基于卫星云图的视场坐标转化模型

利用卫星云图的灰度矩阵,可以换算出经纬度等信息,并由此对不同时刻的卫星数据进行有效的风矢度量。本文运用地心坐标系统,构建了几何模型,结合lagrange插值法给出了灰度矩阵元素行列号对应于经纬度坐标的换算公式;借助Matlab软件得出了灰度矩阵中对应行、列的经纬二维数组;并用Matlab将灰度矩阵转化为卫星云图,并添加经纬信息,统一坐标系统后用Matlab绘制海岸线。     

宝鸡钛产业发展的思考

宝鸡走向全国,面向世界的优势和城市名片就是“中国钛城”。根据SWOT态势分析,宝鸡要建设具有国际竞争力的“中国钛城”,就应当提高宝鸡市钛材加工能力,增强钛材的竞争力。进行钛加工资源的重组、整合,全力推进骨干企业集团化、规模化,实现聚集效应。加大科技投入力度,努力形成适合我国国情的钛合金材料体系,真正使钛产业成为宝鸡最具竞争优势的产业。     

三端点区间数互反判断矩阵的排序方法研究

本文研究了三端点区间数互反判断矩阵的一致性和排序方法。在三端点区间数互反判断矩阵完全一致性概念的基础上,首次将矩阵特征向量思想引入三端点区间数互反判断矩阵之中——研究了三端点区间数互反判断矩阵的一致性与权重向量之间的类似特征向量关系,并运用线性规划模型给出一种处理三端点区间数决策者对方案属性权重的方法,然后通过三端点区间数权重向量的期望值,进行方案集结排序。最后通过具体的案例,验证了所提出方法的有效性和适用性。     

Trajectories of Fertility and Household Composition in the Demographic Profile of the Czech Republic

The household composition matrix is a representation of the demographic structure of households, specific to age groups of household members and household heads. As such, the matrix reflects also the environmental conditions, housing in particular, that mould households' demographic structure. By specifically depicting the presence of children in households, household composition could be viewed as gauging fertility within the context of housing conditions. This stance is examined in an application to Czech census data for the year 1991, at the commencement of an intense process of socio-economic transformation that accompanied the collapse of communism across Eastern Europe. Within this process, housing had an inadvertent impact upon the structure of households in general, and upon fertility decline in particular. By using the standard matrix representation of household composition, correspondence between trajectories of age-specific fertility and household composition emerge throughout the Czech Republic. This correspondence illustrates the potential household composition analysis carries for fertility measurement and estimation in rapidly changing economic environments.     

Dynamics of age- and size-structured populations in fluctuating environments: Applications of stochastic matrix models to natural populations

Recent developments of the theory of stochastic matrix modeling have made it possible to estimate general properties of age- and size-structured populations in fluctuating environments. However, applications of the theory to natural populations are still few. The empirical studies which have used stochastic matrix models are reviewed here to examine whether predictions made by the theory can be generally found in wild populations. The organisms studied include terrestrial grasses and herbs, a seaweed, a fish, a reptile, a deer and some marine invertebrates. In all the studies, the stochastic population growth rate (ln λ _s ) was no greater than the deterministic population growth rate determined using average vital rates, suggesting that the model based only on average vital rates may overestimate growth rates of populations in fluctuating environments. Factors affecting ln λ _s include the magnitude of variation in vital rates, probability distribution of random environments, fluctuation in different types of vital rates, covariances between vital rates, and autocorrelation between successive environments. However, comprehensive rules were hardly found through the comparisons of the empirical studies. Based on shortcomings of previous studies, I address some important subjects which should be examined in future studies.     

两种区间数据主成分分析方法的比较研究

针对顶点主成分分析算法（VPCA）计算量会随着变量个数的增加而按指数速度增长的问题,Cazes P提出一种简化算法,通过直接计算VPCA的相关系数矩阵,可以消除大量的冗余计算,解决VPCA的维数灾难问题。文章通过对这两种方法的计算过程和计算结果进行比较,说明这两种方法在计算结果上是完全等价的,但是,Cazes P提出的简化算法的计算过程更简单、所占据的存储空间更小、计算速度更快,实验分析进一步验证了理论分析的相关结论。     

实数域上一类矩阵方程的解

研究了实数域上一类矩阵方程解的性质、结构,给出了相应的算法步骤、算例,并把相应的结论推广到此类型的矩阵方程上.     

基于PEST-SWOT矩阵的动漫期刊发展战略分析

动漫期刊作为动漫产业的先行者与试验田,其发展受多种因素影响。基于PEST-SWOT矩阵分析方法,可对动漫期刊的发展环境系统全面的进行分析。根据矩阵中不同组合,相应提出动漫期刊实行产业链运营、建设新媒体平台、组建品牌化方阵、打造民族风动漫的发展战略。     

风险偏好与属性约简在决策问题中的应用研究

将决策者风险偏好与属性约简算法应用到具有大量属性的决策问题,提出先对决策者分类再进行决策的策略． 根据决策者的风险偏好特征将其分为风险规避型、风险中立型、风险偏好型,针对不同类型的决策者分别采取相应的算法,提取有效属性并利用有效属性进行决策; 针对不同风险偏好的决策者,提出相应的风险偏好预期规则; 提出基于优势关系辨析矩阵的属性赋权算法及基于属性值优势度矩阵的进行信息融合与排序算法; 最后通过属性值为实数与区间数的两个实际案例表明该算法的科学合理性．     

A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging

Studies on diffusion tensor imaging (DTI) quantify the diffusion of water molecules in a brain voxel using an estimated 3 × 3 symmetric positive definite (p.d.) diffusion tensor matrix. Due to the challenges associated with modelling matrix‐variate responses, the voxel‐level DTI data are usually summarized by univariate quantities, such as fractional anisotropy. This approach leads to evident loss of information. Furthermore, DTI analyses often ignore the spatial association among neighbouring voxels, leading to imprecise estimates. Although the spatial modelling literature is rich, modelling spatially dependent p.d. matrices is challenging. To mitigate these issues, we propose a matrix‐variate Bayesian semiparametric mixture model, where the p.d. matrices are distributed as a mixture of inverse Wishart distributions, with the spatial dependence captured by a Markov model for the mixture component labels. Related Bayesian computing is facilitated by conjugacy results and use of the double Metropolis–Hastings algorithm. Our simulation study shows that the proposed method is more powerful than competing non‐spatial methods. We also apply our method to investigate the effect of cocaine use on brain microstructure. By extending spatial statistics to matrix‐variate data, we contribute to providing a novel and computationally tractable inferential tool for DTI analysis.