浅谈施工阶段的工程造价管理

就施工单位如何把工程造价的发生控制在合理的范围和核定的造价限额以内，从工程承包实行招投标制、缩短工期、重视现场签证和索赔工作等方面进行了阐述，以达到节约投资，提高经济效益目的。     

浅论建设项目投资控制的重点

建设项目控制的重点应放在投资决策和设计阶段中,否则会造成巨大的损失。这不仅需要人们改革组织结构和管理体制,更需要从根本上转变思想观念。     

Huffman algebras for independent random variables

Based on a rearrangement inequality by Hardy, Littlewood, and Polya, we define two-operator algebras for independent random variables. These algebras are called Huffman algebras since the Huffman algorithm on these algebras produces an optimal binary tree that minimizes the weighted lengths of leaves. Many examples of such algebras are given. For the case with random weights of the leaves, we prove the optimality of the tree constructed by the power-of-2 rule, i.e., the Huffman algorithm assuming identical weights, when the weights of the leaves are independent and identically distributed.     

An integer concave minimization approach for the minimum concave cost capacitated flow problem on networks

Formulating the minimum concave cost capacitated network flow problem as an integer concave minimization problem, we establish finite branch and bound algorithms, in which the branching operation is the so–called integral rectangular partition and the bounding procedure is performed by the classical minimum linear cost flow problem on subnetworks. For the special case that the flow cost function is concave on a fixed number of arcs and linear on the others, an upper bound of the running time is given. Received: 19 July 1996 / Accepted: 8 July 1997     

软件开发成本估算模型的研究

软件成本准确地估算和管理控制是软件项目开发成功的基础。该文分析了影响软件开发成本估算准确性的因素,并详细介绍了IBM模型、Putnam模型和COCOMO模型三种经验估算模型,探讨了软件开发成本估算的新需求。     

基于网络图像的阿奇姆博多拼图

拼图是使用不同形式的素材进行组装的合成艺术.文中介绍了一种崭新的拼图创作方法,通过挑选主题相关的网络图像的前景抠图来表现一幅输入图像,能够生成奇异的具有阿奇姆博多风格的拼图.鉴于网络图像数据的海量规模,总能够找到合适的前景抠图来匹配输入图像的各个分割部分;通过将挑选的前景抠图按照一定的规则进行组装,使其在整体上可以从形状和颜色表现输入图像,而在局部每个前景抠图仍然可以辨识.实验结果和用户体验表明,采用文中方法可以有效地生成具有娱乐性的阿奇姆博多拼图.     

沙特房建项目水电安装设计优化与成本控制探讨

以沙特阿美吉赞应急响应中心工程建设为例,阐述项目图纸会审阶段的设计优化处理、施工工艺、材料比选、安装方法等方面的成本控制措施,从而为今后类似工程施工提供参考和借鉴。     

Group technology based adaptive cell formation using predator-prey genetic algorithm

In recent years, there has been a considerable growth of application of group technology in cellular manufacturing. This has led to investigation of the primary cell formation problem (CFP), both in classical and soft-computing domain. Compared to more well-known and analytical techniques like mathematical programming which have been used rigorously to solve CFPs, heuristic approaches have yet gained the same level of acceptance. In the last decade we have seen some fruitful attempts to use evolutionary techniques like genetic algorithm (GA) and Ant Colony Optimization to find solutions of the CFP. The primary aim of this study is to investigate the applicability of a fine grain variant of the predator-prey GA (PPGA) in CFPs. The algorithm has been adapted to emphasize local selection strategy and to maintain a reasonable balance between prey and predator population, while avoiding premature convergence. The results show that the algorithm is competitive in identifying machine-part clusters from the initial CFP matrix with significantly less number of iterations. The algorithm scaled efficiently for large size problems with competitive performance. Optimal cluster identification is then followed by removal of the bottleneck elements to give a final solution with minimum inter-cluster transition cost. The results give considerable impetus to study similar NP-complete combinatorial problems using fine-grain GAs in future.     

一种新的修正恒模算法

阐述了恒模算法(CMA)、修正恒模算法(MCMA)和判决引导(DD)算法的基本原理。针对CMA和MCMA收敛速度慢,固定步长条件下收敛速度和剩余误差之间存在矛盾的缺陷,在分析CMA误差特性的基础上,利用DD算法误差函数的模值和判决器输出构造新的误差函数,提出了一种新的变步长MCMA。新算法在加快收敛速度的同时保持小的剩余误差。仿真结果表明,新算法比CMA和MCMA收敛速度快,均衡输出剩余码间干扰(ISI)小而且能克服相位偏移,具有很好的实用性。