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。     

由有穷自动机构造正则表达式的新算法

本文借助图论的理论，通过识别回路和不包含回路的由起始状态到终止状态的路径的方法，提出一种构造给定有穷自动机对应的正则表达式的新算法，并给出具体实例。     

刚体极限平衡法与有限元法在工程边坡稳定性分析中的应用与比较

刚体极限平衡法、有限元法在工程边坡稳定分析中是两个常用的计算方法,文中对两种方法的理论基础和计算过程进行了简要介绍,并应用两种方法对一工程实例进行了分析。结果表明,二者的计算结果相近,且相同工况下有限元计算安全系数略小于刚体极限平衡法计算安全系数。     

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.     

Drag on two co-axial rigid spheres moving normal to a plane: Newtonian and Carreau fluids

The boundary effect and the presence of a nearby entity on the drag of a rigid entity is investigated by considering the movement of two identical, rigid, coaxial spheres normal to a plane in both a Newtonian and a Carreau fluid at a low to medium large Reynolds number. The parameters key to the phenomenon under consideration, including the nature of the fluid, the separation distance between two spheres, the distance between the near sphere and the plane, and the Reynolds number, on the drag coefficient are discussed. We show that the influence of a boundary on the drag coefficient is more important than that of the nature of a fluid and that of the separation distance between two spheres. The variation of the drag coefficient as a function of Reynolds number for a Carreau fluid is similar to that for a Newtonian fluid. Due to the shear-thinning nature of the former the drag coefficient in the former is smaller than that in the latter. The influence of the index parameter of a Carreau fluid becomes appreciable only if the Carreau number is sufficiently large. Correlations between the drag coefficient and the key parameters of a system are developed for the case when the Reynolds number is smaller than l.     

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。     

一种非限制性的手写数字的识别方法

本文从语义，句法模式识别观点，提出一种基于模型指导的有限状态属性自动机，进行特征抽取。对每一种典型的畸变模型设计一个有限状态属性文法及其相应的属性自动机，采用自下而上和自上而下相结合的控制策略，并在低层次引入知识指导，减少了工作量和不确定性。基于上述方法实现的非限制性手写数字识别系统，经过对１１００个非限制性手写数字样本的测试，平均识别率达９５．２％，拒识率为４．６％，误识率为０．２％。     

Matrix expression and reachability analysis of finite automata

In this paper,we propose a matrix-based approach for finite automata and then study the reachability conditions.Both the deterministic and nordeterministic automata are expressed in matrix forms,and th...     

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。