靶场测控通信系统可靠性分析

首先确定了测控通信系统可靠性分析框架，然后针对系统构型为串并联系统、试验信息为成败型数据／寿命型数据的情况，研究了可靠性信息融合的Bayes方法，最后讨论了系统可靠性的Bayes统计推断问题，包括点估计、置信估计和假设检验。     

正态单边可靠性经典精确下限的新程序

本文给出了正态分布单边可靠性经典精确下限的一种新的计算方法及程序。该程序比国标GB4885-85提供的程序能大量节省机时。     

Improved predictions of equatorial TEC for real time applications

It is important to use models developed specifically for the equatorial ionospheric estimation for real-time applications, particularly in Satellite Navigation. This work demonstrates a methodology for improved predictions of VTEC in real time using the model developed for the equatorial ionosphere by the authors. This work has been done using TEC data of the low solar activity period of 2005 obtained using dual frequency GPS receivers installed under the GAGAN project of ISRO. For the purpose, the model is first used in conjunction with Kriging technique. Improvement in accuracy is observed when compared with the estimations from the model alone using the measurements as true reference. Further improvement is obtained by Bayesian combination of these estimates with independent Neural Network based predictions. Statistical performance of improvement is provided. An improvement of ∼1 m in confidence level of estimation of VTEC is obtained.     

Categorical Bias in Line Angle Judgments: Sex Differences and the Use of Multiple Categories

According to the Category Adjustment (CA) model, spatial estimates (e.g., of location) involve Bayesian combination across multiple, hierarchical pieces information, each weighted by its relative certainty. Recent work, though, has shown that men and women differ in terms of their certainty regarding fine-grained and categorical information in location memory. Here we demonstrate that this reflects a more general sex difference in visuospatial processing by examining bias patterns in a line angle judgment task (JLAP-15). In addition, the data suggest that multiple, hierarchical levels of categorical information influenced spatial judgments. These results imply that the hierarchical combination outlined by the CA model extends beyond two levels, and that men and women apply differential weighting to these representations.     

基于云贝叶斯网络的运输飞机超轮速风险评估

起飞超轮速严重威胁飞行安全,为了有效评估运输飞机起飞时在不确定性因素影响下的超轮速风险水平,提出一种基于云模型和贝叶斯网络的评估模型。选取抬轮速度、总重、低压转子转速、抬轮率、抬轮时机、升降舵控制量、风的分量、总温8个指标,建立超轮速风险指标体系;运用基于启发式高斯云变换算法和正向高斯云算法的云模型,实现超轮速风险等级...     

Bayesian network-based extraction of lunar impact craters from optical images and DEM data

Impact craters are among the most noticeable geomorphological features on the planetary surface and yield significant information about terrain evolution and the history of the solar system. Thus, the recognition of impact craters is an important branch of modern planetary studies. Aiming at addressing problems associated with the insufficient and inaccurate detection of lunar impact craters, a decision fusion method within the Bayesian network (BN) framework is developed in this paper to handle multi-source information from both optical images and associated digital elevation model (DEM) data. First, we implement the edge-based method for efficiently searching crater candidates which are the image patches that can potentially contain impact craters. Secondly, the multi-source representations of an impact crater derived from both optical images and DEM data are proposed and constructed to quantitatively describe the two-dimensional (2D) and three-dimensional (3D) morphology, consisting of Histogram of Oriented Gradient (HOG), Histogram of Multi-scale Slope (HMS) and Histogram of Multi-scale Aspect (HMA). Finally, a BN-based framework integrates the multi-source representations of impact craters, which can provide reductant and complementary information, for distinguishing craters from non-craters. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on three lunar scenes using both orthoimages from the Lunar Reconnaissance Orbiter (LRO) and DEM data acquired by the Lunar Orbiter Laser Altimeter (LOLA). Experimental results demonstrate that integrating optical images with DEM data significantly decreases the number of false positives compared with using optical images alone, with F₁-score of 84.8% on average. Moreover, compared with other existing fusion methods, our proposed method was quite advantageous especially for the detection of small-scale craters with diameters less than 1000 m.     

贝叶斯景象匹配技术

景象匹配技术以其极高的末制导精度在导航系统中起着关键性的作用。经典的景象匹配定位方法只能提供象素级的定位精度,为了获得更高的精度可采用ＦＦＴ匹配算法、最小二乘匹配算法等。该文首次提出了一种新的贝叶斯景象匹配算法。其基本思想在于：匹配定位位置可看作为随机量,先利用匹配图像间的信息求其条件概率分布,再利用图像中的大量样本来求解其贝叶斯估计值。同时研究了基于贝叶斯估计的景象匹配技术的原理及算法,并将其与     

一种基于贝叶斯估计的高分辨目标定向新方法

提出一种高分辨方位估计新方法。它是建立在阵列输出信号和噪声参数联合后验概率密度基础上的空间谱估计。仿真结果表明,该方法具有以下几个显著的优点：①极高的空间分辨率和估计精度;②可解相干源;③低信噪比、少快拍数的情况下很有潜力;④可以估计不同方向目标源的能量;⑤具有较好的工程应用前景。该方法和ＭＵＳＩＣ方法的性能进行了比较并给出计算机仿真结果。     

通用航空机队设备可靠性动态识别模型

通用航空机队设备可靠性是通航单位安全运行的前提条件,是影响运营单位经济效益的重要因素之一。根据通航单位机队设备可靠性数据统计、分析及实际使用情况,采用ATA100章节名称作为指标源。综合可变模糊识别方法和权重阶梯朴素贝叶斯分类器模型的优势,构建了通用航空机队设备可靠性动态识别模型。为了避免主观给定指标权重导致的不合理,应用熵权法客观获取指标权重。最后利用实例测试样本验证了权重阶梯朴素贝叶斯分类器的合理性,并基于该方法对待识别样本进行了可靠性状态识别。实例分析表明：基于权重阶梯朴素贝叶斯分类器的通用航空机队设备可靠性状态识别模型具有较强的可行性和合理性,为通用航空机队设备可靠性状态提供了一种科学的识别方法。     

基于连续蚁群算法的Bayesian方位估计快速方法

针对蚁群算法(ACO)在解决高维非线性搜索问题方面的有效性,提出了基于蚁群优化算法的Bayesian最大后验概率方位估计(ACO-Bayesian)快速方法.该方法将Bayesian最大后验概率函数作为蚁群算法的目标函数,选取若干一维高斯函数的加权和作为连续蚁群算法中信息量概率分布函数,经过有限次迭代得到Bayesian方法的非线性全局最优解.仿真结果表明,ACO-Bayesian方法在保持Bayesian方法优良性能的同时,将Bayesian方法的计算量减少到原来的1/14.水池实验结果验证了ACO-Bayesian方法的正确性和有效性,为其工程应用奠定了基础.