已有监测方法存在通信盲区,采集数据平稳度较差,致使设备故障预测误差率较高,无法满足智能电网发展需求。为此,提出基于改进射频识别(radio frequency identification,RFID)技术的输配电设备全生命周期智能化监测方法。划分输配电设备...已有监测方法存在通信盲区,采集数据平稳度较差,致使设备故障预测误差率较高,无法满足智能电网发展需求。为此,提出基于改进射频识别(radio frequency identification,RFID)技术的输配电设备全生命周期智能化监测方法。划分输配电设备全生命周期阶段,并总结生命周期阶段判定特征;以此为基础,改进RFID技术搭建多天线RFID通信框架;以搭建通信框架为依据,结合设备不同生命周期阶段特性,自适应调整运行数据的采集间隔,获取输配电设备运行数据;有效结合整合移动平均自回归(autoregressive integrated moving average,ARIMA)模型与支持向量机算法,提前预测输配电设备故障,从而实现输配电设备全生命周期的智能化监测。试验结果表明:所提方法的输配电设备运行数据采集平稳度数值较小,故障预测误差率较低。输配电设备智能化监测结果可以反映智能电网的稳定运行、发展进度与供电质量,以此保障输配电设备的可靠运行。展开更多
The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation...Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.展开更多
A new improved group space-time block code (G-STBC) based on constellation rotation for four transmit antennas was proposed. In comparison with the traditional G-STBC coding scheme, the proposed space-time code has lo...A new improved group space-time block code (G-STBC) based on constellation rotation for four transmit antennas was proposed. In comparison with the traditional G-STBC coding scheme, the proposed space-time code has longer code length and adopts proper rotation-based symbols, which can increase the minimum distance of space-time codes and thereby improve code gain and achieve full diversity performance. The simulation results verify that the proposed group space-time code can achieve better bit error performance than both the traditional group space-time code and other quasi-orthogonal space-time codes. Compared with Ma’s full diversity full rate (FDFR) codes, the proposed space-time code also can achieve the same excellent error performance. Furthermore, the design of the new space-time code gives another new and simple method to construct space-time codes with full diversity and high rate in case that it is not easy to design the traditional FDFR space-time codes.展开更多
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
基金Project(41272137) supported by the National Natural Science Foundation of China
文摘Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.
基金National High Technology Research andDevelopment Program (863) of China( No. 003AA12331007 ) and NationalNatural Science Foundation of China(No. 60272079, 60332030)
文摘A new improved group space-time block code (G-STBC) based on constellation rotation for four transmit antennas was proposed. In comparison with the traditional G-STBC coding scheme, the proposed space-time code has longer code length and adopts proper rotation-based symbols, which can increase the minimum distance of space-time codes and thereby improve code gain and achieve full diversity performance. The simulation results verify that the proposed group space-time code can achieve better bit error performance than both the traditional group space-time code and other quasi-orthogonal space-time codes. Compared with Ma’s full diversity full rate (FDFR) codes, the proposed space-time code also can achieve the same excellent error performance. Furthermore, the design of the new space-time code gives another new and simple method to construct space-time codes with full diversity and high rate in case that it is not easy to design the traditional FDFR space-time codes.
