高维纵向数据群点的刚体结构检验方法

本文中, 我们提出一种检验高维纵向数据群点在运动中是否保持刚体结构的方法, 并给出高维群点变形系数的定义. 对群点刚体结构的检验, 可以把握群点运动的主要特征, 并便于对群点的未来运动趋势做总体预测. 通过仿真案例以及分析比较中国东部、西部城市群的经济发展过程中多元性的变化, 验证了方法的合理性和应用价值.     

机电产品可靠性寿命试验数据的灰色预测方法

采用灰色系统预测理论对产品可靠性寿命试验数据进行预测,提出了建立产品可靠性寿命试验数据的灰色预测NGM(1,1)模型的方法,并通过采用试验数据序列与预测数据序列总体分布函数相等性检验方法确认灰色预测NGM(1,1)模型用于产品可靠性寿命试验数据预测是可行的.算例结果表明,采用灰色预测方法预测产品可靠性寿命试验数据并进行相关的分布函数参数估计有较高的精度,可达到缩短试验时间和节约试验费用目的.     

基于改进的SVR模型在年降水量预测中的应用

鉴于降水量数据的高维非线性性和周期性,建立了支持向量回归(SVR)预测模型用于降水量预测,由于对该模型输入特征的选取极为重要,因此提出了一种基于季节自回归(SARI)的输入特征选取方法.利用已有的降水量数据建立SARI模型,通过观察模型表达式提取建立SVR模型所需的输入特征用于训练支持向量机,并通过网格参数寻优法确定SVR模型的参数,进行降水量预测.实例分析中,应用此模型对黄土丘陵半干旱区域的降水量进行预测,将预测结果与季节时间序列(SARIMA)模型的预测结果进行对比,结果表明,模型具有更高的预测精度和拟合优度,可以用于降水量的预测.     

基于POT方法的极值理论在基金净值预测中的应用

为对基金净值数据进行建模,根据基金净值样本数据的尾部特点,建立极大,极小值分布的GPD模型,运用POT方法确定临界值,进而对参数进行估计,并对模型进行检验.最后,运用建立的模型对一些极值点进行预测.所得结果很好地描述了数据特点,对极值点的预测符合实际.     

淮安市PM2.5的分布特征与灰色预测模型

研究对象是淮安市主要大气污染物中PM2.5在2013-2016年间的观测数据,从两个方面对PM2.5的浓度进行了研究.一方面,利用非参数假设检验中的卡方拟合检验与W检验等方法研究了PM2.5浓度的分布特征.研究发现,污染物PM2.5的日均浓度服从对数正态分布、月均浓度服从正态分布.另一方面,建立了PM2.5年均浓度的精度为一级可用于长期预测的GM(1,1)预测模型,利用预测模型对未来五年中PM2.5的浓度水平进行了预测.     

利用提升小波的蛋白质相互作用特征提取

利用提升小波从蛋白质序列中提取出它们相互作用的频谱特征,经支持向量机训练学习后,用于预测蛋白质间的相互作用.模拟计算结果表明,在阳性数据和阴性数据平衡的前提下,利用提升小波获取的低维蛋白质相互作用特征向量可以得到较高预测精度.进一步阐述了不同物种的蛋白质相互作用网络有着不同特征,为了得到更准确的预测结果,需要利用不同的方法提取蛋白质相互作用的特征.     

中国八个城市居民食品摄入量与胃癌死亡率的对应分析

为探讨食品摄入量及胃癌死亡率与中国部分城市之间的关系,用对应分析方法对食品摄入量及胃癌死亡率与中国部分城市之间的关系进行分析.对应分析将8个城市16项食品摄入量及胃癌死亡率分成四个点群:点群族I城市包括上海、南京、苏州,食品包括大米、豆制品、绿色蔬菜、咸菜、猪肉、鱼虾类、绽粉类;点群族II城市包括北京、天津、西安,食品包括面粉、浅色蔬菜、水果、乳类、植物油;点群族III城市包括长春,食品包括干豆类、蛋类;点群族IV城市包括沈阳,食品包括薯类.说明中国部分城市与胃癌死亡率存在地区性差异,饮食中应减少精制米类及食盐的摄入,而增加面类和植物油的摄入.     

基于无偏灰色PSOMarkov优化模型的加油站便利店零售销量预测方法研究

加油站便利店是一种新兴的零售业态,准确地预测其零售销量具有重要意义.以往的销量预测模型多为单一模型,提出了一种基于粒子群优化的无偏灰色PSOMarkov预测方法.首先,基于灰色系统理论建立了无偏GM(1,1)预测模型,消除了常规GM(1,1)预测模型的固有偏差.其次,利用Markov理论对无偏GM(1,1)预测模型的相对残差进行了修正,模型能较好地体现数据的波动特征.最后,利用改进的粒子群优化算法白化无偏GM(1,1)-Markov预测模型灰区间的参数,得到无偏灰色PSO-Markov预测模型.通过云南昆明市红瓦副加油站便利店的零售销量案例表明,模型能提高预测模型的精度.模型可用于加油站便利店的商品销售预测,并为企业的经营决策提供依据.     

自适应神经模糊推理系统在交通污染物浓度预测中的应用

城市交通带来的废气排放已经成为城市大气污染的主要来源之一。交通污染问题的成因和机理较为复杂,变化规律具有较强非线性和周期性特征。将自适应神经模糊推理系统(adaptive neuro fuzzy inference system,ANFIS)应用于交通污染物浓度时序数据预测时呈现出良好的泛化能力。本文以长沙市CO小时浓度数据为研究目标,通过分析CO浓度时序数据的自相关性、偏自相关性,以及交通流对CO浓度的时滞性影响,确定ANFIS预测模型的输入变量。结果表明,相较于传统的时间序列预测模型以及机器学习模型,ANFIS模型预测结果具有更高的精度,能够对交通环境污染进行预测及预警,为防止城市灾害性大气污染事件发生奠定理论研究基础并提供有效决策支持。     

带方向信息的薄板样条插值函数及其应用北大核心CSCD

薄板样条(TPS)函数是一个很好的形变分析工具,常用于图像配准,传统的薄板样条函数只是利用特征点的坐标信息,而现实中很多特征点都带有方向信息,比如指纹细节点和sift特征点.为了在薄板样条函数中利用方向信息,本文在传统薄板样条目标函数基础上增加了方向平行惩罚项及方向一致性惩罚项,根据变分法的结论,目标函数的求解转换为求解一个微分方程,用格林函数法求解这个微分方程,从而得到带方向信息的薄板样条函数新形式.本文得到的薄板样条函数适合于任意维度的点集,在指纹图像配准和人造点集插值上的实验表明,和传统薄板样条函数相比,本文提出的薄板样条函数对于带方向信息的图像配准和点集插值更加准确.     

Multiple imputation in principal component analysis

The available methods to handle missing values in principal component analysis only provide point estimates of the parameters (axes and components) and estimates of the missing values. To take into account the variability due to missing values a multiple imputation method is proposed. First a method to generate multiple imputed data sets from a principal component analysis model is defined. Then, two ways to visualize the uncertainty due to missing values onto the principal component analysis results are described. The first one consists in projecting the imputed data sets onto a reference configuration as supplementary elements to assess the stability of the individuals (respectively of the variables). The second one consists in performing a principal component analysis on each imputed data set and fitting each obtained configuration onto the reference one with Procrustes rotation. The latter strategy allows to assess the variability of the principal component analysis parameters induced by the missing values. The methodology is then evaluated from a real data set.     

含主应力轴旋转的土体一般应力应变关系

本文利用矩阵理论,分析了使主应力轴产生旋转的应力增量特性,并将一般应力增量分解为与应力共主轴部分及使之产生旋转部分。据此,将含主应力轴旋转的复杂三维问题简化为三维应力应变共轴问题和三主值不变绕某一主轴旋转问题的结合,大大简化了分析的难度。文中还结合有关模型给出了一般三维问题的具体计算方法。     

Principal differential analysis of the Aneurisk65 data set

We explore the use of principal differential analysis as a tool for performing dimensional reduction of functional data sets. In particular, we compare the results provided by principal differential analysis and by functional principal component analysis in the dimensional reduction of three synthetic data sets, and of a real data set concerning 65 three-dimensional cerebral geometries, the AneuRisk65 data set. The analyses show that principal differential analysis can provide an alternative and effective representation of functional data, easily interpretable in terms of exponential, sinusoidal, or damped-sinusoidal functions and providing a different insight to the functional data set under investigation. Moreover, in the analysis of the AneuRisk65 data set, principal differential analysis is able to detect interesting features of the data, such as the rippling effect of the vessel surface, that functional principal component analysis is not able to detect.     

考虑主应力轴旋转的土体本构关系研究进展

对目前国内外考虑主应力轴旋转的试验研究及本构模型研究进行了总结分析,并对进一步研究提出了相应的建议．基于不同的加载条件,从纯主应力轴旋转和耦合主应力轴旋转两个方面,较全面的描述了主应力轴旋转情况下土体的基本变形特性,并对考虑主应力轴旋转的土体变形试验提出了进一步研究的建议．较为系统地评述了当前较有代表性的考虑主应力轴旋转的土体本构模型（边界面模型、多机构模型、运动硬化模型和广义塑性模型）,得出了广义塑性模型更适合用来描述考虑主应力轴旋转的土体变形特性的结论．总结未来考虑主应力轴旋转的土体本构关系研究的主要方向是:把握主应力轴旋转情况下土体变形的本质特性,建立推理严密、形式简单、适用方便的本构模型,并用来指导工程实践．     

A principal component method to impute missing values for mixed data

We propose a new method to impute missing values in mixed data sets. It is based on a principal component method, the factorial analysis for mixed data, which balances the influence of all the variables that are continuous and categorical in the construction of the principal components. Because the imputation uses the principal axes and components, the prediction of the missing values is based on the similarity between individuals and on the relationships between variables. The properties of the method are illustrated via simulations and the quality of the imputation is assessed using real data sets. The method is compared to a recent method (Stekhoven and Buhlmann Bioinformatics 28:113–118, 2011) based on random forest and shows better performance especially for the imputation of categorical variables and situations with highly linear relationships between continuous variables.     

城市主导特色的评价模型研究

本文从影响城市特色因素中设定体现城市主导特色的基本要素,并建立城市主导特色评价体系,利用回归模型分析城市特色演变轨迹及城市新特色的形成和发展。     

Parallel Principal Axes

Parallel principal axes are introduced and used to investigate the structure of multivariate distributions. In the class of elliptical distributions, it is shown that self-consistency of parallel principal axes characterizes normality. Parallel principal axes are used to illustrate the equivalent event fallacy in the context of self-consistency. Finally, the idea of piecewise principal component axes is introduced and related to an allometric extension model.     

Comparison of the flow of viscoelastic media in shear and in tension

A geometrical method of comparing steady-state flow in uniaxial tension and in simple shear is proposed. This method is based on a comparison of the deformation regimes relative to the principal axes.     

On the statistical mechanics of deformable media

Random stress and strain fields are examined in relation to machine parts made of materials of the randomly reinforced fiberglass type subjected to random external loads. The conditions of existence of the principal axes of the fourth-order tensor representing the second-order central moments of a plane random stress or strain field are determined. A method of determining the principal axes and principal values is described. A numerical example is worked.V. A. Steklov Mathematical Institute, Sverdlovsk Branch, Academy of Sciences of the USSR. S. M. Kirov Ural Polytechnic Institute, Sverdlovsk. Translated from Mechanika Polimerov, Vol. 4, No. 4, pp. 618–623, July–August, 1968.     

Principal component analysis for histogram-valued data

This paper introduces a principal component methodology for analysing histogram-valued data under the symbolic data domain. Currently, no comparable method exists for this type of data. The proposed method uses a symbolic covariance matrix to determine the principal component space. The resulting observations on principal component space are presented as polytopes for visualization. Numerical representation of the resulting polytopes via histogram-valued output is also presented. The necessary algorithms are included. The technique is illustrated on a weather data set.