关联规则快速聚焦算法研究与实现

提出了一种实用的快速聚焦关联规则更新算法。在需要反复调整最小支持度的情况下,如何充分利用以往挖掘过程中的信息,避免多次扫描数据集;在如何有针对性的产生候选项集,从而减少候选集的规模;在如何提高候选项集的支持事务计数的效率等方面进行了研究,给出了算法的具体实现。通过分析,本算法是可行的。     

Structural behaviour of fibre metal laminates subjected to a low velocity impact

Structural impact tests were first presented to cover typical fibre metal laminates (FMLs) subjected a low velocity projectile impact,which produced the corresponding load-displacement traces and deformation/failure modes for the validation of numerical models.Finite element (FE) models were then developed to simulate the impact behaviour of FMLs tested.The aluminium (alloy grade 2024-0) layer was modelled as an isotropic elasto-plastic material up to the on-set of post failure stage,followed by shear failu...     

快速辐射度算法的实现研究

当虚拟场景发生变化时,扩展的逐步求精算法作为一种快速的辐射度算法,大量减少了重新计算辐射度所花费的时间。对扩展的逐步求精算法进行研究,并利用OpenGL对算法进行了实现。通过在实验中对虚拟场景的表面反射率和几何属性进行改变,获得了一些证明算法有效性的数据,从而验证了该算法的有效性。     

基于Web网页语料构建动态语言模型

为语音识别系统构建语言模型，首先要进行语料准备，语料来源决定语言模型的性能。Web网页中涵盖了各种最新的语言现象，为语料准备提供了最多样化的资源。但Web网页中语义完整字串通常夹杂在格式、标记、广告等无用字串中。首先介绍语言模型的训练算法和更新方法，继而提出一种从HTML文档提取用于训练语言模型的语义完整汉字字串的算法，最后给出语料提取实验结果、语言模型训练结果和语言模型的动态更新结果。为基于Web网页语料动态更新语言模型提供了一个完整的解决方案。     

Universality two-dimensional Kauffman model for parallel and sequential updating

Kauffman's random Boolean nets are studied on the square lattice by computer simulation comparing parallel and sequential updating of the automata. At the phase transition the fractal dimensions of time and actual damage are found to be independent of the updating method.     

On progressively truncated maximum likelihood estimators

Summary The basic regularity conditions pertaining to the asymptotic theory of progressively truncated likelihood functions and maximum likelihood estimators are considered, and the uniform strong consistency and weak convergence of progressively truncated maximum likelihood estimators are studied systematically. Work done during the first author's visit (as a visiting scholar) to the University of North Carolina at Chapel Hill, supported by the Ministry of Education of the Japanese Government. Work supported by the (U.S.) National Heart, Lung and Blood Institute, Contact NIH-NHLBI-F1-2243-L.     

Progressive hedging and tabu search applied to mixed integer (0,1) multistage stochastic programming

Many problems faced by decision makers are characterized by a multistage decision process with uncertainty about the future and some decisions constrained to take on values of either zero or one (for example, either open a facility at a location or do not open it). Although some mathematical theory exists concerning such problems, no general-purpose algorithms have been available to address them. In this article, we introduce the first implementation of general purpose methods for finding good solutions to multistage, stochastic mixed-integer (0, 1) programming problems. The solution method makes use of Rockafellar and Wets' progressive hedging algorithm that averages solutions rather than data. Solutions to the induced quadratic (0,1) mixed-integer subproblems are obtained using a tabu search algorithm. We introduce the notion of integer convergence for progressive hedging. Computational experiments verify that the method is effective. The software that we have developed reads standard (SMPS) data files.     

Updating beliefs about variables given new information on how those variables relate

Bayesian techniques specify how to update beliefs about a variable given information on that variable or related variables. In many cases, statistical analyses also provide information about the relationship between variables, but the Borel Paradox prohibits many natural ways of updating beliefs conditioned on information about a relationship. This paper presents a method by which beliefs can be updated without violating the Borel Paradox under certain circumstances. We apply our approach to relationships specified by a statistical model (i.e., regression), and relationships described by statistical simulation.