基于混沌序列加权抽样和排序变换的图像置乱

为增强网络中传输的数字图像信息的安全性,对一类典型的基于混沌排序图像置乱算法——Ye算法进行了研究,给出应用间隔抽样混沌序列置乱图像可以提高混沌排序置乱算法置乱度这一性质,并依据近似熵理论给出其提升的依据.为增加图像置乱算法的密钥空间,结合Shannon加权保密思想,设计了基于加权抽样混沌序列排序图像置乱算法.结果表明,该方法密钥空间大,置乱强度高,抗重构攻击.     

航空装备备件需求量的概率区间计算方法

科学合理地解决备件配置问题一直为人们所瞩目,备件配置数量的多少不仅影响装备的维修甚至影响装备的战备完好率。计算备件需求量的传统模型是概率模型,然而概率模型中的分布参数往往有一定程度的不确定性。本文对概率模型中含有有界不确定参数的备件需求量计算问题进行研究,根据航空装备备件的分类,分别提出了寿命服从指数分布、正态分布和威布尔分布部件的备件需求量的概率区间确定方法。所提出的方法较好地解决了分布参数有界不确定时航空装备备件的配置问题并能够充分保证备件的保障率,进而提高航空综合保障水平。最后,通过数值算例说明了该方法的有效性和实用性。     

Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting

The support vector machine (SVM) combined with K-nearest neighbors (KNN), called the SVM-KNN method, is new classing algorithm that take the advantages of the SVM and KNN. This method is applied to the forecasting models for solar flares and proton events. For the solar flare forecasting model, the sunspot area, the sunspot magnetic class, and the McIntosh class of sunspot group and 10 cm solar radio flux are chosen as inputs; for the solar proton event forecasting model, the inputs include the longitude of active regions, the flux of soft X-ray, and those for the solar flare forecasting model. Detailed tests are implemented for both of the proposed forecasting models, in which the SVM-KNN and the SVM methods are compared. The testing results demonstrate that the SVM-KNN method provide a higher forecasting accuracy in contrast to the SVM. It also gives an increased rate of ‘Low’ prediction at the same time. The ‘Low’ prediction means occurrence of solar flares or proton events with predictions of non-occurrence. This method show promise for forecasting models of solar flare and proton events.     

第2009卡林顿周太阳活动的初步分析

介绍了第 2 0 0 9卡林顿周大黑子群AR484,AR486和AR488产生的太阳活动以及这些太阳活动对地球空间环境造成的影响。依据对太阳活动的统计研究 ,简要分析了三个大黑子群活动的差异。分析结果指出 ,关注某一时期太阳活动的活动经度带以及太阳活动在某一半球的主次是解决太阳活动短期预报非常重要的思路     

Study of the Enhancement Events of Relativistic Electron at the Geosynchronous Orbit

The high flux of energetic electron on geostationary orbit can induce many kinds of malfunction of the satellite there, within which the bulk-charging is the most significant that several broadcast satellite failures were confirmed to be due to this effect. The electron flux on geostationary orbit varies in a large range even up to three orders accompanied the passage of interplanetary magnetic cloud and the following geomagnetic disturbances. Upon the investigation of electron flux enhancement events, two types of events were partitioned as recurrent events and random ones. Both of the two kinds of events relate to the interplanetary conditions such as solar wind parameters, IMF etc and their evolution characters as well. As for the recurrent events, we found that, (1) all of the events exhibits periodic recurrence about 27 days, (2) significant increase of electron flux relates to interplanetary index and characters of their distribution, (3) the electron flux also has relation to solar activity index. An artificial neural network was constructed to estimate the flux I day ahead. The random electron flux enhancement events are rare and present different distribution figures to the recurrent ones. The figure of the random events and the conditions of their occurrence is also discussed in this paper.     

基于神经网络的磁平静期赤道电集流预测

利用BP神经网络技术分别对2008年后磁平静期印度扇区、秘鲁扇区以及CHAMP卫星的赤道电集流（EEJ）变化进行预测,其中神经网络训练数据为对应的2000—2007年磁平静期EEJ观测数据,输入参量为天数、地方时、太阳天顶角、太阳活动指数（F_10.7）、太阴时以及卫星地理经度,输出参量为EEJ.对EEJ预测结果进行了统计学分析,并且与实际观测结果进行对比.结果表明：BP神经网络对事件中EEJ的变化具有很好的预测能力,预测结果能够反映EEJ的重要分布特征;EEJ预测值与观测值之间具有很好的相关性,其中地磁台站观测值与预测值相关性系数可达85%以上.此外,将BP神经网络模型的预测结果与Yamazaki提出的经验模型结果进行对比,结果显示BP神经网络与其经验模型性能相当.研究结果表明,BP神经网络技术在平静期EEJ变化预测方面性能优异,具有良好的应用前景.     

A tip clearance prediction model for multistage rotors and stators in aero-engines

Tip clearances of multistage rotors and stators greatly affect aero-engines’ aerodynamic efficiency, stability and safety. The inevitable machining and assembly errors, as well as the complicated error propagation mechanism, cause overproof or non-uniform tip clearances. However, it is generally accepted that tip clearances are difficult to predict, even under assembly state. In this paper, a tip clearance prediction model is proposed based on measured error data. Some 3D error propagation sub-models, regarding rotors, supports and casings, are built and combined. The complex error coupling relationship is uncovered using mathematical methods. Rotor and stator tip clearances are predicted and analyzed in different phase angles. The maximum, minimum and average tip clearances can be calculated. The proposed model is implemented by a computer program, and a case study illustrates its performance and verifies its feasibility. The results can be referred by engineers in assembly quality judgement and decision-making.     

基于模糊规划的航空公司混合营销渠道协调

为了对航空公司混合渠道进行更有效协调,根据渠道协调特点,运用区间分析法构建了需求不确定情况下航空公司混合渠道协调的模糊规划模型,并对模型进行了多维度数值模拟.结果表明,模型对不确定需求具有鲁棒性,适用于需求不确定下航空公司混合渠道协调;随着分销渠道比例下降,渠道协调鲁棒性呈现先升后降趋势;随着需求区间变大,模糊规划模型的隶属度呈现下降趋势.因此航空公司应适当降低分销渠道比例,同时加强市场调查缩小机票需求不确定区间范围;代理人应选择代理销售需求不确定性低的机票产品,并与航空公司合作适当降低分销渠道比例从而获得鲁棒双赢策略.     

基于动态时间阈值的报警聚合方法研究

针对网络入侵检测中持续性攻击引发的多个报警事件时间间隔变化的问题,引入时间间隔变异系数描述报警的时间波动特征;通过将报警数据属性分为时间约束属性和相似度约束属性,提出了一种利用动态时间阈值约束的相似报警数据聚合方法。实验结果表明,这种方法能有效减少持续性攻击触发的冗余报警。