基于COM／COM＋的三层C／S模型

在分析了两层C／S模型的局限性的基础上，深入探讨了基于COM／COM 的三层CAS模型，并介绍了这种模型在企业人事管理系统中的应用。最后指出了基于COM／COM 的三层C／S模型的优越性。采用COM／COM 技术可减少企业级软件的开发难度，降低成本，利于软件的升级和维护。     

注射流动模拟软件HSCAE4.0的技术特色

CAE软件有不少,为配合本期的主题,我们挑选了部分软件介绍给读者。如果读者需要了解更多的CAE软件,也可以来信点题,我们会在以后的文章中涉及。华塑软件研究室推出了以流动模拟软件为代表的塑料注射成形过程系列化分析软件HSCAE。该软件具有自主版权,无论在数学模型、算法、数据结构还是商品化程度均可与国外同类软件相媲美。本支重点介绍HSCAE4．0中注射流动模拟软件的主要功能和技术特点。1．友好的图形界面HSCAE4．0是在WINDOWS95／NT环境下采用面向对象程序设计方法开发的塑料注射成形过程系列化分析软件,为用户提供…     

军用通信电源机组舱通风散热的数值模拟

以军用通信电源机组舱为研究对象,采用了ANSYS／FLOTRAN软件对其通风散热系统进行数值模拟,通过与实验结果相比较,分析了各种空气紊流流动数学模型和对流项各阶分离方案的优劣,并采用最佳的模型和分离方案来模拟机组舱在进风口和抽风口位置改变后的通风散热情况,从而为系统的改进设计和运行提供切实可行的理论基础。     

气化炉与辐射废锅接口壁面熔渣流动数值模拟

应用商业软件FLUENT,对用于200 MW级IGCC发电的2 000 t 煤/d级多喷嘴对置式水煤浆气化炉接口内合成气和熔渣的流动进行了模拟与研究.经过合理简化和假设,建立了数学模型.采用重整化群(RNG)k-ε湍流模型计算接口内湍流流动,应用VOF模型对熔渣层与合成气相界面进行追踪,应用DTRM模型进行辐射模拟计算.模拟结果表明:在接口倾角小于35.时,熔渣不能顺利流下,将不断堆积且很可能形成渣堵;而在较大倾角以及接口直筒段内,熔渣均能稳定挂壁并对耐火砖层或水冷壁形成保护,且不同角度下的渣层厚度变化不大.分析了熔渣在流动过程中的现象及特征.经检验,模拟结果与相关文献及实验结果吻合较好.     

水中对抗仿真系统实体模型管理问题的研究

水中对抗作战仿真系统是模拟水中武器对抗作战的网上仿真演练系统。对于大规模的仿真演练,必然要求开发有效的、功能完善的模型管理系统。该文介绍了开发仿真实体模型管理系统过程中的方法：首先从系统仿真建模工作开始进行分析和研究,采用层次化、模块化建模并将仿真模型与仿真实验平台有效分离,规范了建模过程,分析了采用DLL技术在模型实现中的应用,并根据模型的特点,分析了采用模型库与模型管理系统对仿真模型进行管理的方法。     

凸体、旋转体的体几何模型与内部可视化

在可视化与ＣＡＤ／ＣＡＭ的许多应用问题中,不但需要描述三维实体的外形,也要求表达实体的内部信息。传统的面壳模型只能描述实体的外形,无法表达实体的内部信息。针对这种情况,利用Ｂｌｅｎｄｉｎｇ形变技术给出了由实体的面壳模型构造其实体模型（体几何模型）的简易方法,从而把实体的面壳模型与实体模型的表示统一起来。体几何模型可应用于实体内部的可视化及ＣＡＤ／ＣＡＭ中。     

LEO/MEO双层卫星网络的性能分析

基于模型方法和软件仿真方法,对一种低轨／中轨(LEO／MEO)双层卫星网络进行了性能分析．首先建立了双层卫星网络的广义随机Petri网(GSPN)模型,通过对模型进行分析和合理简化,采用SPNP4．0软件进行了网络性能分析．然后,将分析的结果与采用OPNET软件对LEO／MEO双层卫星网络模拟的结果进行比较,验证了模型分析方法的正确性,同时得到了一些新的结论．     

嵌入式系统协同验证环境的设计与实现

介绍了一个用来验证嵌入式系统硬件／软件,及其交互功能的硬件／软件协同验证环境,硬件／软件功能分别使用系统提供的ＥＨＤＬ语言及ＥＳＤＬ语言设计和描述,该协同验证环境由嵌入式软件调试器和嵌入式硬件模拟器组成,其采用了指令集结构的协同模拟模型,利用该集成环境,硬件／软件的设计错误可以在系统制造之前被发现并修改。     

基于等离子流动控制的翼型减阻技术

为研究基于等离子流动控制的减阻技术,基于Langtry-Menter转捩模型提出边界层转捩数值模拟技术.该技术可有效结合转捩模型与湍流模型,用标准模型验证其精确性,为采用等离子流动控制抑制边界层分离和转捩研究提供数值模拟平台.采用基于现象学模型的等离子流动控制数值模拟技术,对流动分离以及边界层转捩抑制进行数值模拟,为基于等离子流动控制的翼型减阻技术提供参考.     

基于Web的多层客户／服务器数据库应用程序

讨论基于Web的多层客户／服务器数据库应用计算模型,并提出采用Delphi建立基于Web的多层客户／服务器数据库应用程序的方法和步骤。     

A 3D model perceptual feature metric based on global height field

Human visual attention system tends to be attracted to perceptual feature points on 3D model surfaces. However, purely geometric-based feature metrics may be insufficient to extract perceptual features, because they tend to detect local structure details. Intuitively, the perceptual importance degree of vertex is associated with the height of its geometry position between original model and a datum plane. So, we propose a novel and straightforward method to extract perceptually important points based on global height field. Firstly, we construct spectral domain using Laplace–Beltrami operator, and we perform spectral synthesis to reconstruct a rough approximation of the original model by adopting low-frequency coefficients, and make it as the 3D datum plane. Then, to build global height field, we calculate the Euclidean distance between vertex geometry position on original surface and the one on 3D datum plane. Finally, we set a threshold to extract perceptual feature vertices. We implement our technique on several 3D mesh models and compare our algorithm to six state-of-the-art interest points detection approaches. Experimental results demonstrate that our algorithm can accurately capture perceptually important points on arbitrary topology 3D model.     

基于分治法的高维大数据集模糊聚类算法

将高维的大数据集随机分成若干个子集，对每个子集聚类采用一种基于遗传算法的高维数据模糊聚类方法。该方法引入了一个模糊非相似矩阵来表示高维样本之间的非相似程度，并将高维样本随机初始化到二维平面，利用遗传算法迭代优化二维样本的坐标值，实现二维样本之间的欧氏距离向样本间的模糊非相似度的趋近。将得到的最优的二维样本用模糊C-均值聚类(FCM)算法聚类，克服了聚类有效性对高维样本空间分布的依赖。实验仿真表明，该算法有较好的聚类效果，且极大地提高了聚类的速度。     

降维映射分析法及其应用

用人工神经网络将多维空间的样本数据降维映射到二维平面上,并生成目标函数的等值线,可全景式地展现出样本数据集操作空间的面貌和特征,由此可直接看出最优操作点或最优操作区域。映射平面上的任意点可通过逆映射算法将其还原到多维空间。本文对这个方法的基本原理进行了简要的描述,并用于操作优化的实例计算。演示的实例说明,这个方法比模式识别方法优越。     

Automatic learners with feedback queries

Automatic classes are classes of languages for which a finite automaton can decide whether a given element is in a set given by its index. The present work studies the learnability of automatic families by automatic learners which, in each round, output a hypothesis and update a long-term memory, depending on the input datum, via an automatic function. Many variants of automatic learners are investigated: where the long-term memory is restricted to be the current hypothesis whenever this exists, cannot be of length larger than the length of the longest datum seen, or has to consist of a constant number of examples seen so far. Learnability is also studied with respect to queries which reveal information about past data or past computation history; the number of queries per round is bounded by a constant.     

Nonlinear analysis of reinforced concrete cylindrical shells

In this paper, analysis of reinforced concrete cylindrical shells is performed using a strain-based finite element. The shell element employed is bidimensional, cylindrical circular and has four-nodes and five nodal degrees of freedom. The nonlinearities due to concrete cracking and yielding of the steel are taken into account. The constitutive models for the materials employ the smeared cracking concept and a finite element layered approach. Concrete is modeled by a strain-induced orthotropic-elastic model under plane state of stress. A bilinear steel model is used and the stress/reversal with Baushinger effect is included. Examples show the good accuracy provided by this analysis.     

A discontinuous Galerkin algorithm for the two-dimensional shallow water equations

A new high-resolution finite element scheme is introduced for solving the two-dimensional (2D) depth-integrated shallow water equations (SWE) via local plane approximations to the unknowns. Bed topography data are locally approximated in the same way as the flow variables to render an instinctive well-balanced scheme. A finite volume (FV) wetting and drying technique that reconstructs the Riemann states by ensuring non-negative water depth and maintaining well-balanced solution is adjusted and implemented in the current finite element framework. Meanwhile, a local slope-limiting process is applied and those troubled-slope-components are restricted by the minmod FV slope limiter. The inter-cell fluxes are upwinded using the HLLC approximate Riemann solver. Friction forces are separately evaluated via stable implicit discretization to the finite element approximating coefficients. Boundary conditions are derived and reported in details. The present model is validated against several test cases including dam-break flows on regular and irregular domains with flooding and drying.     

羊毛衫CAD中尺寸缩放的研究与实现

选择前后中直线、衣长线(肩点)、袖肥中线、袖山深线、领中线作为缩放码的基准线，以M型号上衣尺寸为基本尺寸，采用国际惯例提供的十分法比例计算档差，通过缩放数据库和缩放算法对各种型号的羊毛衫上衣自动进行尺寸缩放，计算机根据缩放后的尺寸进行工艺计算。     

The Fourier Finite-element Approximation of the Lamé Equations in Axisymmetric Domains with Edges

This paper is concerned with a priori error estimates and convergence analysis of the Fourier-finite-element solutions of the Neumann problem for the Lamé equations in axisymmetric domains with reentrant edges. The Fourier-FEM combines the approximating Fourier method with respect to the rotational angle using trigonometric polynomials of degree N (N→∞), with the finite element method on the plane meridian domain of with mesh size h (h→0) for approximating the Fourier coefficients. The asymptotic behavior of the solution near reentrant edges is described by singularity functions in non-tensor product form and treated numerically by means of finite element method on locally graded meshes. For the rate of convergence of the combined approximations in is proved to be of the order      

On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SmartSifter and empirically demonstrates its effectiveness. SmartSifter detects outliers in an on-line process through the on-line unsupervised learning of a probabilistic model (using a finite mixture model) of the information source. Each time a datum is input SmartSifter employs an on-line discounting learning algorithm to learn the probabilistic model. A score is given to the datum based on the learned model with a high score indicating a high possibility of being a statistical outlier. The novel features of SmartSifter are: (1) it is adaptive to non-stationary sources of data; (2) a score has a clear statistical/information-theoretic meaning; (3) it is computationally inexpensive; and (4) it can handle both categorical and continuous variables. An experimental application to network intrusion detection shows that SmartSifter was able to identify data with high scores that corresponded to attacks, with low computational costs. Further experimental application has identified a number of meaningful rare cases in actual health insurance pathology data from Australia's Health Insurance Commission.     

BP神经网络在脑电图信号预测中的应用

由于EEG和BEAM的无创、低成本、能反映大脑病变部位的机能变化等特点，为了预测大脑疾病发生的可能性。设计了一个3层BP网络，并建立了相应的数学模型，以所记录数据中的一部分作为样本，利用Matlab的神经网络工具箱中的算法和函数，对上述神经网络进行训练、仿真、并预测另一部分数据，同时作出待预测数据和预测数据的脑电地形图，进行比较，验证了BP网络模型的有效性。其算法可开发出程序模块，集成到相关脑电图仪的软件系统中。