Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.

Interpersonal Perception in Virtual Groups: Examining Homophily, Identification and Individual Attraction Using Social Relations Model in Network

With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.

A fast automatic identification method for seismic belts based on distance correlation and its earthquake case

Earthquake prediction practice and a large number of earthquake cases show that anomalous images of small earthquake belts may appear near the epicenter before strong earthquakes.Through the research of earthquake cases,researchers have a relatively consistent method to determine the clarity of an identified seismic belt,but there is still a lack of method on seismic belt identification from the distribution of scattered points.Due to the complexity of exhaustive algorithm,the rapid automatic identification technique of seismic belts has been progressing slowly.Visual recognition is still the basic method of seismic belt identification.Based on the algorithm of distance correlation,this paper presents a fast automatic identification method of seismic belts.The effectiveness of this method was proved by 100 random earthquakes and an example of seismic belts of magnitude 4.0 before the 2005 Jiujiang M5.7 earthquake.The results show that:①the automatic identification of seismic belts should first identify the"relational earthquake",then identify the"suspected seismic belt",and finally use the criterion of seismic belt clarity to determine;②random earthquakes and real earthquakes identification results show that the distance correlation method can realize the fast automatic identification of seismic belts by computer.

Damage Identification in Beam-Type Structures Using Pseudo Strain Energy Density and Grey Relation Coefficient

Based on pseudo strain energy density (PSED) and grey relation coefficient (GRC), an index is proposed to locate the damage of beam-type structures in time-domain. The genetic algorithm (GA) is utilized to identify the structural damage severity of confirmed damaged locations. Furthermore, a systematic damage identification program based on GA is developed on MATLAB platform. ANSYS is employed to conduct the finite element analysis of complicated civil engineering structures, which is embedded with interface technique. The two-step damage identification is verified by a finite element model of Xinxingtang Highway Bridge and a laboratory beam model based on polyvinylidens fluoride (PVDF). The bridge model was constructed with 57 girder segments, and simulated with 58 measurement points. The damaged segments were located accurately by GRC index regardless of damage extents and noise levels. With stiffness reduction factors of detected segments as variables, the GA program evolved for 150 generations in 6 h and identified the damage extent with the maximum errors of 1% and 3% corresponding to the noise to signal ratios of 0 and 5%, respectively. In contrast, the common GA-based method without using GRC index evolved for 600 generations in 24 h, but failed to obtain satisfactory results. In the laboratory test, PVDF patches were used as dynamic strain sensors, and the damage locations were identified due to the fact that GRC indexes of points near damaged elements were smaller than 0.6 while those of others were larger than 0.6. The GA-based damage quantification was also consistent with the value of crack depth in the beam model.

A Study On the Structural Dimension of Middle Class Relational Identification in Chinese Context

Through in-depth interviews,this study makes an exploratory study on the dimension of the middle class relational identification in Chinese context by using the grounded theory.It is mainly composed of 4 dimensions:relational orientation,relational adaptation,relational evaluation and relational development.According to the four dimension model of middle class relational identification,the middle class relational identification in China is the cognitive and behavioral orientation that in the action of achieving a certain purpose,because of the perception of consistence with each other in role cognition and personal cognition,the middle class is willing to unite and trust,respect and understand and coordinate the conflict to adapt to this relationship,and then to form a positive evaluation of the relationship and a sense of belonging and commitment to this relationship.The construction of the structural dimension of middle class relational identification in China is conducive to explaining the endemic phenomena of Chinese middle class.

Identification of a LMNA (c.646C>T) variant by whole-exome sequencing in combination with a dilated cardiomyopathy (DCM) related gene filter in a family with familiar DCM

Dilated cardiomyopathy（DCM）is characterized by the dilated heart chambers and reduced systolic function in the absence of specific aetiology[1].Approximately one third of DCM cases are hereditary.In recent years,DCM concomitant with arrhythmias and sudden death resulting from gene mutation has been widely

Quantitative identification of relatively highlevel radioactive sandstone in the Upper Triassic Yanchang Formation of the Ordos Basin, China

Most relatively high-level radioactive sandstone(HRSS)reservoir has considerable oil(or gas)resource potential.HRSS is often wrongly identified due to its similar logging response characteristics as mudstone,which leads to the omission of effective reservoirs.In this paper,a quantitative identification method for HRSS is proposed after the analyzing of the response characteristics and relationship between spontaneous potential log and natural gamma-ray log in conventional sandstone and mudstone strata.Take the Upper Triassic Yanchang Formation in Ordos Basin as an example:the responses of spontaneous potential log and the responses of natural gamma-ray log are synchronized and positively correlated in conventional sandstone and mudstone strata,but they are not synchronized in HRSS.Quantitative identification of HRSS was realized based on this synchronization feature,and a"virtual compensation"of natural gamma-ray log was performed.At the same time,logging evaluation method about HRSS has been discussed.The final results shows that this identification method work effectively,and can reduce the misjudgment and omission of effective reservoirs.

ISOLATION AND IDENTIFICATION OF THE RELATED GENE MEDIATING γ-RADIATION-INDUCED APOPTOSIS IN HUMAN HL- 60 CELLS

To identify the member of the caspase family proteases involved in γ radiation induced apoptosis in HL 60 cells, using degenerated oligonucleotide primers encoding the highly conserved peptides, which were present in all known caspases, RT PCR was performed on poly (A) RNA from the γ radiation induced apoptotic HL 60 cells. Then, cloned and sequenced to identify the amplified DNA fragments. The results showed that the amplified DNA fragments were identified with a part of caspase 3 cDNA. It indicated that caspase 3 was involved in γ radiation induced apoptosis in HL 60 cells and may be the pivotal element of radiation induced apoptosis.

Inversion of focal mechanism and events identification using an improved relative amplitude method

The relative amplitude method （RAM） is more suitable for source inversion of low magnitude earthquakes because it avoids the modeling of short-period waveforms. We introduced an improved relative amplitude method （IRAM） which is more robust in practical cases. The IRAM uses a certain function to quantify the fitness between the observed and the predicted relative amplitudes among direct P wave, surface reflected pP and sP waves for a given focal mechanism. Using the IRAM, we got the fault-plane solutions of two earthquakes of mb4.9 and mb3.8, occurred in Issyk-Kul lake, Kyrgyzstan. For the larger event, its fault-plane solutions are consistent with the Harvard＇s CMT solutions. As to the smaller one, the strikes of the solution are consistent with those of the main faults near the epicenter. The synthetic long period waveforms and the predicted P wave first motions of the solutions are consistent with observations at some of regional stations. Finally, we demonstrated that fault-solutions cannot interpret the characteristics of teleseismic P waveforms of the underground nuclear explosion detonated in Democratic People＇s Republic of Korea （DPRK） on October 9, 2006.

IMMUNOCHEMICAL IDENTIFICATION AND LOCALIZATION OF CYTOCHROME P-450HSjISOZYME, AN ENZYME RELATED TO NITROSAMINE METABOLISM, IN HUMAN GASTRIC MUCOSA AND GASTRIC CARCINOMA

Monoclonal antibody (MAb) to rat liver cyto-chrome P-450j isozyme, an activating enzyme specific to nitrosamine metabolism, was used coupled with immunoblotting, densitometer scanning of SDS-PAGE gels and immunohistochemical technique. The trace P-450HSj isozyme (Mr. 51.5 Kd) was found in human gastric mucosa. It was similar to P-450j in molecular weight, catalytic and immunochemical properties. The concentrations of P-450HSj in mucosa of lesser curvature were higher than those in greater curvature. This might be one of the important reasons that lesser curvature is the commonest area for gastric carcinoma. But there was possibly less P-450HSj in gastric mucosa with cancer. Im-munohistochemically, P-450HSj was discovered in the cytoplasm of some glandular epithelial cells, especially in the glands with hyperplastic and intestinal metaplastic changes adjacent to carcinoma. It was also found in some normal glands and in tumor cells of high-differentiated adenocarcinoma, but not in those of low-differentiated ones. Following subjects are discussed: (1) the method of detecting trace P-450HSj, (2) the rule of distribution of P-450HSj, and (3) the relationship between the isozyme and the occurrence of gastric cancer caused by nitrosa-mines.

Identification of a candidate elongation related gene in rice induced by GA_3

We identified a gibberellin-induced gene frag-ment in rice elongation by using differentialdisplay(DD)of mRNA.The rice seedlingscarried the eui(elongated)gene,named Zhen-chang A,were used,which were sensitive toGAand elongated rapidly after application of

Structural damage identification using test static data based on grey system theory

A new structural damage identification method using limited test static displacement based on grey system theory is proposed in this paper. The grey relation coefficient of displacement curvature is defined and used to locate damage in the structure, and an iterative estimation scheme for solving nonlinear optimization programming problems based on the quadratic programming technique is used to identify the damage magnitude. A numerical example of a cantilever beam with single or multiple damages is used to examine the capability of the proposed grey-theory-based method to localize and identify damages. The factors of meas-urement noise and incomplete test data are also discussed. The numerical results showed that the damage in the structure can be localized correctly through using the grey-related coefficient of displacement curvature, and the damage magnitude can be iden-tified with a high degree of accuracy, regardless of the number of measured displacement nodes. This proposed method only requires limited static test data, which is easily available in practice, and has wide applications in structural damage detection.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

Nonlinear Modeling and Identification of Structural Joint by Response Control Vibration Test

Components of mechanical product are assembled by structural joints,such as bolting,riveting,welding,etc.Structural joints introduce nonlinearity to some engineering structures,and the nonlinearity need to be modeled precisely.To meet serious quality requirements,it is necessary to detect and identify nonlinearity of mechanical products for structural optimization.Modal test to acquire a dynamic response has been applied for decades,which provides reliable results for finite element(FE)model updating.Here response control vibration test for identification of nonlinearity is presented.A nonlinear system can be regarded as linearity for particular steady state response,and classical linear analysis tool is applicable to extract modal data for particular response.First,its applicability is illustrated by some numerical simulations.Subsequently,it is implemented on experimental setup with structural joints by shaking table.The stiffness and damping function dependent of relative displacement are fitted to describe its inherent nonlinearity.The spring and damping forces are identified by harmonic balance method(HBM)to predict output response.Based on the identified results,the procedure is recommended that it allows a reliable measurement of nonlinearity with a certain accuracy.

Priority Directions of National Policy of Regulation of Mutual Relations Between Youth and Mass Media： The Theoretical and Empirical Researches

The study was concerned with the problem of the Russian youth, with a special emphasis on the history of the theoretical and empirical researches devoted to the youth issues. The research delineates the term thesaurus and its connection with the formation of youth＇s outlook. The central focus of the research is social and cultural value orientations of the young Russian. The study presents a detailed analysis of impact that mass media plays in socialization of youth, its generations, its influence on public opinion formation and priority directions of national policy of regulation of mutual relations between youth and mass media. It attempts to examine the presence of mass media in youth＇s everyday life. The research provides a detailed analysis of contemporary state of the youth＇s trust to the mass media and degree of dependence on it. The study also presents a detailed analysis of the traces of mass media＇s influence on young Russians. The completed research is fundamental for the sociological and the philosophical generalisations that underline comparative research on the youth problems in Russia, its social and cultural value orientations and the influence on public opinion formation and priority directions of national policy of regulation of mutual relations between youth and mass media.

基于GA-LSTM自适应卡尔曼滤波的路面不平度识别

准确、快速地识别出车辆当前行驶的路面激励信息,是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题,提出了基于遗传算法(genetic algorithm,GA)优化长短期记忆神经网络(long short-term memory networks,LSTM)自适应卡尔曼滤波的路面不平度识别算法。基于2自由度车辆悬架模型,通过灰色关联法选择LSTM神经网络的特征输入变量,并采用GA优化LSTM神经网络的模型参数以准确识别路面等级,并据此实时更新卡尔曼滤波器算法中的噪声矩阵,实现了在复杂路况下对路面不平度的自适应识别。仿真和试验研究表明,所提出的基于GA-LSTM自适应卡尔曼滤波算法能够快速准确的识别路面不平度与路面等级,与传统卡尔曼滤波算法相比,相关系数、均方根误差和最大绝对误差分别提高3.11%、37.5%和51.2%,表明所提算法对复杂工况具有很好的自适应能力。

基于灰色关联度分析-极限学习机的低阻油层及水淹层测井识别——以渤海P区块馆陶组为例

历经近20的开发,渤海P区块进入高含水期,馆陶组发育的大量低阻油层与水淹层在测井曲线形态上差异不明显。为了精确进行水淹层识别以及水淹层等级划分,采用了机器学习算法。首先采用灰色关联度分析,筛选低阻油层和水淹层识别的敏感参数曲线;其次构建了极限学习机水淹层识别模型,对模型进行训练,获取最优参数。将其应用于实际资料处理,结果表明,基于灰色关联度分析极限学习机的低阻油层及水淹层测井识别方法对低阻油层与水淹层的预测精度较高,符合率达89.3%,远远优于未经过灰色关联度分析筛选的预测结果,具有实际应用价值。

基于事件相关电位的监控员任务切换刺激模态差异性对警觉度的影响研究

为探究不同模态转换下任务切换对监控人员警觉度衰减现象的影响,采用脑电实验法,设计了危险动作识别、危险场景识别等任务,并结合行为数据及事件相关电位(Event Related Potential,ERP)技术进行综合分析;实验重点考察在任务切换过程中,不同模态的任务之间进行切换对监控人员警觉度的具体影响;通过对煤矿监控调度人员的实证研究,揭示了切换任务的模态差异性对监控员警觉度的影响及其内在机制。结果表明:当在相同刺激任务间进行切换时带来的工作绩效下降,主要原因在于任务切换产生的切换成本导致注意力分散,具体体现在任务切换后P300和P200平均波幅的升高;但是在不同刺激任务间进行切换时,尤其是切换到听觉刺激任务时,被试者的警觉度水平反而得到了一定程度的提升。