部分相干厄米-高斯列阵光束通过湍流大气传输的方向性

推导出了部分相干厄米-高斯(H-G)列阵光束通过湍流大气传输的二阶矩束宽和远场发散角的解析公式。采用远场发散角作为光束方向性的评价参数,研究了部分相干H-G列阵光束通过湍流大气传输的方向性。研究表明:在一定条件下,部分相干H-G列阵光束与对应的高斯光束不论在自由空间还是湍流大气中均具有相同的方向性。此外,进一步研究发现,在自由空间中,由远场发散角和归一化远场平均光强分布所表征的部分相干H-G列阵光束的方向性是不一致的,但湍流可以使得两种描述相一致。这一结论与高斯-谢尔模型(GSM)列阵光束的相关结论存在差异。在自由空间中,与高斯光束具有相同远场发散角的非相干合成的GSM列阵光束与对应的高斯光束具有相同的归一化远场光强分布。     

The clique operator on matching and chessboard graphs

Given positive integers m,n, we consider the graphs G_n and G_m,n whose simplicial complexes of complete subgraphs are the well-known matching complex M_n and chessboard complex M_m,n. Those are the matching and chessboard graphs. We determine which matching and chessboard graphs are clique-Helly. If the parameters are small enough, we show that these graphs (even if not clique-Helly) are homotopy equivalent to their clique graphs. We determine the clique behavior of the chessboard graph G_m,n in terms of m and n, and show that G_m,n is clique-divergent if and only if it is not clique-Helly. We give partial results for the clique behavior of the matching graph G_n.     

Improved prediction for a multivariate normal distribution with unknown mean and variance

The prediction problem for a multivariate normal distribution is considered where both mean and variance are unknown. When the Kullback–Leibler loss is used, the Bayesian predictive density based on the right invariant prior, which turns out to be a density of a multivariate t-distribution, is the best invariant and minimax predictive density. In this paper, we introduce an improper shrinkage prior and show that the Bayesian predictive density against the shrinkage prior improves upon the best invariant predictive density when the dimension is greater than or equal to three.     

多元随机序列泛函的强偏差定理

利用熵密度和样本偏差率的概念,建立了多元随机序列泛函关于条件期望的用不等式表示的强极限性质(称之为强偏差定理),在推论部分得到了非齐次马氏链的强偏差定理和随机条件概率的调和平均值的极限性质等相关结论.证明中给出了将条件矩母函数应用于研究多元随机序列泛函的强极限性质的一种途径.     

Optimization and numerical simulation for the extraction system of the H- multicusp ion source

A new ion source has been designed and manufactured for the CYCLONE30 accelerator, which has a much advanced performance compared with the original. It is expected that the newly designed ion source extraction system will transport a very large percentage of the beam without deteriorating the beam optics, which is designed to deliver an H- beam at 30 keV. The accelerator assembly consists of three circular aperture electrodes made of copper. The simulation study was focused on finding parameter sets that raise the beam perveance as large as possible and which reduce the beam divergence as low as possible. Ion beams of the highest quality are extracted whenever the half-angular divergence is minimum, for which the perveance current intensity and the extraction gap have optimum values. The triode extraction system is designed and optimized by using CST software （for Particle Beam Simulations）. The physical design of the extraction system is given in this paper. From the simulation results, it is concluded that it is possible to achieve this goal by decreasing the thickness of the plasma electrode, shortening the first gap, and adjusting the acceleration electrode voltage.     

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding

The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.     

高斯光束经圆形光阑衍射的远场发散特性

线偏振高斯光束经圆形光阑衍射后,其远场可表示成互相正交的横电(TE)项和横磁(TM)项之和。利用TE项和TM项的远场能流分布,导出了高斯衍射光束的TE项和TM项远场功率的解析表达式,由此可度量TE项和TM项在远场占总功率的比例。基于能流二阶矩的定义,给出了高斯衍射光束、TE项和TM项远场发散角的解析式以及三者远场发散角间的关系通式,重点分析了f参数和截取参数对远场发散角的影响。结果表明:随着f参数的增大,远场发散角先增大后趋向于各自的饱和值。截取参数对远场发散角的影响与f参数相关,当f参数较大时,截取参数对远场发散角的影响不明显;当f参数适中时,随着截取参数的增大,远场发散角先减小后趋向于各自的最小值;但当f参数较小时,高斯衍射光束和TM项二者的远场发散角出现一定的波动性。     

两种反常积分敛散性的判别方法

介绍了两种判别反常积分敛散性的判别方法.     

Numerical modelling of the stability of loaded shells of revolution containing fluid flows

A mixed finite-element algorithm is proposed to study the dynamic behavior of loaded shells of revolution containing a stationary or moving compressible fluid. The behavior of the fluid is described by potential theory, whose equations are reduced to integral form using the Galerkin method. The dynamics of the shell is analyzed with the use of the variational principle of possible displacements, which includes the linearized Bernoulli equation for calculating the hydrodynamic pressure exerted on the shell by the fluid. The solution of the problem reduces to the calculation and analysis of the eigenvalues of the coupled system of equations. As an example, the effect of hydrostatic pressure on the dynamic behavior of shells of revolution containing a moving fluid is studied under various boundary conditions. __________ Translated from Prikladnaya Mekhanika i Tekhnicheskaya Fizika, Vol. 49, No. 2, pp. 185–195, March–April, 2008     

Computation of Kullback–Leibler Divergence in Bayesian Networks

Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.