数据仓库多源实化视图一致性维护的方法研究

研究了多源单视图下数据仓库实化视图联机维护与查询一致性问题,并对现有算法做了改进.改进后的算法在更改信息中增加时间戳控制数据源端查询的时序,并引入动作列表控制数据仓库端信息提交的顺序,采用补偿思想和应答机制来协调数据源与数据仓库间的数据更新,从而保证了实化视图维护和查询的一致性.     

基于随机森林的京津冀地区PM2.5遥感反演及变化分析

大气细颗粒物PM_2.5是影响人类生存环境和身体健康的主要大气环境污染物,研究PM_2.5质量浓度季节变化的规律及空间分布特征,对于大气污染物的预防和治理有着重要的意义。利用2018～2020年MODIS卫星L2级AOD产品、MERRA-2气象数据以及地面站点PM_2.5实测数据,基于改进的随机森林算法,构建AOD-PM_2.5反演模型,对京津冀地区PM_2.5质量浓度进行估算,并分析PM_2.5质量浓度空间分布特征以及季节变化规律。结果表明：(1)春夏秋冬4组模型决定系数(R2)均值分别为0.78、0.66、0.83、0.83,模拟精度较高。(2)2018～2020年京津冀地区春夏秋冬四季PM_2.5浓度呈显著的空间分布特征及季节变化规律。其中PM_2.5污染最大值出现在冬季,最小值出现在夏季。(3)历年同季节相比,京津冀地区PM_2.5污染范围和浓度数值均有所减小,2020春季和秋季PM_2.5<...     

基于更新补偿的实化视图增量过滤研究

针对WHIPS实化视图维护模型不能检测无效增量问题,提出了增量过滤机制.该机制利用视图定义中的约束条件,并通过更新补偿机制,对基表增量进行有效性检测和过滤,减少了网络通信并避免了数据仓库端的无效的视图增量计算.文章最后通过实验对比,证明了这种机制的有效性.     

无回路网络中的最小费用流算法

针对无回路网络的特殊性,利用广探法的思想,提出了无回路网络最短路的有效算法,并在此基础之上提出了最小费用流的有效算法.其算法的复杂性分别为o(m)和o(mvo),相比拓扑排序法和最小费用路算法,本文提出的算法更为简练、易懂且复杂性低.     

Identification of Mechanism Stiffness of Autoeliminating Clearance for Auxiliary Bearing

In this article, a method for identification of equivalent stiffness was proposed based on a genetic algorithm by studying the stiffness characteristics of autoeliminating clearance auxiliary bearing devices (ACABDs) in static condition. Subsequently, the equivalent stiffness damping model and the Hertz contact theory were combined to establish the theoretical equations of the ACABD. Moreover, the linear equivalent model of the ACABD was established to eliminate the influence of contact of the revolute pair on mode shape and mode frequency of the rotor. In addition, simulations and experiments were carried out to verify the effectiveness of the genetic algorithm–based stiffness identification method. The results indicated that the relative errors between the equivalent stiffness in the X and Y directions identified by the linear equivalent model and the theoretical values were 6.22 and 7.19% respectively, demonstrating the feasibility of this identification method.     

多重BP神经网络分流结构在控制系统故障诊断中的应用

提了多重ＢＰ神经网络分流结构，以减少ＢＰ算法的学习时间，提高故障模式识别率。把这种方法应用于控制系统的故障诊断，实验结果证明，该方法可行，收到满意效果。     

Object-oriented representations, causal reasoning and expert systems

Abstract: In this article we describe how two popular AI representation techniques—frames and production systems— can be usefully combined under a general AI object-oriented approach to problems which arise in the domain of hardware fault diagnosis. One of the main advantages of such a combination is that causal reasoning— crucial to the domain under consideration — can also be naturally and effectively represented. In order to put the combination of frames and production systems on a sound methodological footing, we first provide a knowledge engineering methodology which is an object-oriented re-interpretation of ontological analysis.     

采场风流模拟实验台研制

本文从采场风流流动及瓦斯运移数学模型导出模型实验的相似准则,指导实验台设计。采用新型的实验材料和独特的采空区充填技术加工制做的多功能、组合式采场风流模拟实验台,具有设计结构合理、密封性能良好、重量轻、采空区充填带调整简单易行、风流流动及瓦斯涌出状态易控制、模型倾角任意可调等特点。预备实验证明,用该实验台研究采场风流流动及瓦斯运移规律是可行的。     

FMSLPP: fake-message based sink location privacy preservation for WSNs against global eavesdroppers

Traditional encryption and authentication methods are not effective in preserving sink’s location privacy from a global adversary that could be monitoring the network’s traffic and time-of-arrival of traffic flows. In this paper, we present a novel method named fake-message based sink location privacy preservation (FMSLPP) to protect sink’s location privacy against global eavesdroppers. In this method, we let sensors generate fake messages with a probability before the sink sends a message, in order to confuse an adversary about the sink’s location. We also make each node have approximately the same traffic volume to protect the sink’s location privacy. Simulation results from two approaches of sensor deployment (random deployment or controlled deployment) indicate that FMSLPP makes it very difficult for the global adversary to identify the location of the sink; at the same time, transmission of fake messages does not impact significantly the network’s performance in terms of network life.