Analysis of College Students’ Test Scores Based on Two-Component Mixed Generalized Normal Distribution

In order to improve the fitting accuracy of college students’ test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditional Maxinnization (ECM) algorithm to estimate parameters and conduct numerical simulation, and performs fitting analysis on the test scores of Linear Algebra and Advanced Mathematics of F University. The empirical results show that the two-component mixed generalized normal distribution is better than the commonly used two-component mixed normal distribution in fitting college students’ test data, and has good application value.

Electricity price forecasting using generalized regression neural network based on principal components analysis

A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

GENERIC STABILITY AND EXISTENCE OF ESSENTIAL COMPONENTS OF THE SOLUTION SET FOR THE SYSTEM OF GENERALIZED VECTOR EQUILIBRIUM PROBLEMS

By using Fort theorem the generic stability result for the system of generalized vector equilibrium problems is established. Further, by proving the existence and connectivity of minimal essential set the existence result of essential components in the solution set is derived.

INVARIANT SUBSPACES AND GENERALIZED FUNCTIONAL SEPARABLE SOLUTIONS TO THE TWO-COMPONENT b-FAMILY SYSTEM

Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.

gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis

Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning techniques.Although there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling framework.The package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable modeling.More specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in statistics.As a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from GSCA.The main function,gscaLCA,works for both binary and ordered categorical variables.In addition,gscaLCA can be used for latent class regression as well.Visualization of profiles of latent classes based on the posterior probabilities is also available in the package gscaLCA.This paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.

THE GLOBAL ATTRACTOR FOR A VISCOUS WEAKLY DISSIPATIVE GENERALIZED TWO-COMPONENT μ-HUNTER-SAXTON SYSTEM

This article is concerned with the existence of global attractor of a weakly dissipative generalized two-component μ-Hunter-Saxton （gμHS2） system with viscous terms. Under the period boundary conditions and with the help of the Galerkin procedure and compactness method, we first investigate the existence of global solution for the viscous weakly dissipative （gμHS2） system. On the basis of some uniformly prior estimates of the solution to the viscous weakly dissipative （gμHS2） system, we show that the semi-group of the solution operator {S（t）}t≥0 has a bounded absorbing set. Moreover, we prove that the dynamical system {S（t）}t≥0 possesses a global attractor in the Sobolev space H2（S） × H2（S）.

基于Generative Component的中国古建筑参数化设计

为有效分析古建筑,利用参数化建模软件Generative Component(GC),结合编程设计对中国古建筑主要结构和构件进行参数化分析,找出其内在结构的规律性及各构件之间的参数关系,实现古建筑的数字化、参数化描述,生成参数可修改的结构化三维模型.

Influence of dc Component during Inadvertent Operation of the High Voltage Generator Circuit Breaker during Mis-Synchronization

This paper analyses the synchronization problem of a generator onto power system without satisfying synchronization condition. The main focus of the paper is on the impact of the dc component of the current in the high voltage circuit breaker during its close-open operating cycle. Using real time measurements of currents/voltages and angles during the close-opening cycle of high voltage generator circuit breaker and the impact of the dc component of current in context of interrupting large magnitude of current from the circuit breaker. In addition, the paper describes a study case model and the results of simulations performed using the software EMTP-ATP of an actual incident that occurred during the inadvertent synchronization of a large 339 MW, 24 kV generator to the grid.

Blind source separation of ship-radiated noise based on generalized Gaussian model

When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.

Risk based security assessment of power system using generalized regression neural network with feature extraction

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.

Blind source separation based on generalized gaussian model

Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.

Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.

Localized waves in three-component coupled nonlinear Schrdinger equation

We study the generalized Darboux transformation to the three-component coupled nonlinear Schr ¨odinger equation.First-and second-order localized waves are obtained by this technique.In first-order localized wave,we get the interactional solutions between first-order rogue wave and one-dark,one-bright soliton respectively.Meanwhile,the interactional solutions between one-breather and first-order rogue wave are also given.In second-order localized wave,one-dark-one-bright soliton together with second-order rogue wave is presented in the first component,and two-bright soliton together with second-order rogue wave are gained respectively in the other two components.Besides,we observe second-order rogue wave together with one-breather in three components.Moreover,by increasing the absolute values of two free parameters,the nonlinear waves merge with each other distinctly.These results further reveal the interesting dynamic structures of localized waves in the three-component coupled system.

Application of Modern Intelligent Optimization Method in Parameter Setting of PI Regulator of Accelerator Power Suppies Components

In the accelerator control system,many components use PI regulators for feedback control of the closed-loop system.In the process of parameter setting of PI regulators,empirical methods are often required and a lot of time is spent on parameter setting.This paper proposes a serial algorithm for generating multiple dimensional variables,which covld greatly improve the speed of parameter setting,reduce the time of manual parameter setting,and provide a fast and feasible method for parameter setting of equipment in accelerators that requires manual intervention solution.

含逆变型分布式电源和分支负荷的配电网自适应电流差动保护

电流差动保护具有良好的选择性和灵敏性。但是,逆变型分布式电源(inverter-interfaced distributed generator,IIDG)和分支负荷接入改变了配电网的故障特性,现有配电网的电流差动保护面临拒动或误动的风险。为解决以上问题,该文在计及P/Q控制的IIDG和分支负荷的故障等值建模基础上,从故障点、IIDG和分支负荷的相对位置出发,挖掘配电网线路两端正序电流差的幅值特征以及正序电流故障分量的相位特征。在此基础上,提出一种正序电流幅值与故障分量相角阈值配合的双比例制动系数自适应差动保护方案,并且利用幅相平面分析所提保护判据的可靠性和灵敏性。最后通过PSCAD/EMTDC验证了所提保护方案的有效性,仿真结果表明该方案适应不同故障类型、位置和过渡电阻,且不受IIDG以及分支负荷接入的影响。

大型变速抽水蓄能发电电动机不同转速下三维端部电磁场和损耗研究

大型变速抽水蓄能发电电动机端部区域漏磁和构件损耗较高,为了研究不同转速时大型变速抽水蓄能发电电动机三维端部电磁场和端部构件损耗的变化规律,本文建立326 MW变速抽水蓄能发电电动机三维端部瞬态电磁场的数学模型,研究变速抽水蓄能电机在发电机工况下不同转速时定转子端部构件磁密的变化规律,确定变速抽水蓄能电机端部构件涡流密度的分布情况,探究变速抽水蓄能电机在发电机工况下不同转速时定子压圈、定子环板、转子护环以及转子齿压板等端部构件损耗的变化规律。采用相同的计算方法对小容量10 MW变速抽水蓄能发电电动机进行研究,通过试验测试和计算结果的对比验证了本文计算方法的可行性。

基于改进型SOGI-FLL的单相并网逆变器电压控制方法

针对常规基于二阶广义积分发生器的锁频环(second-order generalized integrator based frequency locked-loop,SOGI-FLL)在单相并网逆变器电压控制中对直流及谐波分量抑制能力不足,从而引起输出电压频率、相位振荡的问题,提出一种基于改进型SOGI-FLL的单相并网逆变器电压控制方法。该方法在常规SOGI-FLL控制的基础上,在电压信号输入端加入级联型谐振滤波环节来消除谐波分量;同时引入直流控制环节,借助输入电压误差估计值来消除直流分量,达到电网电压频率和相位快速跟踪效果,从而实现电压的自适应控制。使用MATLAB及RT-LAB硬件在环半实物平台,在频率突变、含直流分量及谐波分量的非理想电网环境中,对二阶广义积分器锁相环、双二阶广义积分器锁频环与改进型SOGI-FLL 3种控制方法进行仿真及实验。结果表明,所提改进型SOGI-FLL控制方法在消除直流及谐波干扰的同时,能在0.025 s内实现频率锁定,且频率偏差小于2%,可增强系统对非理想电网信号的适应能力,实现并网电压的快速跟踪,具有良好动态性能。

基于经验模态分解和深度学习的短期风电功率预测

精准的风电功率预测有利于全网电力平衡、系统安全稳定运行和节能减耗。提出一种基于经验模态分解(empirical mode decomposition, EMD)、核主成分分析(kernel principal component analysis, KPCA)和长短期记忆(long short-term memory, LSTM)神经网络的短期风功率预测模型。采用EMD技术将多维气象序列分解为多个固有模态分量,以挖掘原始数据的主要特征并消除噪声;引入KPCA进行降维处理,提取数据的非线性特征;使用LSTM神经网络对特征提取的序列进行学习并完成预测,获得风电功率预测的最终结果。使用所提出的模型对新疆某一风电场风电功率进行预测,将预测结果与其他模型对比。结果表明,该预测模型能改善预测性能,降低风电功率预测误差。

面向大规模定制的建筑工业化智能设计方法研究

为了推动大规模定制的建筑工业化模式,提出了两种适用于工业化建筑的智能设计方法:面向装配的设计方法和面向销售的设计方法,并以轻钢龙骨式结构建筑为例,展示了设计流程和案例说明。面向装配的设计方法以二维板块化部品为基本单元,以直接面向工厂下订单为目标,首先建立具有预先标定结构性能的建筑部品数据库,在生成建筑平面后,再将结构荷载分配到每个墙体、楼面部品上,自动匹配所需的建筑部品型号,并进行整体结构验算。面向销售的设计方法以三维建筑模块为基本单元,以直接面向销售下订单为目标,定义具有各种功能的模块并建立生成式设计规则,在建筑初步设计阶段考虑因不同的建筑布局而产生的不同建筑成本,并通过遗传算法优化建筑布局和成本。这两种方法选择的部品和模块均符合工业化大规模生产的要求,为实现大规模生产的工业化设计流程提供了新思路。

高原鼠兔种群密度与生境因子作用关系

高原鼠兔(Ochotona curzoniae)是青藏高原上的小型哺乳动物,一定数量内的高原鼠兔可以提高高寒草地的生物多样性,改善土壤结构,有利于维持生态系统的稳定性。但当其种群密度过大时,可能会导致草地退化。探究高原鼠兔种群密度的影响因素能够为科学防控高原鼠兔提供理论依据。本研究从气象、土壤、植被、地形及人类活动多角度探究16个生境因子对高原鼠兔种群密度的影响,通过主成分分析及Pearson相关性分析从16个生境因子中筛选出年均温、植被高度、海拔及土壤硬度为主要建模因子,并采用广义加性模型(GAM)分析高原鼠兔种群密度与4个建模因子之间的作用关系,构建高原鼠兔种群密度预测模型。结果显示:本研究构建的GAM模型拟合度较高(R^(2)=0.946),可以较好地评估青藏高原地区高原鼠兔的潜在致灾风险;土壤硬度与高原鼠兔种群密度之间存在显著负相关;海拔、植被高度和年均温度均与高原鼠兔种群密度之间存在非线性关系,并且海拔为3800~4000 m、植被高度为6~8 cm、年均温度为-2~0℃时高原鼠兔种群密度达到最大。