过程控制中计算机图形信息压缩存贮和图形滚动方法

对工业生产过程控制和管理中的计算机图形显示,提出一种压缩存贮和滚动的新方法。新方法的实施,可使图形数据占用的存贮空间减少约80％,一般地不需使用外存,从而大大提高了显示速度,解决了普通微型机存贮空间和显示速度之间的矛盾,有效地提高了普通微机的功能/价格比。所提出的方法均经工程实践检验过,效果良好。     

Introduction to Ant Colony Algorithm and Its Application in CIMS

Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food , scholars bring forward this new evolutionary algorithm. This algorithm has sev eral characteristics such as positive feedback, distributed computing and stro nger robustness. Positive feedback and distributed computing make it easier to find better solutions. Based on these characteristics...     

天然铅及其同位素全套中子核反应数据的理论计算

用球形光学模型和核反应多步过程的半经典理论，计算了入射能量在1.0keV-20MeV的中子与^204Pb，^206Pb,^207Pb,^206Pb，和^NatPb相互作用的全套核反应数据，计算结果与评价实验数据比较，符合较好。     

互补问题中Gauss-Newton方法全局收敛性的推广(英文)

研究了由Subramamian为求解互补问题提出的阻尼Gauss-Newton方法的收敛性质，在较弱的条件下，给出了一个全局收敛效果，这个结果是Subramanian PK (1993)和（1997）中相应结果的一个推广。     

用PowerBuilder实现仓库管理系统的开发

以 2 35处仓库管理系统为例 ,介绍了利用 Power Builder开发数据库的设计与实现方案 ,并对其设计过程出现的问题进行了分析和解决     

水文资料整编管理系统的设计思路及功能实现

介绍了水文资料整编管理系统的设计思路、数据结构及功能实现，采用Microsoft Visual Basic6．0为开发工具，以Microsoft Access2000为数据管理平台，包括了数据采集录入、计算整理汇总、图形绘制、报表打印，在水文资料整编中有较强的应用价值．     

利用二次贝齐尔函数的一维优化算法

本文利用贝齐尔函数良好的保型性质,将它作为拟合函数,构造了一个一维优化算法。数值实验表明,对于高度非线性的目标函数,新算法具有很强的适应性,比同类算法更有效。     

硬盘分区链表中结点信息的完全恢复算法

通过对分区链表中结点信息的研究 ,及前后结点中相关数据项比较之后 ,参照磁盘基数表中所记录的相应分区长度 ,给出了其中任一结点或整个分区链表丢失之后的修复算法     

Improved customer choice predictions using ensemble methods

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard approach for this problem. The ensemble models are found to improve upon individual decision trees and outperform logistic regression.     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.