A novel linearly-weighted gradient smoothing method (LWGSM) in the simulation of fluid dynamics problem

This paper describes a novel linearly-weighted gradient smoothing method (LWGSM). The proposed method is based on irregular cells and thus can be used for problems with arbitrarily complex geometrical boundaries. Based on the analysis about the compactness and the positivity of coefficients of influence of their stencils for approximating a derivative, one favorable scheme (VIII) is selected among total eight proposed discretization schemes. This scheme VIII is successively verified and carefully examined in solving Poisson equations, subjected to changes in the number of nodes, the shapes of cells and the irregularity of triangular cells, respectively. Strong form of incompressible Navier–Stokes equations enhanced with artificial compressibility terms are tackled, in which the spatial derivatives are approximated by consistent and successive use of gradient smoothing operation over smoothing domains at various locations. All the test cases using LWGSM solver exhibits its robust, stable and accurate behaviors. The attained incompressible LWGSM solutions show good agreements with experimental and literature data. Therefore, the proposed LWGSM can be reliably used for accurate solutions to versatile fluid flow problems.     

An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.     

一种基于ARM-Linux的分布式管道泄漏监测系统

针对中国输油管道现场具体情况,设计一套基于嵌入式ARM-Linux和LabVIEW的分布式管道泄漏监测系统,包括远程测控终端(RTU)、监测PC以及工业局域网.详细论述了系统架构、硬件组成、软件流程等.通过实际开发与应用,证明此设计完全适用于输油管道特殊现场,不仅降低了成本与功耗,还提高了效率,为长距离石油管道运输系统...     

基于DPT的非线性调频信号的DOA估计

提出了一种基于DPT的宽带非线性调频信号的DOA估计算法.首先将非线性调频(NLFM)信号建模为高阶多项式相位信号(PPS)模型,然后通过高阶瞬时矩进行多项式相位变换.接收信号将变换为单个正弦信号和新的噪声.再利用ROOT-MUSIC或者ESPRIT算法对变换得到的正弦信号的波达方向进行估计.理论分析和仿真结果表明,该...     

基于Web与组件技术的企业应用系统设计模型

文章介绍了“Browse－WebServe－DBMS”体系结构的三种模式及相关的程序设计技术[1],提出了一个基于Web与组件技术的企业应用系统软件设计模型,开发了一个应用实例,并统计分析了相关数据,说明了基于Web与组件技术的软件设计方法对于提高组件重用率,缩短开发周期,提高开发效率,提高系统的可维护性等具有明显的优势。     

A new approach for the solution of singular optima in truss topology optimization with stress and local buckling constraints

The present paper investigates problems of truss topology optimization under local buckling constraints. A new approach for the solution of singular problems caused by stress and local buckling constraints is proposed. At first, a second order smooth-extended technique is used to make the disjoint feasible domains connect, then the so-called ε-relaxed method is applied to eliminate the singular optima from problem formulation. By means of this approach, the singular optimum of the original problem caused by stress and local buckling constraints can be searched approximately by employing the algorithms developed for sizing optimization problems with high accuracy. Therefore, the numerical problem resulting from stress and local buckling constraints can be solved in an elegant way. The applications of the proposed approach and its effectiveness are illustrated with several numerical examples. Received May 2, 2000     

教学软件的模糊多层次综合评价

由于影响教学软件质量的因素很多,因而对它的评价困难较大.文章提出了采用模糊多层次综合评价方法对教学软件质量进行评价,并讨论了利用层次分析法来确定各评价因素的权重.     

Foxpro与Oracle之间数据迁移的实现方法

文章根据作者在应用系统开发中的实践经验,总结了利用ODBC技术在Foxpro与Oracle之间进行数据交换的三种方法,分析每一种方法的优缺点,并以具体实例作了详细说明。     

Nonlinear blind source separation using a radial basis functionnetwork

This paper proposes a novel neural-network approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neural-network to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF network. A contrast function which consists of the mutual information and partial moments of the outputs of the separation system, is defined to separate the nonlinear mixture. The minimization of the contrast function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Two learning algorithms for the parametric RBF network are developed by using the stochastic gradient descent method and an unsupervised clustering method. By virtue of the RBF neural network, this proposed approach takes advantage of high learning convergence rate of weights in the hidden layer and output layer, natural unsupervised learning characteristics, modular structure, and universal approximation capability. Simulation results are presented to demonstrate the feasibility, robustness, and computability of the proposed method.