电压凹陷特征量检测算法研究

对于电压凹陷的三大特征量幅值、持续时间和相位跳变的准确检测是电压质量评估与抑制干扰首先要解决的重要课题。介绍了常用的凹陷特征量的有效值计算法、峰值电压法、基波电压分量法和单相电压变换平均值法 ,并以单相电源为参考电压采用 6 0°延时方法构造了一个虚拟的三相系统 ,借助dq变换将三相电压变换到d q轴 ,提出了有实用意义的d q分解平均值法和d q分解低通滤波法 ,可对单相电压凹陷的三大特征量进行瞬时计算。通过计算机仿真 ,对各种方法的适用性进行了详细的对比分析 ,结果表明本文所提方法具有较好的跟踪特性 ,适合于电压凹陷分析与动态电压恢复器 (DVR)的实时检测应用。

电网单相电压暂降特征量检测的二点算法

基于求导运算的单相电压暂降特征量检测算法精度受采样频率影响。为此,利用相邻两个时刻的电压采样值构造方程,提出了一种二点检测算法。通过相应增广矩阵,分析了算法的唯一解、无穷解和无解条件。建立了算法Simulink仿真模型,并与传统求导检测法及αβ变换求导检测法进行了对比。仿真结果表明三种算法的检测时间相同,但二点检测算法避免了导数运算造成的误差,检测结果精度更高,且不受采样频率影响,可有效降低电压暂降检测装置对采样频率及运算速率的要求,适合应用于检测速度要求高、谐波含量低的场合。

基于特征检测量的XLPE电缆绝缘老化寿命预测方法

针对XLPE电缆绝缘老化影响电力系统稳定运行的问题,以绝缘状态检测项目为基础,提出基于多个特征检测量的偏最小二乘(PLS)老化时间预测模型。首先针对现有的数据样本较小及模型中存在的多重共线性问题,引入最小二乘支持向量回归机(LSSVR)优化模型主成分得分向量;然后利用最新得分向量建立LSSVR-PLS老化时间预测模型;最后利用回归参数T检验法对比检验了模型非线性处理能力,对杭州某区域多根110 kV XLPE电缆样品进行预测分析,结果表明改进模型适用于电缆检测量小样本数据的处理,能够消除原始模型存在的多重共线性问题,并且具有更高的预测精准度,对电缆的运维及电网改造具有重要的指导意义。

一种基于循环谱的突发信号盲检测算法

突发信号盲检测在非合作通信中至关重要。待检测信号特征量的构造与突发信号段起止点的精确捕获是突发信号盲检测技术的关键。文中提出了一种基于循环谱的突发信号盲检测算法,算法首先以循环谱循环频率截面的相对方差作为待检测信号的特征量,该特征量具有优良的抑制突发强噪声干扰性能;然后利用特征量的方差进行信号存在性判决;最后采用基于特征量峰值的突发信号段起止点搜索算法,避免了设置检测门限带来的信噪比估计、噪声功率估计等问题,同时可在一定的检测正确率条件下保证起止点的捕获精度。仿真结果表明,该算法能有效抑制突发强噪声干扰,信号存在性判决准确率高,而且在误差控制要求较严格条件下的检测精度优于双窗能量法。

舰船地震波信号的滑动功率谱实时检测算法

为提高低信噪比环境下的地震波信号检测概率,结合自适应线谱增强技术,基于滑动功率谱提出一种新的舰船地震波信号实时检测算法。首先利用自适应线谱增强技术对信号进行处理,然后对处理后的信号进行功率谱估计,选取一段频带内的平均功率作为检测特征量,并设计相应的自适应检测阈值算子,最后根据一定的检测规则完成对舰船地震波信号的实时检测。实测数据和仿真数据表明,该检测算法能够有效完成低信噪比的目标检测,在信噪比为-15 dB时,检测概率达到93%。

Phishing detection method based on URL features

In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.

Face mask detection algorithm based on HSV+HOG features and SVM

To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.

Adaptive partitioning PCA model for improving fault detection and isolation

In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.

Blur Invariant Image Forgery Detection Method Using Local Phase Quantization

With the rapid development of powerful image, editing software makes the forgery of the digital image easy. Researchers proposed methods to cope with image authentication in recent years. We proposed a passive image authentication technique to determine the copy move forgery that copied a part of an image and pasted it on the other region in the same image. First, the method divides the image into overlapping blocks. It uses LPQ （local phase quantization） to label each block. The column average value of labeled blocks constitutes the feature vector for the block. Similarity among the feature vectors gives a clue about the forgery. Local phase quantization has not been used to detect copy move forgery in the literature before. Experimental results show that, the method has higher accuracy ratios and lower false negative values under blurring operation at high levels compared to other methods. Our method can also detect multiple copy move forgery.

Study of partial discharge characteristics at repetitive square voltages based on UHF method

Due to overvoltage produced by inverter output,inverter-fed motor insulation systems often experience fast electrical aging process,especially when partial discharge(PD) is incepted.Before putting into use,the PD detection should be performed on inverter-fed motors at repetitive square voltages to avoid the PD caused insulation deterioration when the motors are collected to inverters having specific characteristics.However,unlike PD tests at AC/DC voltages proposed in IEC 60270,the PD detection at repetitive square voltages is much more complex because of serious interference generated by impulse generator.To solve the problem,ultra-high frequency(UHF) method seems recommendable for its preferable signal-to-noise ratio(SNR).The chief aim of this study is to investigate PD pulse and statistical characteristics of turn-to-turn insulation for inverter-fed traction motors.A square-shaped Archimedes antenna,specially designed for the PD detection at repetitive square voltages of fast rise times,was used to perform PD tests on turn-to-turn insulation models.Time and frequency analysis results indicate that energy component of generator disturbance and PD pulses are mainly distributed in the 0-0.5 GHz and 0.6-1.5 GHz range,respectively.Based on the results,suitable filter was designed for power disturbance suppression.Additionally,resorting to the sensor unit(i.e.antenna and filter) and the PD test system,the PD statistical features at square voltages of different frequencies were obtained.Experimental results show that higher frequency will give rise,statistically,to PD of lower magnitudes distributing at smaller phases.A reasonable interpretation of this phenomenon was presented.Lastly,according to the PD statistical features,some suggestions for the PD detection system design,generator parameter optimization and the PD pulse extracting were given.The results of this work would be beneficial to the increase of the sensitivity when performing the PD detection on insulation systems for inverter-fed motors at repetitive square voltages and thus,improving the reliability of inverter-fed motors.

Applications and challenges for single-bacteria analysis by flow cytometry

Flow cytometry(FCM)is a powerful technique for single-bacteria analysis via simultaneous light-scattering and fluorescence measurements.By offering high-throughput,quantitative,and multiparameter analysis at the single-cell level,FCM has gained an increased popularity in microbiological research,food safety monitoring,water quality control,and clinical diagnosis.Here we will review the recent applications of flow cytometry in areas such as(1)total bacterial cell count,(2)bacterial viability analysis,(3)specific bacterial detection and identification,(4)characterization of physiological changes under environmental perturbations,and(5)biological function studies.Nevertheless,despite these widespread applications,challenges still remain for the detection of small sizes of bacteria and biochemical features that cannot be brightly stained via fluorescence.Recent improvement in FCM instrumentation will be discussed,and particularly the development of high sensitivity flow cytometry for advanced analysis of single bacterial cells will be highlighted.