云环境下基于信誉度的评估模型的研究

随着互联网的迅猛发展,云计算成为目前一种较为热门的商业计算模式,其强大的计算能力广泛应用在各个领域,但同时云计算在使用过程中也存在缺乏信息隐私安全性以及数据可靠性等一系列问题。为了解决以上问题,本文在云计算资源管理中加入信誉度这一概念,提出一种新的模型—信誉度评估模型,仿真结果表明基于信誉度的评估模型能提高云计算环境下资源管理的效率,提高云服务提供商和消费者的信誉度。     

纯电动汽车的研究

在能源危机日益凸显的当代,研究新能源汽车是当今全世界的重大课题。电动汽车是未来汽车的发展趋势。本文针对新能源汽车中的纯电动汽车,分析了其特点,阐述了其分类、结构与工作原理。     

机器学习方法在储层分类中的应用

油气田开发中有效储层和非有效储层的样本点存在混合带时,两类储层的划分是一个难点问题.从统计学上来看,其本质是一个含噪声的小样本二分类问题,可以采用机器学习方法,充分挖掘有试油成果的样本点的数据信息.分别利用线性判别分析、支持向量机、多层感知机神经网络建立储层分类模型,利用10次10折交叉验证法进行模型评估与优选,并利用全部样本点建立了有效的储层分类模型,最后将模型推广应用到样本分布的三种不同情形.结果表明,线性支持向量机模型具有最好的分类效果和很强的泛化能力,对于区分有效储层和非有效储层是有效的,可以在油气田开发中进行推广.     

A User Range Accuracy Estimation Method of the Global Navigation Satellite System

An Analytic estimation method （AEM） which has less execution time is proposed to calculate the User range accuracy （URA） used by the user to detect the integrity potential risk. It shows that the most important thing of computing URA is to find the maximum error vector in the error vector space. By using the covariance matrix of the error vector, the searching of the maximum error vector is converted to an analytic geometry problem. The value of the maximum error vector can be obtained di- rectly by mathematical derivation. Experiments are made to compare the performance between the proposed AEM algorithm and the Exhaustive grid search （EGS） method. The related results show that the AEM algorithm can re- duce more than 92% of execution time. This algorithm is more suitable for computing URA at the Master control station （MCS） for the engineering applications.     

Short Texts Classification Through Reference Document Expansion

With the rapid development of information technology, short texts arising from socialized human inter- action are gradually predominant in network information streams. Accelerating demands are requiring the industry to provide more effective classification of the brief texts. However, faced with short text documents, each of which contains only a few words, traditional document classifi- cation models run into difficulty. Aggressive documents expansion works remarkably well for many cases but suf- fers from the assumption of independent, identically dis- tributed observations. We formalize a view of classification using Bayesian decision theory, treat each short text as ob- servations from a probabilistic model, called a statistical language model, and encode classification preferences with a loss function defined by the language models and the ex- ternal reference document. According to Vapnik＇s meth- ods of Structural risk minimization （SRM）, the optimal classification action is the one that minimizes the struc- tural risk, which provides a result that allows one to trade off errors on the training sample against improved gener- alization performance. We conduct experiments by using several corpora of microblog-like data, and analyze the ex- perimental results. With respect to established baselines, results of these experiments show that applying our pro- posed document expansion method produces better chance to achieve the improved classification performance.     

一种嵌入式防火墙安全保护创新机制

传统的防火墙通常是在内部网络与外部网络之间实现保护机制,通过对输入和输出网络的数据包进行实时监测,一旦发现安全威胁便会立刻做出防护响应,具有数据处理时间短、效率高等特点,能够满足大多数操作程序的应用,但是也存在成本高、实现难等弊端。文中针对以上问题,提出了一套基于AMR处理器的嵌入式防火墙安全保护创新机制,给出了嵌入式防火墙的总体框架及软硬件架构设计,选用了性能良好的S3C2410X嵌入式芯片,对嵌入式防火墙的网卡驱动、应用程序的关键技术进行详细研究,对基于AMR处理器的嵌入式防火墙的通信性能进行了对比测试,其通信速率可达15.5Mpbs左右,比一般的处理器通信性能提高2.15倍。     

浅析供用电合同（电费电价）的纠纷处理

供用电合同既是供用电管理的基础资料,又是处理供用电纠纷最重要的依据。在供用电合同纠纷中,电费电价纠纷居于纠纷首位。文中阐述了电价产生纠纷的原因,提出了客观可行的处理方法,以保护供用电双方合法权益为目的,促使供电企业健康发展。     

面向云计算中虚拟化技术的等级保护要求研究

虚拟化技术是云计算的关键核心技术,在支撑云计算体系的同时,其本身的安全性也是人们关注的焦点。文中对虚拟化技术进行简单的介绍,分析虚拟化技术给信息系统带来的新的威胁和风险,在信息系统等级保护的框架下针对云计算中的虚拟化系统提出新的控制点和增强要求,为云计算系统的等级保护做出有益的探索。     

基于内容智能聚合服务的元数据处理技术应用研究

引言 近年来,随着社会信息量爆炸式增长,用户面临多种传播渠道中的多重业务选择。     

A New Anonymity Model for Privacy-Preserving Data Publishing

Privacy-preserving data publishing （PPDP） is one of the hot issues in the field of the network security. The existing PPDP technique cannot deal with generality attacks, which explicitly contain the sensitivity attack and the similarity attack. This paper proposes a novel model, （w,γ, k）-anonymity, to avoid generality attacks on both cases of numeric and categorical attributes. We show that the optimal （w, γ, k）-anonymity problem is NP-hard and conduct the Top-down Local recoding （TDL） algorithm to implement the model. Our experiments validate the improvement of our model with real data.