C/C + +源代码风格检测工具的实现

文中给出了评价C／C 源代码风格的规则分类和检查工具的总体结构，阐述了代码检查工具的核心技术——扩展词法分析和扩展语法分析，给出了代码检测工具的应用实例。     

一种固定阈值的毫米波图像边缘检测方法

文章提出了一种固定阈值的毫米波图像边缘检测方法。根据图像的统计特征确定出像素的标准梯度强度，并采用单阈值对图像进行边缘粗定位，然后施行非最大抑制处理和滤波，得到最终检测结果。由于该算法对各幅毫米波图像设置相同的阈值，故能实现自动检测，同时还具有计算量小的特点。实验结果表明该方法进行毫米波图像边缘检测的有效性和可行性。     

Manufacturing and structural safety evaluation of a composite train carbody

This paper explains the manufacturing process of a composite train carbody with a sandwich composite structure for bodyshell and a stainless steel structure for the under frame. In addition, the structural behavior and safety of the composite carbody of the Korean tilting train was investigated by the static load tests. From the test results, the stiffness of the composite carbody met the specified design. In the aspect of strength, the maximum stress of the composite bodyshell was of 12.2% of strength of CF1263 carbon/epoxy.     

Perceptually Grounded Language Learning: Part 2—DETE: A Neural/Procedural Model

In Part 1 of this two-part series, we introduced Katamic memory—a neural network architecture capable of robust sequence learning and recognition. In Part 2, we introduce the Blobs World taskjdomain for language learning and describe the DETE language learning system, which is composed of over 50 Katamic memory modules. DETE currently learns small subsets of English and Spanish via association with perceptual! motor inputs. In addition to Kaiamic memory, DETE employs several other novel features: (1) use of feature planes, to encode visual shapes, spatial relationships and the motions of objects, (2) phase-locking of neural firing, in order to represent focus of atention and to bind objects across multiple feature planes, and (3) a method for encoding temporal relationships, so that DETE can learn utterances involving the immediate past and future. We compare DETE to related models and discuss the implications of this approach for language-learning research.     

模糊综合评判方法在碳酸盐岩油气勘探中的应用

详细地阐述了模糊综合评判方法的数学原理及其在油气勘探开发中的应用.根据某油田的生产层参数资料,运用MICROSOFT EXCEL软件,在对各参数进行统计研究并了解它们分布规律的基础上,合理地构造出一系列隶属函数,通过编制相应的计算机程序,完成综合评判.评判结果与勘探实际吻合较好.该方法有效地适用于碳酸盐岩油气藏的勘探开发.     

Choosing OWA operator weights in the field of Social Choice

One of the most important issues in the theory of OWA operators is the determination of associated weights. This matter is essential in order to use the best-suited OWA operator in each aggregation process. Given that some aggregation processes can be seen as extensions of majority rules to the field of gradual preferences, it is possible to determine the OWA operator weights by taking into account the class of majority rule that we want to obtain when individuals do not grade their pairwise preferences. However, a difficulty with this approach is that the same majority rule can be obtained through a wide variety of OWA operators. For this reason, a model for selecting the best-suited OWA operators is proposed in this paper.     

An efficient algorithm for mining frequent inter-transaction patterns

In this paper, we propose an efficient method for mining all frequent inter-transaction patterns. The method consists of two phases. First, we devise two data structures: a dat-list, which stores the item information used to find frequent inter-transaction patterns; and an ITP-tree, which stores the discovered frequent inter-transaction patterns. In the second phase, we apply an algorithm, called ITP-Miner (Inter-Transaction Patterns Miner), to mine all frequent inter-transaction patterns. By using the ITP-tree, the algorithm requires only one database scan and can localize joining, pruning, and support counting to a small number of dat-lists. The experiment results show that the ITP-Miner algorithm outperforms the FITI (First Intra Then Inter) algorithm by one order of magnitude.     

电梯交通系统(Ⅰ)──电梯交通配置现状及发展

本文对国内外电梯交通配置设计现状及发展趋势作了论述。指出了由统计特性描述进入到采用专家系统和模糊规则的动态特性描述这一总的发展趋势。     

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。