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.     

CXFTV2: A Fortran subroutine for the discrete least squares convex approximation

A Fortan subroutine calculates the least squares approximation to n data values containing random errors subject to non-negative second divided differences (convexity). The method employs a dual active set quadratic programming technique that allows several concavities of an iterate to be corrected simultaneously, which is a distinctive feature of this calculation. A B-spline representation of the iterates reduces each active set calculation to an unconstrained minimization with fewer variables that requires only O(n) computer operations. Details in these techniques including the data structure that establishes the implementation of the method are specified. Numerical testing on a variety of data sets indicates that the subroutine is particularly efficient, terminating after a small number of active set changes, the subroutine being suitable for large numbers of data. A numerical example and its output is provided to help the use of the software.     

基于VME总线的高速A／D缓冲系统的研制

本文介绍一种基于ＶＭＥ总线的高速Ａ／Ｄ缓冲系统的原理和结构。着重阐述了高速数据采集技术，高速ＦＩＦＯ缓冲技术，存储器总线切换技术，ＶＭＥ及ＰＣ／ＩＳＡ总线接口技术在本系统中的成功应用。     

Speed-up fractal image compression with a fuzzy classifier

This paper presents a fractal image compression scheme incorporated with a fuzzy classifier that is optimized by a genetic algorithm. The fractal image compression scheme requires to find matching range blocks to domain blocks from all the possible division of an image into subblocks. With suitable classification of the subblocks by a fuzzy classifier we can reduce the search time for this matching process so as to speedup the encoding process in the scheme. Implementation results show that by introducing three image classes and using fuzzy classifier optimized by a genetic algorithm the encoding process can be speedup by about 40% of an unclassified encoding system.     

一种彩色C型显示及数据处理终端

本文介绍了一种利用数字信号处理器ＴＭＳ３２０Ｃ２５用作雷达数据实时处理，利用图形处理器ＴＭＳ３４０１０用作雷达视频图象处理，完成雷达视频图象彩色显示的雷达终端。     

An object design framework for structural engineering

Object-oriented principles have introduced several useful concepts for developing complex software systems. As a result, several methodologies have been suggested for the overall design of software systems based on these concepts. Methodologies and frameworks for designing objects that are to be part of the software systems are currently lacking. This paper proposes anobject design framework andmethodology, which utilizes the object-oriented concepts, for planning, organizing and designing structural engineering design objects. Design objects in an integrated structural engineering system are complex and often related to each other in various different ways. The paper also identifies several important relationships among structural engineering design objects. These relationships serve as communication channels through wich design objects send messages to and receive responses from each other. Several examples, drawn from reinforced concrete structures, will be presented to demonstrate the object design methodology and to illustrate how the framework is effective in reducing the complexity of design objects in an integrated structural engineering system.     

基于神经-模糊推理系统的机械设计数据处理

针对机械设计领域中所存在的大计算量、多图表问题,应用一种神经-模糊推理系统进行映射辩识,以提高计算速度和编程处理能力,消除知识的"组合爆炸"现象。通过对具体数据、图表的实际应用表明,应用这种方法能够很好地表达原图表数据关系。同时,相对于单纯应用人工神经网络方法,它的数据识别精度更高,速度更快,能够满足设计计算要求。     

袖珍计算机放射性总量测量系统

本文介绍以PC-1500(A)型袖珍计算机为主体的地面放射性总量数据采集及处理系统的方案、硬件、软件及应用。     

井-地电位成像技术研究剩余油分布

井—地电位测量是在地表向注水井中套管施加一个大电流，通过测量地表加注高矿化度的导电流体前后的电位分布，经数据处理及反演计算，求出注水各小层的电阻率分布，进而计算出各小层的剩余油饱和度分布状况，指出油藏的挖潜方向。从临17—22井区应用情况来看，与生产动态符合较好。     

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.