基于双回反向序网的同杆4回线单端故障测距算法

提出了一种利用单端工频量的同杆4回线故障测距算法.采用同杆4回线双回同反序解耦方法计算得到考虑分布参数特性的故障线路沿线电压;采用同杆4回线双回反序网络计算得到考虑分布参数特性的故障电流分支系数,进而计算出故障线路沿线电流.由于故障支路可假设为纯阻性,因此利用故障处电压电流相量之比的虚部为0的特性进行故障位置搜索,最终实现同杆4回线中1回线内及2回线间跨线故障时的故障定位.本方法计算简单,不存在伪根问题,且不受系统阻抗、线路分布电容及过渡电阻的影响.仿真表明在同杆4回线各种故障类型下算法均具有很高的测距精度.

基于反向模糊Petri网的应急响应条件下事故的致因分析

分析应急响应行动对事故发展的影响,对完善应急准备具有重要作用。提出利用反向模糊Petri网进行推理,以对应急响应行动进行分析。讨论了模糊Petri网和反向模糊Petri网的推理方法,以火灾多米诺效应这一典型受应急响应影响的事故为例,建立了Petri网模型,并对主要的应急行动进行了分析,结果表明,基于模糊Petri网反向推理的分析方法对应急行动进行分析是可行的。

SSL反向代理网关请求分发的系统架构设计

针对将客户端请求向Web系统中各SSL反向代理网关分发的问题,提出了两种系统架构设计方案,为SSL会话粒度的SSL反向代理网关请求分发算法设计提供了理论支持,并总结了两种设计方案的优缺点。其中一种方案的请求分发设备串联在客户端与安全网关系统之间,用TCP hand-off的方法转发客户端请求,其额外开销较小,响应速度较快,但无法分析客户端请求内容,且实现较为复杂;另一种方案的请求分发设备利用HTTP的重定向机制转发客户端请求,其额外开销较大,但可以分析客户端请求内容,且实现较为容易。

CATV光纤网回传电视现场直播信号

微波传输信号方式受到环境制约,而中小城市没有卫星通讯系统,因此回传信号成了中小电视台现场直播的“瓶颈”。本文介绍了CATV光纤网回传电视现场直播信号的优势,阐述了反向回传与正向传输不同指标要求,讨论了实现CATV网回传电视现场直播信号的技术参数和设计方案。

Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network

The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.

Approximation Property of the Modified Elman Network

A new type of recurrent neural network is discussed, which provides the potential for modelling unknown nonlinear systems. The proposed network is a generalization of the network described by Elman, which has three layers including the input layer, the hidden layer and the output layer. The input layer is composed of two different groups of neurons, the group of external input neurons and the group of the internal context neurons. Since arbitrary connections can be allowed from the hidden layer to the context layer, the modified Elman network has more memory space to represent dynamic systems than the Elman network. In addition, it is proved that the proposed network with appropriate neurons in the context layer can approximate the trajectory of a given dynamical system for any fixed finite length of time. The dynamic backpropagation algorithm is used to estimate the weights of both the feedforward and feedback connections. The methods have been successfully applied to the modelling of nonlinear plants.

Temperature compensation method of silicon microgyroscope based on BP neural network

The temperature characteristics of a silicon microgyroscope are studied, and the temperature compensation method of the silicon microgyroscope is proposed. First, an open-loop circuit is adopted to test the entire microgyroscope＇s resonant frequency and quality factor variations over temperature, and the zero bias changing trend over temperature is measured via a closed-loop circuit. Then, in order to alleviate the temperature effects on the performance of the microgyroscope, a kind of temperature compensated method based on the error back propagation（BP）neural network is proposed. By the Matlab simulation, the optimal temperature compensation model based on the BP neural network is well trained after four steps, and the objective error of the microgyroscope＇s zero bias can achieve 0.001 in full temperature range. By the experiment, the real time operation results of the compensation method demonstrate that the maximum zero bias of the microgyroscope can be decreased from 12.43 to 0.75（°）/s after compensation when the ambient temperature varies from -40 to 80℃, which greatly improves the zero bias stability performance of the microgyroscope.

VIRTUAL TARGET DIFFERENTIAL GAME MIDCOURSE GUIDANCE LAW FOR HYPERSONIC CRUISE MISSILE BASED ON NEURAL NETWORK

For the high altitude cruising flight phase of a hypersonic cruise missile （HCM）, a relative motion mod- el between the missile and the target is established by defining virtual target and combining the theory of the dif- ferential geometry with missile motion equations. Based on the model, the motion between the missile and the tar- get is considered as a single target differential game problem, and a new open-loop differential game midcourse guidance law （DGMGL） is deduced by solving the corresponding Hamiltonian Function. Meanwhile, a new struc- ture of a closed-loop DGMGL is presented and the training data for back propagation neural network （BPNN） are designed. By combining the theory of BPNN with the open-loop DGMGL obtained above, the law intelligence is realized. Finally, simulation is carried out and the validity of the law is testified.

基于相位特性的同杆双回线非同步故障定位研究

提出了一种基于双曲正切函数相位特性同杆双回线非同步故障测距新原理。根据所取参考点与故障点相匹配时双曲正切函数相位过零这一特征进行定位。该方法理论证明了非同步故障时可利用对侧反向正序电流相位来同步校正该侧反向正序电流相位,无需通过数据移动来同步匹配两侧信息。原理上不存在伪根问题,对非线性电阻故障具有良好的适用性,所需运算量小,能有效克服传统方法存在的测距精度和测距速度此消彼长的矛盾。PSCAD/EMTDC软件仿真验证了此方法的正确性和有效性。

Improved BP Neural Network for Transformer Fault Diagnosis

The back propagation (BP)-based artificial neural nets (ANN) can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types, using the highly nonlinear mapping nature of the neural nets. An efficient BP-ALM (BP with Adaptive Learning Rate and Momentum coefficient) algorithm is proposed to reduce the training time and avoid being trapped into local minima, where the learning rate and the momentum coefficient are altered at iterations. We developed a system of transformer fault diagnosis based on Dissolved Gases Analysis (DGA) with a BP-ALM algorithm. Training patterns were selected from the results of a Refined Three-Ratio method (RTR). Test results show that the system has a better ability of quick learning and global convergence than other methods and a superior performance in fault diagnosis compared to convectional BP-based neural networks and RTR.

混压同塔四回线故障测距

混压同塔四回线故障时的精准测距因线路间存在复杂耦合导致测距难度较大,为此文中从更贴近工程实际的不对称阻抗模型入手,介绍了两种不同物理意义的阻抗解耦矩阵M1、M2。两种解耦方式下的反向零序g0序网有着相同的网络拓扑结构,其外部序阻抗、序电压均为0,序电流仅在四回线内部流动,不受外部设备影响。因此,统一利用g0序网,由两端母线到故障点处序电压相等建立一元一次测距方程,巧妙地将混压同塔四回线的故障测距转化到单个序网上来,大大减小了混压同塔四回线故障测距的复杂度。大量的PSCAD/EMTDC仿真数据表明,两种算法均能实现精确测距,不受故障距离、过渡电阻和电源功角差等系统运行参数的影响。

LEARNING ALGORITHM OF STAGE CONTROL NBP NETWORK

This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending Enhanced Combination Algorithm (ECA) is proposed.The analysis of the generalized property and sample error shows that the ECA can heighten the study speed and reduce individual error.

APPLICATION OF NEURAL NETWORK INVERSE CONTROL SYSTEM IN TURBO DECODING

Adaptive inverse control system can improve the performance of turbo decoding,and modeling turbo decoder is one of the most important technologies. A neural network model for the inverse model of turbo decoding is proposed in this paper. Compared with linear filter with its revi-sion,the general relationship between the input and output of the inverse model of turbo decoding system can be established exactly by Nonlinear Auto-Regressive eXogeneous input (NARX) filter. Combined with linear inverse system,it has simpler structure and costs less computation,thus can satisfy the demand of real-time turbo decoding. Simulation results show that neural network in-verse control system can improve the performance of turbo decoding further than other linear con-trol system.

Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

Comparison of flow behaviors of near beta Ti-55511 alloy during hot compression based on SCA and BPANN models

The flow behavior of Ti-55511 alloy was studied by hot compression tests at temperatures of 973−1123 K and strain rates of 0.01−10 s^(−1).Strain-compensated Arrhenius(SCA)and back-propagation artificial neural network(BPANN)methods were selected to model the constitutive relationship,and the models were further evaluated by statistical analysis and cross-validation.The stress−strain data extended by two models were implanted into finite element to simulate hot compression test.The results indicate that the flow stress is sensitive to deformation temperature and strain rate,and increases with increasing strain rate and decreasing temperature.Both the SCA model fitted by quintic polynomial and the BPANN model with 12 neurons can describe the flow behaviors,but the fitting accuracy of BPANN is higher than that of SCA.Sixteen cross-validation tests also confirm that the BPANN model has high prediction accuracy.Both models are effective and feasible in simulation,but BPANN model is superior in accuracy.

Optimization of processing parameters for microwave drying of selenium-rich slag using incremental improved back-propagation neural network and response surface methodology

In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.

Application of neural network to prediction of plate finish cooling temperature

To improve the deficiency of the control system of finish cooling temperature (FCT), a new model developed from a combination of a multilayer perception neural network as the self-learning system and traditional mathematical model were brought forward to predict the plate FCT. The relationship between the self-learning factor of heat transfer coefficient and its influencing parameters such as plate thickness, start cooling temperature, was investigated. Simulative calculation indicates that the deficiency of FCT control system is overcome completely, the accuracy of FCT is obviously improved and the difference between the calculated and target FCT is controlled between -15 ℃ and 15 ℃.

APPLICATIONOFNEURALNETWORKTOFLIGHTCONTROLSYSTEMDESIGN

Artificial neural network (ANN) has a great capability of self learning. The application of neural network to flight controller design can get good result. This paper studies the method of choosing controller parameters using neural network with Back Propagation (B P) algorithm. Design and simulation results show that this method can be used in flight control system design.

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.

Prostate Cancer Risk Prediction and Online Calculation Based on Machine Learning Algorithm

Objective To build a prostate cancer(PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence(AI) technology under healthcare data platforms.Methods After preprocessing of the data from Population Health Data Archive,smuothly clipped absolute deviation(SCAD) was used to select features.Random forest(RF),support vector machine(SVM),back propagation neural network(BP),and convolutional neural network(CNN) were used to predict the risk of PCa,among which BP and CNN were used on the enhanced data by SMOTE.The performances of models were compared using area under the curve(AUC) of the receiving operating characteristic curve.After the optimal model was selected,we used the Shiny to develop an online calculator for PCa risk prediction based on predictive indicators.Results Inorganic phosphorus,triglycerides,and calcium were closely related to PCa in addition to the volume of fragmented tissue and free prostate-specific antigen(PSA).Among the four models,RF had the best performance in predicting PCa(accuracy:96.80%;AUC:0.975,95% CI:0.964-0.986).Followed by BP(accuracy:85.36%;AUC:0.892,95% CI:0.849-0.934) and SVM(accuracy:82.67%;AUC:0.824,95% CI:0.805-0.844).CNN performed worse(accuracy:72.37%;AUC:0.724,95% CI:0.670-0.779).An online platform for PCa risk prediction was developed based on the RF model and the predictive indicators.Conclusions This study revealed the application value of traditional machine learning and deep learning models in disease risk prediction under healthcare data platform,proposed new ideas for PCa risk prediction in patients suspected for PCa and had undergone core needle biopsy.Besides,the online calculation may enhance the practicability of AI prediction technology and facilitate medical diagnosis.