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云安全面临的威胁和未来发展趋势 被引量:8
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作者 郑禄鑫 张健 《信息网络安全》 CSCD 北大核心 2021年第10期17-24,共8页
随着云计算技术的广泛应用与发展,云安全的重要性也日益突出,针对云环境的攻击日益频发,相关的攻击技术和方法也不断升级换代。文章首先对云安全的发展现状进行总结,然后分别分析云架构各个层次面对的安全挑战以及目前的主要应对措施,... 随着云计算技术的广泛应用与发展,云安全的重要性也日益突出,针对云环境的攻击日益频发,相关的攻击技术和方法也不断升级换代。文章首先对云安全的发展现状进行总结,然后分别分析云架构各个层次面对的安全挑战以及目前的主要应对措施,分别从特征数据获取和特征处理两个维度,对目前云安全研究成果进行总结归纳与分析,指出相关技术的发展趋势。最后展望了云安全的发展前景,并且提出一个多云平台管理架构。 展开更多
关键词 云安全 特征数据获取 特征数据处理 多云架构
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GQL:Extending XQuery to Query GML Documents 被引量:9
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作者 GUAN Jihong ZHU Fubao ZHOU Jiaogen NIU Liping 《Geo-Spatial Information Science》 2006年第2期118-126,共9页
GML is becoming the de facto standard for electronic data exchange among the applications of Web and distributed geographic information systems. However, the conventional query languages (e. g. SQL and its extended v... GML is becoming the de facto standard for electronic data exchange among the applications of Web and distributed geographic information systems. However, the conventional query languages (e. g. SQL and its extended versions) are not suitable for direct querying and updating of GML documents. Even the effective approaches working well with XML could not guarantee good results when applied to GML documents. Although XQuery is a powerful standard query language for XML, it is not proposed for querying spatial features, which constitute the most important components in GML documents. We propose GQL, a query language specification to support spatial queries over GML documents by extending XQuery. The data model, algebra, and formal semantics as well as various spatial Junctions and operations of GQL are presented in detail. 展开更多
关键词 XML GML spatial feature query language XQUERY
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基于贝叶斯网络的车辆定位冗余信息过滤方法研究
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作者 高翔 《河南科技》 2022年第23期23-27,共5页
随着中国人民生活水平的不断提高,汽车的保有量也在逐年上升,而交通事故率也随之上升。及时定位目标车辆将有助于快速处理交通事故。为解决车辆定位过程中因外界干扰导致定位效果差的问题,本研究结合形态规则和机器学习方法,提出基于贝... 随着中国人民生活水平的不断提高,汽车的保有量也在逐年上升,而交通事故率也随之上升。及时定位目标车辆将有助于快速处理交通事故。为解决车辆定位过程中因外界干扰导致定位效果差的问题,本研究结合形态规则和机器学习方法,提出基于贝叶斯网络的车辆定位信息冗余过滤方法。首先获取卫星图像,并分析车辆特性,在特征空间内寻找最近的样本对象。然后用线性分类器对车辆图像进行分类,将独立性信息融入贝叶斯网络模型中。最后,利用K-L特征压缩器来去除冗余数据。试验结果表明,图像上冗余车辆矩形框的遮盖率达98%,仅剩与试验车辆图像规则相关或相似的图像,证明该方法的过滤效果优秀,可满足现实需求。 展开更多
关键词 形态规则 机器学习 车辆定位 冗余信息过滤 数据特征处理
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Estimating Water Retention with Pedotransfer Functions Using Multi-Objective Group Method of Data Handling and ANNs 被引量:2
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作者 H.BAYAT M.R.NEYSHABOURI +1 位作者 K.MOHAMMADI N.NARIMAN-ZADEH 《Pedosphere》 SCIE CAS CSCD 2011年第1期107-114,共8页
Pedotransfer functions (PTFs) have been developed to estimate soil water retention curves (SWRC) by various techniques. In this study PTFs were developed to estimate the parameters (θs, θr, α and λ) of the B... Pedotransfer functions (PTFs) have been developed to estimate soil water retention curves (SWRC) by various techniques. In this study PTFs were developed to estimate the parameters (θs, θr, α and λ) of the Brooks and Corey model from a data set of 148 samples. Particle and aggregate size distribution fractal parameters (PSDFPs and ASDFPs, respectively) were computed from three fractal models for either particle or aggregate size distribution. The most effective model in each group was determined by sensitivity analysis. Along with the other variables, the selected fractal parameters were employed to estimate SWRC using multi-objective group method of data handling (mGMDH) and different topologies of artificial neural networks (ANNs). The architecture of ANNs for parametric PTFs was different regarding the type of ANN, output layer transfer functions and the number of hidden neurons. Each parameter was estimated using four PTFs by the hierarchical entering of input variables in the PTFs. The inclusion of PSDFPs in the list of inputs improved the accuracy and reliability of parametric PTFs with the exception of ~s- The textural fraction variables in PTF1 for the estimation of a were replaced with PSDFPs in PTF3. The use of ASDFPs as inputs significantly improved a estimates in the model. This result highlights the importance of ASDFPs in developing parametric PTFs. The mCMDH technique performed significantly better than ANNs in most PTFs. 展开更多
关键词 aggregate size distribution fraetal parameters particle size distribution
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