Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.     

A weighted multivariate Fuzzy C-Means method in interval-valued scientific production data

Clustering is the process of organizing objects into groups whose members are similar in some way. Most of the clustering methods involve numeric data only. However, this representation may not be adequate to model complex information which may be: histogram, distributions, intervals. To deal with these types of data, Symbolic Data Analysis (SDA) was developed. In multivariate data analysis, it is common some variables be more or less relevant than others and less relevant variables can mask the cluster structure. This work proposes a clustering method based on fuzzy approach that produces weighted multivariate memberships for interval-valued data. These memberships can change at each iteration of the algorithm and they are different from one variable to another and from one cluster to another. Furthermore, there is a different relevance weight associated to each variable that may also be different from one cluster to another. The advantage of this method is that it is robust to ambiguous cluster membership assignment since weights represent how important the different variables are to the clusters. Experiments are performed with synthetic data sets to compare the performance of the proposed method against other methods already established by the clustering literature. Also, an application with interval-valued scientific production data is presented in this work. Clustering quality results have shown that the proposed method offers higher accuracy when variables have different variabilities.     

基于以太网络的数据传输系统设计研究

随着电子通信技术的发展,分布式测控系统也应运而生,并且规模在逐渐的扩大。在大的发展趋势前提下,监测数据在远距离传输的时候,如何保证其高效、安全、可靠地传输,已经成为计算机产业热议的话题。嵌入式以太网的实现为数据传输系统的设计创造了有利条件。     

浅谈计算机网络与数据通信的发展

计算机网络与数据通信,给人们的生产生活提供了极多的便利和帮助;已成为信息技术和电信技术的主要发展方向之一。本文简单回顾了计算机网络与数据通信发展的历史,着重分析了其现状,并展望了未来的发展趋势。     

浅谈计算机硬盘数据丢失原因及恢复

本文主要对计算机硬盘数据丢失的原因、恢复软件的应用方法进行分析。     

基于四层的数据交换体系结构的实现与性能分析

随着信息数据处理的要求不断提高,目前以三层体系结构为主的信息系统逐渐不能满足其需要,新增了数据预处理层地四层体系结构是未来发展的趋势。本文在概述四层系统结构理论的基础上,具体阐述了顸处理层的具体实现,并对其性能进行分析。     

异常数据挖掘在实际中的应用

本文主要讨论了异常数据挖掘在实际中的应用,简要地介绍了异常点的定义、分类及异常数据挖掘的定义、功能等,详细的介绍了异常数据挖掘技术在金融领域、电信领域、商业领域中和生物医学以及DNA分析中的应用。     

谈在.NET平台中DataList控件嵌套的应用

介绍在.NET平台下利用DataList控件的嵌套和绑定数据技术,处理主从关系数据和多列数据等多级关联数据的显示方法和技巧,为网站开发和教学提供一定实用技术支持和帮助。     

Retrieving ocean surface current by 4-D variational assimilation of sea surface temperature images

In this article we propose a new method to estimate ocean mesoscale structures of the surface current velocity by processing sea surface satellite images. Assuming that the intensity level can be described by a transport-diffusion equation, the proposed approach is based on variational assimilation of image observations within a simple transport-diffusion model. This approach permits to retrieve the current velocity field from a sequence of satellite images. Results of processing synthetic data and real NOAA-AVHRR satellite images are presented and commented.     

Modeling energy dependent nuclear reaction probabilities from pointwise measurements including full covariance information

For the design of large and highly complex facilities – like, the international project ITER (http://www.iter.org/) – reliable basic data with well established covariance information (uncertainties, correlations) are indispensable. Procedures to attain the requirements by means of a Bayesian generalized least squares method and recent approaches using theoretical nuclear reaction codes in combination with Monte Carlo techniques will be discussed and results compared. Particular emphasis is given to the procedures of constructing covariance matrices for the experimental data sets as this determines the quality of the result.