Rectilinear steiner tree heuristics and minimum spanning tree algorithms using geographic nearest neighbors

We study the application of the geographic nearest neighbor approach to two problems. The first problem is the construction of an approximately minimum length rectilinear Steiner tree for a set ofn points in the plane. For this problem, we introduce a variation of a subgraph of sizeO(n) used by YaO [31] for constructing minimum spanning trees. Using this subgraph, we improve the running times of the heuristics discussed by Bern [6] fromO(n ² log n) toO(n log² n). The second problem is the construction of a rectilinear minimum spanning tree for a set ofn noncrossing line segments in the plane. We present an optimalO(n logn) algorithm for this problem. The rectilinear minimum spanning tree for a set of points can thus be computed optimally without using the Voronoi diagram. This algorithm can also be extended to obtain a rectilinear minimum spanning tree for a set of nonintersecting simple polygons.The results in this paper are a part of Y. C. Yee's Ph.D. thesis done at SUNY at Albany. He was supported in part by NSF Grants IRI-8703430 and CCR-8805782. S. S. Ravi was supported in part by NSF Grants DCI-86-03318 and CCR-89-05296.     

Software for dynamic reconfigurable order picking systems

This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.     

非理想信道下Colpitts混沌电路的自适应同步研究

本文研究了Colpitts混沌电路在非理想信道条件下的自适应同步问题。引入自适应控制器对输入到响应部分（response system）的衰变混沌信号进行预处理，来补偿衰落信道对于同步性能的影响。给出了系统的数学模型，对恒定信道衰变和时变信道衰变两种非理想信道条件下的混沌ColpiRs电路的自适应同步进行了数值仿真研究。仿真结果表明，对于恒定或慢变化的时变信道衰变，自适应控制器可以有效的提高Colpitts电路响应部分和驱动部分（Drive system）的同步性能。     

A New Algorithm for the Elimination of Common Subexpressions in Hardware Implementation of Digital Filters by Using Genetic Programming

A new algorithm based on Genetic Programming (GP) for the problem of optimization of Multiple constant Multiplication (MCM) by Common Subexpression Elimination(CSE) is developed. This method is used for hardware optimization for DSP systems. A solution based on Genetic Programming is shown in this paper. The performance of the technique is demonstrated in one- and multi-dimensional digital filters with constant coefficients.     

连铸温度场数值模拟及冷却水参数优化

由于用实验方法来研究工艺参数对铸坯温度场的影响较困难,故而提出了一种在冶金冷却条件指导下的启发式GA(遗传算法)数值模拟优化方法,为优化工艺参数提供了理论依据,并在优化基础上采用MIMO(多输入多输出)系统PID(比例积分微分)控制器实现了二冷段配水的优化控制,改善了铸坯的质量.     

构造性神经网络在测井岩性识别中的应用

测井岩性识别是石油勘探中十分重要的基础工作,准确的岩性识别结果可以为勘探和开发提供可靠的依据。人工神经网络方法可对复杂的高维数据进行非线性映射,在模式识别领域得到越来越广泛的应用。针对传统的BP神经网络算法存在收敛速度慢、隐层数以及隐层节点数难以确定等缺点,研究了一种构造性神经网络学习算法---级联算法（Cascade Correlation Algorithm,简称CC算法）及其在石油工程中的应用。采用该算法对甘肃镇原-泾川地区进行岩性识别研究,通过与BP神经网络的识别结果进行比较,体现出构造性神经网络的优越性。     

《C语言程序设计》课堂教学方法的探讨

《C语言程序设计》一直是计算机专业及相关专业的专业基础课。该课程既难教又难学。传统的教学方法往往过分注重语法形式的介绍，却忽视了算法的讲解，使得学生无法解决一些实际编程，也很容易挫伤学生的学习兴趣。为此应以算法教学为主。因材施教，合理使用教材，在改用多媒体教学的同时，适当配以板书。调动学生的主观能动性，提高其编程能力。     

一种改进的共同进化遗传算法及其应用

将共同进化遗传算法应用于临床营养决策优化中,虽然取得了一定成果,但算法中仍存在早熟现象、局部搜索能量弱等问题,为此提出一种改进的共同进化模型。算法使用新的子群体划分方法和交叉算子,引入精英保留策略、信息交换原则和小生境技术。通过对糖尿病营养治疗的仿真表明,改进后的算法在保证群体多样性和避免早熟现象的前提下,提高了算法的局部和全局寻优能力,且膳食配方中营养素达标率符合要求,满足专家建议的糖尿病饮食需求。     

基于加载算法的室内无线信道OFDM接收机的性能

本文研究了无线局域网中使信号频谱得到优化的一种简单OFDM子载波加载算法。此算法基于边缘自适应（Margin Adaptive）加载准则及其在室内无线信道上的性能。