PC准则下生长曲线模型回归参数阵岭估计的估计优良性

将ＰＣ准则推广应用于生长曲线模型参数阵的最小二乘估计和岭估计优劣性的比较,给出了岭估计在ＰＣ准则下优于最小二乘估计的条件。     

线性等式约束下一般生长曲线模型参数阵的BLU估计

研究了线性等式约束下一般生长曲线模型，得到了条件线性可估函数的最优线性无偏估计，并证明了在几乎处处意义下是唯一的。     

PC准则下生长曲线模型回归参数阵岭估计的优良性

将 PC准则推广应用于生长曲线模型参数阵的最小二乘估计和岭估计优劣性的比较 ,给出了岭估计在PC准则下优于最小二乘估计的条件     

增长曲线模型中回归系数的根方估计

本文采用根方估计Ｂ（ｍ）（０＜ｍ＜１）来估计增长曲线模型中回归系数Ｂ，通过根方参数ｍ值的选取，可使β（ｍ）＝Ｖｅｃ（Ｂ（ｍ）的均方程误差（ＭＳＥ）小于β＝Ｖｅｃ（Ｂ）的ＬＳＥβ的ＭＳＥ，本文还给出了选取ｍ值的三种方法及应用实例。     

带约束的增长曲线模型中回归系数线性估计的可容许性

在两种矩阵损失函数下讨论了带约束的增长曲线模型中回归系数线性估计的可容许性,并在齐次线性估计类和非齐次线性估计类中得到了容许性的充要条件.     

多元线性模型回归系数的主成分估计

本文对多元线性模型回归系数提出了主成分估计，并证明了主成分估计优于最小二乘估计。进一步，对最小二乘估计的任一线性变换，给出了均方误差的一个无偏估计，并应用极小化均方误差的无偏估计的方法，给出了确定偏参数的公式。     

扩展增长曲线模型中协差阵的最小模估计问题

考虑扩展增长曲线模型Ｙ＝Σ＾（ｍ，ｉ＝１）ＸｉｂＩＺ’ｉ＋Ｕε其中Ｘｉ，Ｚｉ，Ｕ均为已知矩阵，Ｒ（Ｚ１）∩←…∩←Ｒ（Ｚｍ），Ｂｉ是回归系数国，Ｙ＝（ｙ（１），…，（ｙ（ｎ））‘和ε＝（ε（１），…，ε（ｓ）’分别是ｎ×ｐ观测资料矩阵和ｓ×ｐ随机误差矩阵。     

一类具有椭球误差的多元线性模型参数的Bayes估计

本文讨论下列线性模型Ｙｍ×ｎ＝βｍ×ｐＸｐ×ｎ＋βｍ×ｎ其中ε服从一类特殊的矩阵椭球分布，特征矩阵为Σ＾～ｍｎ×ｍｎ＝Σｎ×ｍ⊙Ｖｍ×ｍ。给出了在Σ＞０已知；Ｖ＞０，已知，Σ＝σ＾２Ｉｎ，σ＾２＞０未知；Ｖ＞０未知三种情形下参数矩阵β的Ｂａｙｅｓ估计。     

一类具有椭球误差的多无线性模型参数的Bayes估计

本文讨论下列线性模型Ｙｍ×ｎ＝βｍ×ｐＸｐ×ｎ＼＋βｍ×ｎ其中ε服从一类特殊的矩阵椭球分布，特征矩阵为。给出了在Σ＞０已知；Ｖ＞０，已知，Σ＝σ２Ｉｎ，σ２＞０未知；Ｖ＞０未知三种情形下参数矩阵β的Ｂａｙｅｓ估计。     

不完全信息条件下的电力负荷曲线估计

为了保证电力系统更经济、可靠的运行,对电力系统状态的估计要求越来越严格,对算法的要求也越来越高。本文将核主分量分析算法和独立分量分析算法结合应用到不完全信息条件下的电力系统用户曲线估计中,基于实测数据的实验结果表明了该算法的有效性。与传统ICA相比,这种结合算法具有更好的学习能力,稳定性,准确性和推广性。     

一般增长曲线模型中随机回归系数线性估计的可容许性

本文在矩阵损失下研究了一般增长曲线模型中随机回归系数线性估计的可容许性。分别在齐次线性估计类和非齐次线性估计类中得到了随机回归系数的一个线性估计是可容许的充要条件。     

近邻软磁层偏置磁阻磁头结构参数设计的数学模型

概述了软磁层偏置磁阻磁头的工作原理和特点 ,采用传输线模型对其工作点的选取和结构参数进行了初步设计 ,与实验结果符合较好     

具有时滞的经济增长模型的控制

本文考虑了时滞经济增长模型的控制问题,利用不动点定理,我们给出了比Chukwu时滞控制系统更广泛的一类非线性系统的可控制性条件,并且作为线性情况下的一个简单应用,对香港宏观经济增长的控制进行了实证检验。     

An MSE comparison of the restricted Stein-rule and minimum mean squared error estimators in regression

In this paper, we consider the feasible minimum mean squared error (FMMSE) estimator and the adjusted FMMSE (AFMMSE) estimator which are obtained by shrinking the ordinary least squares (OLS) estimator towards the restricted least squares estimator. We derive the formulas of MSE for the restricted FMMSE and AFMMSE estimators. By numerical evaluations, the MSE performances of the restricted FMMSE and AFMMSE estimators are compared with that of the restricted positive-part Stein-rule estimator.     

Comparison of Cellular Automaton and Phase Field Models to Simulate Dendrite Growth in Hexagonal Crystals

A cellular automaton (CA)-finite element (FE) model and a phase field (PF)-FE model were used to simulate equiaxed dendritic growth during the solidification of hexagonal metals.In the CA-FE model,the conservation equations of mass and energy were solved in order to calculate the temperature field,solute concentration,and the dendritic growth morphology.CA-FE simulation results showed reasonable agreement with the previously reported experimental data on secondary dendrite arm spacing (SDAS) vs cooling rate.In the PF model,a PF variable was used to distinguish solid and liquid phases similar to the conventional PF models for solidification of pure materials.Another PF variable was considered to determine the evolution of solute concentration.Validation of both models was performed by comparing the simulation results with the analytical model developed by Lipton-Glicksman-Kurz (LGK),showing quantitatively good agreement in the tip growth velocity at a given melt undercooling.Application to magnesium alloy AZ91 (approximated with the binary Mg-8.9 wt% Al) illustrates the difficulty of modeling dendrite growth in hexagonal systems using CA-FE regarding mesh-induced anisotropy and a better performance of PF-FE in modeling multiple arbitrarily-oriented dendrites growth.     

数字化校园中统一身份认证平台的设计

统一身份认证平台能在高度集成的校园网络应用环境中,将相互独立应用系统中的用户和权限资源进行统一、集中管理。统一身份认证平台的设计是在开源门户系统Liferay基础上,配置LDAP和CAS单点登录服务,通过定制CAS的认证流程实现统一身份认证。     

聚丙烯纤维再生混凝土损伤本构模型的研究

基于Weibull统计分布理论和Lemaitre等效应变假定原理,推导出聚丙烯纤维再生混凝土单轴受压损伤本构模型,对聚丙烯纤维再生混凝土进行单轴受压试验,根据试验数据,确定了该模型参数,通过试验曲线和模型曲线的对比分析发现两者拟合较好。基于考虑中间主应力、拉压性能影响的适用于任何材料的统一强度理论,推导出聚丙烯纤维再生混凝土的双剪损伤本构模型,建立了纤维混凝土损伤本构模型从单轴到双轴的转化方法。     

Discrete Nonhomogeneous Poisson Process Software Reliability Growth Models Based on Test Coverage

To incorporate the effect of test coverage, we proposed two novel discrete nonhomogeneous Poisson process software reliability growth models in this article using failure data and test coverage, which are both regarding the number of executed test cases instead of execution time. Because one of the most important factors of the coverage‐based software reliability growth models is the test coverage function (TCF), we first discussed a discrete TCF based on beta function. Then we developed two discrete mean value functions (MVF) integrating test coverage and imperfect debugging. Finally, the proposed discrete TCF and MVFs are evaluated and validated on two actual software reliability data sets. The results of numerical illustration demonstrate that the proposed TCF and the MVFs provide better estimation and fitting under comparisons. Copyright © 2012 John Wiley & Sons, Ltd.     

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. Although existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this article, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. Supplementary materials for this article are available online.