快中子零功率反应堆(DF-VI)的临界事故概率分析

本文应用故障树分析方法,对快中子零功率堆(DF-Ⅵ)在启动过程中发生临界事故的可能性做了初步的定量估计。结果表明,该堆每次启动发生临界事故的概率约为10~(-6)量级,得出确保装置启动安全的首要因素是选配合格的操作人员的结论,并对设备的改进提出了一些建议.     

斜井提升钢丝绳磨损“失效树”分析

介绍了引起斜井提升钢丝绳失效的各种因素 ,并建立“失效树” ,为煤矿生产中判断其失效原因及预防提供可靠依据。     

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.     

以数字搜索树表示的二值图象的几何变换

本文介绍以数字搜索树表示的二值图象的一种几何变换算法，文中讨论了四分区与正四边区的基本关系，输出ＤＳＴ结点成立的充分条件，给出了完整的几何变换算法并对算法的复杂性作了分析。     

具有多重焦点的波带透镜的设计

本文提出了一种新型的具有多重焦点的波带透镜的概念及其设计方法。可提供某些特定的光场分布,而且具有性能稳定、微型化和可实际制作等特点。     

基于C#语言的图片插入方法比较

图片是传递与表达信息的重要形式,由于图片数据特殊编码方式,它的存储和显示显得比较复杂。C#功能强大,使用方便,已经成为一种主流的计算机软件编程语言。文章介绍了使用C#向数据库中插入图片的方法,并加以比较。     

基于Sql Server BI平台和决策树技术的CET-4成绩分析

文章首先对Sql Server 2008商业智能平台及决策树技术相关理论作了介绍,然后对挖掘数据源数据进行了一系列预处理。利用Sql Server 2008商业智能平台下的决策树技术并采用数据挖掘扩展语言即DMX语言创建了CET-4成绩分析决策树模型。分类矩阵和挖掘提升图分别对该模型的评估结果表明了模型具有较高的可靠性和分类准确度,同时模型对应的一些规则可作为英语教学管理和改革的重要参考依据。     

基于加权决策树的蛋白质序列分类算法研究

针对蛋白质序列分类的需求,深入研究了蛋白质序列分类算法。对蛋白质序列的特征属性进行了大量的分析和研究,给出了蛋白质序列特征属性的描述形式。在此基础上设计了一种基于加权决策树的蛋白质序列分类算法,详细阐述了加权决策树的构造过程以及决策树的主要参数计算方法,而且根据蛋白质序列的特征,对决策树进行了改进,给出了加权决策树的实现方法。测试结果表明：设计的蛋白质序列分类算法具有较高的分类精度和较快的执行速度。     

基于动态贝叶斯网络的汉语方言辨识

方言的差异性在语音层面上反映在时间序列结构的不同。传统的语音建模方法只能建立稳定的时间序列结构,而方言语音是典型的动态时变时间序列结构。为了更好地提取方言时间序列结构,文中采用动态贝叶斯网路（DBN）进行建模分析,并对DBN的构建方法进行了研究,这种结构与常用于语音识别中的隐马尔可夫模型的不同之处在于它揭示多个时间片内的节点之间的影响。文中探索了不同结构和参数对识别效果的影响。文中的研究表明动态贝叶斯网络对汉语方言的识别比传统方法要好,识别率达到了98．9％。     

一种基于几何分布的新支持向量机多分类方法

二叉树支持向量机是多分类问题的一种有效方法,然而分类的效果与二叉树的结构密切相关。获得更好的分类效果和更高的效率,要使得二叉树高度尽量小而两个子类尽量易分。距离通常用来衡量两个类的分离程度,但不能反映类的分布情况。考虑到多分类中类的分布,文中定义新的分离度和相似度来衡量两个类的分离度,并且提出了一中新的基于几何分布二叉树支持向量机多分类算法,该方法使得二叉树高度尽量小而两个子类尽量易分。实验表明该方法具有较高的分类准确率和效率。