A Novel Method for Pattern Recognition of GIS Partial Discharge via Multi-Information Ensemble Learning

Partial discharge (PD) is the main feature that effectively reflects the internal insulation defects of gas-insulated switchgear (GIS). It is of great significance to diagnose the types of insulation faults by recognizing PD to ensure the normal operation of GIS. However, the traditional diagnosis method based on single feature information analysis has a low recognition accuracy of PD, and there are great differences in the diagnosis effect of various insulation defects. To make the most of the rich insulation state information contained in PD, we propose a novel multi-information ensemble learning for PD pattern recognition. First, the ultra-high frequency and ultrasonic data of PD under four typical defects of GIS are obtained through experiment. Then the deep residual convolution neural network is used to automatically extract discriminative features. Finally, multi-information ensemble learning is used to classify PD types at the decision level, which can complement the shortcomings of the independent recognition of the two types of feature information and has higher accuracy and reliability. Experiments show that the accuracy of the proposed method can reach 97.500%, which greatly improves the diagnosis accuracy of various insulation defects.     

基于改进EEMD的卷积神经网络滚动轴承故障诊断

集合经验模态分解(EEMD,ensemble empirical mode decomposition)对信号进行分解,得到的模态函数(IMF,Intrinsic model function)在2端点存在严重的发散现象,如果将分解结果直接应用到故障诊断系统中,会导致诊断的准确率下降。首先将支持向量机(SVM,support vector machine)和EEMD算法结合进行信号分解,并利用仿真信号进行可靠性分析;其次对SVM(support rector machine)-EEMD分解的分量进行选择后再分解并构建能量向量,最后和卷积神经网络结合,构建滚动轴承故障诊断模型并通过实验验证。结果表明,改进EEMD算法可以有效缓解端点发散问题,构建的故障诊断模型提高了故障诊断精度。     

Structural Prediction of Membrane Protein: Application to Known Structures

Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological fumction and its interaction process with other molecules in a biological system. Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. In this study, on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou, the value of approximate entropy (ApEn) of the query membrane protein was used to integrate the complementary information. By fusing fifteen powerful individual fuzzy K-nearest neighbor (FKNN) classifiers, an ensemble classifier was presented.Each basic classifier was trained in PseAA composition of membrane protein sequences with different parameters. The results of experiments demonstrate it is efficient for the structural prediction of membrane proteins.     

基于I-ELM的飞机复合材料结构损伤识别研究

针对飞机复合材料结构损伤难以有效识别问题,本文提出一种基于增量型极限学习机(Incremental Extreme Learning Machine,I-ELM)的飞机复合材料结构损伤识别新方法。首先对某型机用复合材料层合板进行冲击,而后对其进行疲劳拉伸试验,通过优化布局在复合材料层合板上的光纤光栅传感器募集应变信息,并对其进行预处理。采用互补总体平均经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)方法对募集的应变信息进行自适应分解,得到多个本征模态分量(Intrinsic Mode Function,IMF),计算各阶IMF分量的样本熵,通过核熵成分分析(Kernel Entropy Component Analysis,KECA)方法对样本熵进行特征融合,构建融合特征向量,采用融合特征向量建立基于I-ELM损伤识别模型,通过实验对损伤识别模型的有效性进行了验证,并与所构建的BP的损伤识别模型的识别结果进行了比较。结果表明,该方法能有效对飞机复合材料结构损伤进行识别,具有很好的应用前景。     

基于CEEMD-BiLSTM-RFR的短期光伏功率预测

由于短期光伏预测中气象因素的时间尺度不同,直接分析其对光伏功率的相关性,易忽略时间尺度的影响,进而导致预测模型误差。为提高光伏功率预测精度,构建了预测模型。首先,利用互补集合经验模态分解(complementary empirical mode decomposition, CEEMD)将光伏序列进行分解,得到在不同时间尺度上的光伏分量;然后,通过Pearson相关系数分析各光伏分量与空气温度、太阳辐射度、风速、风向和空气湿度的关系,对于强相关分量建立关于气象因素的随机森林回归(random forest regression, RFR)预测模型,弱相关分量直接通过双向长短期记忆网络(bidirectional long short-term memory neural network, BiLSTM)进行预测;并将预测求和输出。通过安徽省蚌埠市光伏电站7月实测数据进行验证,实验结果表明,所提预测模型CEEMD-BiLSTM-RFR相比传统预测模型有较好的预测精度。     

IP黑名单关联聚类算法对恶意簇检测的优化研究

互联网中复杂的恶意活动都是由IP地址集群共同执行的,通过处理在网络中收集的数据来寻找恶意IP簇成为重要的研究方向。提出一种IP黑名单关联聚类算法(IPBACA),首先,构建IP-IP无向图;然后,利用测量统计相关性来测量IP黑名单与IP的相关性,并使用给定的IP黑名单来找到最佳阈值得出IP簇,判断其标准化残差是否达标;最后,识别出具有高精度的恶意簇。仿真结果表明,对比ICAMO算法,CAIIB算法和DABR算法,本文提出的IPBACA算法在精确率、召回率、F1指标和归一化互信息等4个主要性能指标方面均有明显改善,显著提高了对检测恶意簇的检测能力。     

聚类分析在入侵检测中的应用

在对现有的入侵检测技术研究的基础上,着重对数据挖掘技术中的聚类分析方法在入侵检测领域中的应用进行了研究。通过分析网络中数据的特点,提出了一种基于改进的k-means算法的无监督二次聚类算法,并用入侵检测权威数据集KDD Cup1999作为实验数据将其实现,实验表明,该算法具有较高的检测率和较低的误检率。     

Biosensors based on gold nanoelectrode ensembles and screen printed electrodes

Gold nanowires were synthesized within polycarbonate membranes according to an electroless deposition method, obtaining nanoelectrode ensembles (NEEs) with special electrochemical features. NEEs were coupled with home-produced carbon graphite screen printed electrodes and the electrochemical properties of the original nanoelectrode ensemble on screen printed substrate (NEE/SPS) assembly has been tested for sensors application. Glucose oxidase has been used as model enzyme in order to verify the feasibility of disposable gold NEE/SPS biosensors. Finally, different immobilisation and electrochemical deposition techniques based on either self assembled monolayers of cysteamine (CYS) or amino-propyl-triethoxysilane (APTES) and conductive polyaniline (PANI) molecular wires were used. Spatial patterning of the enzyme on the polycarbonate surface and of PANI wires on gold nanoelectrodes was obtained. Possible direct electron transfer between the enzyme and the PANI modified gold nanoelectrodes has been evaluated.     

Exploring non-equilibrium statistical ensembles

Since Gibbs synthesized a general equilibrium statistical ensemble theory, generalizing the Gibbsian theory to non-equilibrium phenomena has been a dream for some theorists. However, the status of the theory for the non-equilibrium ensemble formalism is not as well established as the Gibbsian ensemble theory. In this work, we explore the formalism behind the non-equilibrium statistical ensemble theory based on the sub-dynamic kinetic equation originating from the Brussels-Austin school and develop...