电磁发射器的原理与应用

传统的化学推进技术已不能满足人类的进一步要求，从原理和能源上变革发射技术势在必行，因此电磁发射技术便应运而生。因其在国防、航天等领域应用前景广阔，目前国内外电磁发射技术发展迅速。文章论述了电磁发射技术的发展历史、现状和电磁发射器的基本原理和广阔的应用前景。     

THEORETICAL APPROACHES TO PROTEIN STRUCTURE-FUNCTION ANALYSIS (Ⅰ)——DIRECTED PERTURBATION CONFORMATIONAL ANALYSIS

A new computational technique called directed perturbation conformational analysis has been developed for use in protein model building and structure-function studies. Designed to perform an efficient local search of a macromolecular potential energy surface, the algorithm can be used to locate multiple energy minimum conformers via low energy transition state structures from a single starting or trial structure. The algorithm contains developments to stabilize transition state optimizations for systems described by many degrees of freedom displaying anharmonic potential energy surfaces. It has been found to be efficient in the generation of alternative equilibrium structures from a given trial structure when compared with those generated from a standard molecular dynamics simulation of N-acetyl, N'-methyl-deca-L-alaninamide.     

电磁波在横向分层不均匀介质上的反射与透射的解析研究

对平面电磁波入射到横向分层不均匀介质上时的情形，麦克斯韦方程组可简化成马丢（Ｍａｔｈｉｅｕ）方程和希尔（Ｈｉｌ）方程，据此可获得透射波和反射波场的解析表达式。从所获得的解我们得知，介质的非均匀性可改变入射波的强度分布和偏振状态，这种改变取决于多种因素，比如介质非均匀的具体形式，入射波的入射和偏振方向等；反射波的强度分布可被也可不被改变，取决于波和介质的参数     

Learning from incomplete data in Bayesian networks with qualitative influences

Domain experts can often quite reliably specify the sign of influences between variables in a Bayesian network. If we exploit this prior knowledge in estimating the probabilities of the network, it is more likely to be accepted by its users and may in fact be better calibrated with reality. We present two algorithms that exploit prior knowledge of qualitative influences in learning the parameters of a Bayesian network from incomplete data. The isotonic regression EM, or irEM, algorithm adds an isotonic regression step to standard EM in each iteration, to obtain parameter estimates that satisfy the given qualitative influences. In an attempt to reduce the computational burden involved, we further define the qirEM algorithm that enforces the constraints imposed by the qualitative influences only once, after convergence of standard EM. We evaluate the performance of both algorithms through experiments. Our results demonstrate that exploitation of the qualitative influences improves the parameter estimates over standard EM, and more so if the proportion of missing data is relatively large. The results also show that the qirEM algorithm performs just as well as its computationally more expensive counterpart irEM.     

Detecting Features in Spatial Point Processes with Clutter via Local Indicators of Spatial Association

We consider the problem of detecting features of general shape in spatial point processes in the presence of substantial clutter. Our goal is to remove clutter from images where one or several features are present and have to be detected. We use a method based on local indicators of spatial association (LISA) functions, particularly on the development of a local version of the product density which is a second-order characteristic of spatial point processes. The classification method is built upon a stochastic version of the EM algorithm (SEM). This method can be applied without user input about the number or shapes of the regions. Our proposal, compared with the kth nearest-neighbor technique, is tested through simulated examples yielding high detection and low false-positive rates. Two real case studies of connective loose tissues in human organs and earthquakes are also presented.     

基于EM算法隐Markov模型参数估计

描述最大似然参数估计问题,介绍如何用EM算法求解最大似然参数估计.首先给出EM算法的抽象形式,然后介绍EM算法的一个应用:求隐Markov模型中的参数估计.用EM算法推导出隐Markov模型中参数的迭代公式.     

Acceleration of the EM algorithm using the vector epsilon algorithm

The expectation–maximization (EM) algorithm is a very general and popular iterative computational algorithm to find maximum likelihood estimates from incomplete data and broadly used to statistical analysis with missing data, because of its stability, flexibility and simplicity. However, it is often criticized that the convergence of the EM algorithm is slow. The various algorithms to accelerate the convergence of the EM algorithm have been proposed. The vector ε algorithm of Wynn (Math Comp 16:301–322, 1962) is used to accelerate the convergence of the EM algorithm in Kuroda and Sakakihara (Comput Stat Data Anal 51:1549–1561, 2006). In this paper, we provide the theoretical evaluation of the convergence of the ε-accelerated EM algorithm. The ε-accelerated EM algorithm does not use the information matrix but only uses the sequence of estimates obtained from iterations of the EM algorithm, and thus it keeps the flexibility and simplicity of the EM algorithm.     

Constrained Nonparametric Maximum Likelihood via Mixtures

Abstract

This article discusses a new technique for calculating maximum likelihood estimators (MLEs) of probability measures when it is assumed the measures are constrained to a compact, convex set. Measures in such sets can be represented as mixtures of simple, known extreme measures, and so the problem of maximizing the likelihood in the constrained measures becomes one of maximizing in an unconstrained mixing measure. Such convex constraints arise in many modeling situations, such as empirical likelihood and estimation under stochastic ordering constraints. This article describes the mixture representation technique for these two situations and presents a data analysis of an experiment in cancer genetics, where a partial stochastic ordering is assumed but the data are incomplete.     

Simple Accurate Approximation of Likelihood Profiles

Likelihood profiles for arbitrary functions of the model parameters are useful for constructing likelihood ratio confidence intervals, diagnosing linear approximation intervals, suggesting linearizing transforms, and many other purposes. This article investigates a simple integration method for producing accurate approximations to likelihood profiles that avoid problems associated with producing exact profiles. A basic theorem guarantees that the approximation can achieve any desired precision. In many cases the method requires no more than adding a few lines of code to that required to produce the maximum likelihood estimate of the parameter vector. Standard methods for computing likelihood profiles are based on solving a sequence of constrained maximum likelihood problems. Each problem generates a point on the profile. An earlier article proposed an integration method that generates the entire profile directly by solving a differential equation. That method, however, requires the Hessian of the log-likelihood which can be difficult to produce. The method considered here requires only the gradient. It may be particularly useful in conjunction with the EM algorithm which typically does not produce any type of confidence interval.     

二参数广义Rayleigh分布

为了研究某些工程过程的寿命,需要一个能够适应这些过程性质的寿命模型.与简单模型相比,基于寿命分布的广义模型在建模过程中更具有直观的适用性和吸引力.本文介绍一个二参数广义Rayleigh分布,推导出了其矩生成函数和k阶矩的表达式,通过对样本点进行归一化,得到了它的近似分布函数、概率密度函数、生存函数、风险率函数等.在此基...