液体火箭发动机环境因子的Bayes估计

基于液体火箭发动机的寿命服从威布尔分布的条件,假定天地试验环境下威布尔分布的形状参数不变,运用Bayes方法对weibull分布天地实验条件下的环境因子进行分析,给出了环境因子在平方损失下的Bayes估计,并与极大似然估计法进行比较,通过仿真实例验证了该方法的有效性。     

C/C复合材料表面烧蚀细观形貌损伤分析

基于C/C复合材料烧蚀表面细观形貌和细观结构上的流场分布规律研究,着重分析了微裂纹尺寸对C/C复合材料中Z向纤维和基体界面上因变温引起的开裂损伤的影响,给出了微裂纹随温度变化的扩展规律及微裂纹特征尺寸随温度变化的表达式;提出了在高温下因变温而造成的损伤模型,为C/C复合材料结构烧蚀计算提供了一种基础性理论。研究结果表明,高温下变温会引起C/C复合材料力学性能的劣化。     

基于北斗的灾害预警信息发布应用层FEC性能分析

根据北斗卫星系统特有的信息传输特征,本文研究了在北斗灾害预警信息发布系统中采用应用层前向纠错(FEC)技术能够成功提高预警信息传输的准确性.仿真结果表明预警信息的成功传输率可到达99.99%以上.所得到的结论对于北斗灾害预警信息发布系统中的参数选择及性能优化具有指导意义.     

武器系统小样本场合下的区间估计

介绍了区间估计的一般方法 ,针对武器系统试验次数少的特点 ,讨论了小样本条件下武器系统战术技术性能指标的置信区间估计的估计方法 ,并以示例进行了说明     

基于光纤以太网的高精度分布式授时技术

White Rabbit(WR)时钟同步技术是综合了同步以太网、精密定时协议(IEEE1588v2)和数字相位测量技术而发展的分布式同步授时技术,能够实现数千米范围内多节点亚纳秒精度的时钟分发。该技术兼容标准以太网协议,不占用额外网络带宽,与数据链路直接集成,结构简单成本低。介绍了White Rabbit技术的基本技术原理及初步应用方法,并给出了各种拓扑结构下的测试结果。采用White Rabbit能很好地解决各种长距离多节点高精度授时需求,能够作为地基增强导航系统的关键支撑技术。     

噪声调幅干扰信号波达方向估计研究

利用噪声调幅干扰信号频谱结构的特殊性,提出基于阵列接收信号载频处频域数据的波达方向估计算法,通过仿真分析表明该算法的角度估计性能明显高于目前广泛采用的比幅比相单脉冲测角法,并且计算量也大大低于传统的波达方向估计算法.     

三维PIV中透视成像的视点定位与透视平面的确定

对三维PIV中透视投影的视点定位与透视平面的确定技术作了深入的研究,提出了一种确定视点坐标与透视平面的方法,给出了相应的数学关系式,最后通过实验的方法对视点坐标与透视平面的确定技术进行了检验。     

×××机翼主梁的寿命分布研究

本文通过运用Ｗ∧２统计量的检验方法对ＸＸＸ飞机八个失效的机翼主梁寿命分布进行了检验,得出该主梁寿命分布为威布尔分布。     

工程方法确定翼面载荷研究

提供一种使用翼面载荷测试的弯、扭矩确定气动载荷分布的工程方法。实例表明这种方法是一种比较简单易行的方法,可以得到工程上较为满意的结果。可供强度工作者参考使用。     

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.