类识别,利用递归降价的方法实现子载波调制阶数M>16的MQAM调制方式的识别。仿真实验结果表明,该方法能够有效实现多径信道下OFDM雷达信号多种子载波调制方式的识别,且识别性能更优,可以识别更完备的子载波调制方式类型。

Support vector machines for emotion recognition in Chinese speech

Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.

Feature Extraction of Stored-grain Insects Based on Ant Colony Optimization and Support Vector Machine Algorithm

[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.

Early-stage Internet traffic identification based on packet payload size

In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.

Novel feature fusion method for speech emotion recognition based on multiple kernel learning

In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. Based on the global features, the local information of different kinds of features is utilized. Both the global and the local features are combined together. Moreover, the multiple kernel learning method is adopted. The global features and each kind of local feature are respectively associated with a kernel, and all these kernels are added together with different weights to obtain a mixed kernel for nonlinear mapping. In the reproducing kernel Hilbert space, different kinds of emotional features can be easily classified. In the experiments, the popular Berlin dataset is used, and the optimal parameters of the global and the local kernels are determined by cross-validation. After computing using multiple kernel learning, the weights of all the kernels are obtained, which shows that the formant and intensity features play a key role in speech emotion recognition. The classification results show that the recognition rate is 78. 74% by using the global kernel, and it is 81.10% by using the proposed method, which demonstrates the effectiveness of the proposed method.

Identification of indoor multi-component pollution gas aliasing peak based on JADE

Monitoring indoor harmful gas can obtain the infrared spectra of mixed harmful gases.Since the absorption bands of mixed gases overlap and their qualitative and quantitative analyses are not easy,feature extraction method based on joint approximative diagonalization of eigenmatrix（JADE）is proposed.By fully mining the hidden information of original data and analyzing higher-order statistics information of the data,each substance spectrum in the mixed gas can be accurately distinguished.In addition,a multi-dimensional data quantitative analysis model of the extracted independent source is established by using support vector machine（SVM）based on regular theory.The experimental results show that the correlation coefficients of the components of mixed gases is above 0.999 1by quantitative analysis,which verifies the accuracy of this feature extraction method.

An infrared human face recognition method based on 2DPCA

Aimed at the problems of infrared image recognition under varying illumination,face disguise,etc.,we bring out an infrared human face recognition algorithm based on 2DPCA.The proposed algorithm can work out the covariance matrix of the training sample easily and directly;at the same time,it costs less time to work out the eigenvector.Relevant experiments are carried out,and the result indicates that compared with the traditional recognition algorithm,the proposed recognition method is swift and has a good adaptability to the changes of human face posture.

Multi-modal face parts fusion based on Gabor feature for face recognition

A novel face recognition method, which is a fusion of muhi-modal face parts based on Gabor feature （MMP-GF）, is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved with a family of Gabor kernels, and then according to the face structure and the key-points locations, the calculated Gabor images were divided into five parts： Gabor face, Gabor eyebrow, Gabor eye, Gabor nose and Gabor mouth. After that multi-modal Gabor features were spatially partitioned into non-overlapping regions and the averages of regions were concatenated to be a low dimension feature vector, whose dimension was further reduced by principal component analysis （PCA）. In the decision level fusion, match results respectively calculated based on the five parts were combined according to linear discriminant analysis （LDA） and a normalized matching algorithm was used to improve the performance. Experiments on FERET database show that the proposed MMP-GF method achieves good robustness to the expression and age variations.

Trace elements spatial distribution characteristics,risk assessment and potential source identification in surface water from Honghu Lake,China

Five trace elements including Zn, Cu, Cd, Cr and As were investigated in surface water from ten typical sampling sites in Honghu Lake. The consequence indicated that all of the detected trace element levels were within the allowed standard of China’s safe water guideline. The hazard quotients （HQ） and the hazard index （HI） value levels of all the five heavy metals in all sampling sites did not exceed the acceptable risk limits of non-carcinogenic value through the selected assessment method. Pearson’s correlation analysis and principal component analysis （PCA） indicated that Zn and Cu mainly originated from the natural alluviation and non-point agricultural sources, whereas Cr and As were mainly derived from industrial effluents. Moreover, Cd mainly originated from both non-point agricultural and industrial pollution sources. In addition, cluster analysis （CA） implied that cluster 1 （including S3, S5, S6 and S10） was considered the set of high pollution sites and cluster 2 （including S4 and S9） was identified as the set of moderate pollution sites.

Experimental study of fatigue degree quantification for multi-feature fusion identification

A comprehensive quantification method of fatigue degree is proposed concerning subjective and objective quantifications.Using the fatigue degree test software,fatigue degree is objectively quantified by analyzing the reaction and operation abilities of drivers about traffic signals.By comparison experiment with that EEG signal based,multivariate statistical analysis and fusion identification based on BP neural network(BPNN) results show that the experimental procedure is simple and practical,and the proposed method can reveal the correlation between fatigue feature parameters and fatigue degree in theory,and also can achieve accurate and reliable quantification of fatigue degree,especially under the associated action of multiple fatigue feature parameters.

Fracture detecting based on Ant Colony Algorithm

Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.