PLC顺控程序的计算机辅助设计

本文用C／C 语言研制了一个计算机辅助设计软件，可以自动设计PLC的SFC应用程序，并针对具体机型S7—200，利用其编程软件，将程序快速下载到PLC，极大地提高了效率。     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

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。     

基于音频数字水印的空间定位技术研究

深入研究了利于音频数字水印实现空间定位的技术思想,提出了一种在数字音频中嵌入水印信息后,实现对声源的空间定位算法,详细阐述了该算法的设计思想,并分别给出了音频数字水印的嵌入过程和数字水印的提取过程。     

基于启发式规则优化的网页元素提取方法

网页信息提取方法中的启发式规则,是识别网页标签信息、利用网页节点分析结果、针对网页不同内容、完成信息提取的重要手段。本研究在对现有启发式规则进行研究分析的基础上,提出了几种优化的启发式规则,实现对网页标题、发布时间、来源以及正文内容等元素信息的精准提取。本研究进一步提出了运用编辑距离算法实现正文内容提取准确率的判定,并提出阙值优化方法,克服了正文提取中噪声节点多、内容识别不完全的缺陷,大大提高了提取的准确度。     

基于灰度共生矩阵的图像纹理特征提取分析

图像纹理作为一种重要的视觉手段,是图像中普遍存在而又难以描述的特征。目前常用的纹理特征提取的方法主要有统计方法、模型方法、信号处理方法和结构方法。灰度共生矩阵即为灰度级的空间相关矩阵,以其为基础的统计方法通过对矩阵统计量的求取较好地提取到了纹理特征,通过选取关键参数编程并进行仿真实现,分别求取了四个方向的灰度共生矩阵及其特征量来分析图像的纹理特征。     

基于DWT的水印嵌入规则性能研究

在数字水印研究中,一个重要的问题是水印的不可见性与鲁棒性之间的折中。根据实际应用,数字水印的嵌入规则主要有乘性规则、加性规则和一种区域融合嵌入规则。本文主要从数字水印的抗攻击性方面,结合不同的嵌入规则,对各种常见图像攻击进行测试,在检测不同嵌入规则性能优劣的同时,为水印方法的选择与应用,提供指导和参考,以达到最佳的使用目的。     

基于维间扩展和事务压缩的关联规则算法改进

Apriori是关联规则挖掘的经典算法,在利用该算法进行医疗数据挖掘的过程中,发现其频繁项集产生过程有一些不必要的开销,为此提出了改进算法Mypriori,利用维间扩展和事务压缩策略来提高频集发现的效率,并通过实验验证了算法的有效性.     

制定规则实现邮件管理自动化

邮件管理自动化的实现,在提高邮件传递质量的同时,还能避免不必要的垃圾邮件。