基于RBAC权限管理模型在OA系统中的运用

分析了传统的RBAC权限管理模型,并根据国内企业的需求和特点,在RBAC的基础上设计了更灵活的权限管理机制,很好的满足了企业的需求。     

基于规则的土壤数据校验模型研究与实现①

数据校验是数据挖掘与知识发现中的重要一环。我国土壤观测数据由于台站观测人员上网条件、观测地记录不便以及需要适当的数据预处理等原因,无法实行在线入库,一般借助于Excel等软件来记录中间结果,再提交土壤分中心,这样的记录过程经常引入不必要的错误。提出了一个基于可定制规则库的土壤数据校验模型。模型主要包括数据格式转换模块、权限管理模块、元数据管理模块、重复记录去除模块、数据校验模块及规则定制与解析模块。低侵入式的轻量级设计,使得在大大减轻数据校验人员工作量的情况下,原有的数据填报流程不需要改变。可定制规则使得模型易于扩展。     

基于ARM+FPGA的运动控制器设计与实现

本文以微控制器AT91RM9200和EP1C6Q240C8为核心,对工业CT机的运动控制器进行了设计,从硬件和软件两个方面对控制器的关键技术进行研究与设计,应用单神经元自适应PID控制算法进行仿真,并给出系统实际运行结果。     

安钢SAP预留系统的开发与应用

本文简述了安钢信息化建设概况,介绍了安钢信息化建设中开发SAP预留系统的设计流程,阐述了预留系统实现的功能.     

以项目为导向的3W1H教学法

随着信息技术时代带来的全新技术和革命,社会对计算机应用人才的质量要求是越来越高.中职教育作为计算机专业人才培养的一部分.如何提高学生的专业素质是目前广大教师亟待解决的问题,而计算机专业的设置和内容以及教育方法,应该得到足够的重视.另外提高学生计算机知识水平和实践动手能力,是职业学校计算机教育的根本任务.把计算机理论教学和实践教学有机地结合起来,笔者认为,"以项目为导向的3W1H教学法"是一个实用有效的好方法.     

典型的隐私保护技术模型研究

随着信息技术、网络技术的飞速发展,市民一通卡、物联网等新兴技术的兴起都带给了人们便捷的生活,知识发现和数据挖掘更是在一些深层次的应用中发挥着积极的作用,但与此同时产生了一个重要问题那就是信息的泄露.因此,如何在有效的在数据挖掘中保护隐私数据已经成为一个重要问题.本文结合数据挖掘技术来研究典型的隐私保护技术及模型,并对其进行分析介绍.     

数字签名技术及其在网络安全中的应用

在电子商务的虚拟世界中,能够在电子文件中识别双方交易人的真实身份,保证交易的安全性、真实性及不可抵懒性的电子技术手段,称之为电子签名.本文介绍了数字签名的概念和常用算法,分析了数字签名的技术原理,具体探讨了数字签名技术在网络安全中的应用.     

计算机及网络技术与现代继续临床医学教育

计算机网络技术是现代信息技术的核心内容,其运用于现代临床医学教育前景广阔,不但适用范围广,教学效率也相当的高。计算机多媒体是现代临床教学的重要手段,而网络技术的使用特使教学内容噩方式发生根本性变革。根据目前计算机技术已深入到医学诊断、治疗及科研领域且发展迅速的现状,提出在继续医学教育中开展计算机知识学习的问题,并针对不同年龄段卫生工作者的具体情况进行了探讨并提出了相应的对策,这对于提高卫生工作者的综合素质也有着重要的意义。     

Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms.     

An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.