机载稀疏阵天线空时杂波谱的深入研究

针对机载稀疏阵列天线雷达的空时杂波谱特性进行了研究,发现不同的稀疏方法和时域脉冲数都对杂波协方差矩阵的秩以及杂波谱有影响。提出在稀疏后的阵列中如果在任意两个阵元间所含的半波长的个数小于等于时域脉冲的个数,且同时保持总的阵列孔径不变,那么无论稀疏后阵列中还剩余多少个有效阵元,杂波协方差矩阵的秩都保持不变。用数值仿真对多种稀疏方法下的阵列空时杂波谱进行了分析,数值结果与所提理论相一致。     

北京市能源强度分析的完全因素分解方法

本文从北京市三次产业角度出发，分析能源强度变化情况，并进而分析强度变化的深层次原因。通过利用完全因素分解方法对1998—2006年间北京市的能源强度进行分析，结果表明，北京市能源强度降低的主要动力来自第二产业的能源利用效率的提高，而产业结构的变化对于能源强度的影响较小。在此基础上，笔者认为未来北京市能源强度降低的关键是继续加强技术创新，以及深入优化产业结构等。     

Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms

In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NP-hard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analysed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kjærulff, 1990) and are comparable to results obtained with simulated annealing (Kjærulff, 1990; Kjærulff, 1992).     

用伴随网络法系统求解网络多端口一阶灵敏度

本文提出用稀疏表格法建立多端口伴随同络混合参数矩阵,进而构成了一个有规则的矩阵关系式,可一次求取计算网络多端口一阶灵敏度所需要的支路变量信息。在此基础上,定义了一个矩阵循环乘概念,并给出了系统求解网络多端口输出相对每个元件参数变化的一阶灵敏度公式。     

Modelling spatial intensity for replicated inhomogeneous point patterns in brain imaging

Summary.  Pharmacological experiments in brain microscopy study patterns of cellular activ- ation in response to psychotropic drugs for connected neuroanatomic regions. A typical ex- perimental design produces replicated point patterns having highly complex spatial variability. Modelling this variability hierarchically can enhance the inference for comparing treatments. We propose a semiparametric formulation that combines the robustness of a nonparametric kernel method with the efficiency of likelihood-based parameter estimation. In the convenient framework of a generalized linear mixed model, we decompose pattern variation by kriging the intensities of a hierarchically heterogeneous spatial point process. This approximation entails discretizing the inhomogeneous Poisson likelihood by Voronoi tiling of augmented point patterns. The resulting intensity-weighted log-linear model accommodates spatial smoothing through a reduced rank penalized linear spline. To correct for anatomic distortion between subjects, we interpolate point locations via an isomorphic mapping so that smoothing occurs relative to common neuroanatomical atlas co-ordinates. We propose a criterion for choosing the degree and spatial locale of smoothing based on truncating the ordered set of smoothing covariates to minimize residual extra-dispersion. Additional spatial covariates, experimental design factors, hierarchical random effects and intensity functions are readily accommodated in the linear predictor, enabling comprehensive analyses of the salient properties underlying replicated point patterns. We illustrate our method through application to data from a novel study of drug effects on neuronal activation patterns in the brain of rats.     

Strong orthogonal decompositions and non‐linear impulse response functions for infinite‐variance processes

The author proves that Wold‐type decompositions with strong orthogonal prediction innovations exist in smooth, reflexive Banach spaces of discrete time processes if and only if the projection operator generating the innovations satisfies the property of iterations. His theory includes as special cases all previous Wold‐type decompositions of discrete time processes, completely characterizes when non‐linear heavy‐tailed processes obtain a strong‐orthogonal moving average representation, and easily promotes a theory of non‐linear impulse response functions for infinite‐variance processes. The author exemplifies his theory by developing a non‐linear impulse response function for smooth transition threshold processes, and discusses how to test decomposition innovations for strong orthogonality and whether the proposed model represents the best predictor. A data set on currency exchange rates allows him to illustrate his methodology.     

高阶变系数线性微分方程的新算子解法(续)

本文根据变系数线性做分方程的新算子解法，给出某些可积类型．     

稀疏数据立方的一种快速计算方法

提出了一种新的稀疏数据立方计算方法CFD(Computation by Functional Dependencies),它采用自底向上的顺序划分数据立方的各个维,同时按自顶向下的顺序计算聚集,从而充分利用了数据立方的各个group-by之间的共性.CFD和以前所有的立方计算方法不同:一是既能避免计算冰山数据立方中不满足最小支持度的group-by,同时也利用了聚集函数的特征来加速聚集计算;二是利用维之间的依赖关系能有效减少划分时间.实验表明,和以前的算法相比CFD的性能明显提高.     

矩阵方程AXAT+BYBT=C的对称半正定解

研究矩阵方程AXAT+BXBT=C的对称半正定解 .利用广义奇异值分解给出了该矩阵方程有解的充要条件及解的通式     

Allowing for heterogeneity in the decomposition of measures of inequality in health*

This paper shows how recently developed regression-based methods for the decomposition of health inequality can be extended to incorporate heterogeneity in the responses of health to the explanatory variables. We illustrate our method with an application to the GHQ measure of psychological well-being taken from the British Household Panel Survey. The results suggest that there is an important degree of heterogeneity in the association of health to explanatory variables across birth cohorts and genders which, in turn, accounts for a substantial percentage of the inequality in observed health.*Data from the British Household Panel Survey (BHPS) were supplied by the ESRC Data Archive. Neither the original collectors of the data nor the Archive bear any responsibility for the analysis or interpretations presented here. This chapter derives from the project “The dynamics of income, health and inequality over the lifecycle” (known as the ECuity III Project), which is funded in part by the European Community's Quality of Life and Management of Living Resources programme (contract QLK6-CT-2002-02297) and the project “La dinámica del estado de salud y los factores socieconómicos a lo largo del ciclo vital. Implicaciones para las políticas públicas”, which is supported by the Fundación BBVA.