A complete importance ranking for components of binary coherent systems,with extensions to multi-state systems

Means of measuring and ranking a system's components relative to their importance to the system reliability have been developed by a number of authors. This paper investigates a new ranking that is based upon minimal cuts and compares it with existing definitions. The new ranking is shown to be easily calculated from readily obtainable information and to be most useful for systems composed of highly reliable components. The paper also discusses extensions of importance measures and rankings to systems in which both the system and its components may be in any of a finite number of states. Many of the results about importance measures and rankings for binary systems are shown to extend to the more sophisticated multi-state systems. Also, the multi-state importance measures and rankings are shown to be decomposable into a number of sub-measures and rankings.     

机载激光雷达数据的三维深度学习树种分类

针对传统基于激光雷达(Light Detection And Ranging,LiDAR)数据的树种分类方法难以直接且全面地利用点云的三维结构信息的问题,提出一种基于三维深度学习的机载LiDAR数据的树种分类方法.该方法直接从三维数据中抽象出高维特征,而无须将点云转化为体素或二维图像.以塞罕坝国家森林公园内白桦和落叶松...     

Fuzzy数序的CV指标法及改进

确定Fuzzy数的序是一个重要的理论问题,有广泛的应用价值.CV指标法基于Fuzzy数概率分布的均值及方差对Fuzzy数进行定序,但在Fuzzy数概率分布的均值及方差的一些组合下存在失效问题,文中通过实例对此进行了分析,定义了综合Fuzzy数均值、方差及信息量的限位系数并给出了基于限位系数的Fuzzy数定序方法.     

舰载武器系统研制方案多目标生成与评价方法

舰载武器系统研制方案的生成、评价与优选问题是武器装备研制论证中的重要内容.在分析目前舰载武器系统研制方案论证方法的基础上,构建了舰载武器系统多目标优化模型,提出了舰载武器系统研制方案的多目标生成方法,并建立了不完全偏好信息的舰载武器系统研制方案的多目标评价与优选模型.实例分析说明,该模型与方法是有效的,可为武器装备研制论证问题提供辅助决策方法和智力支持.     

基于Legendre伪谱法的UGV避障路径规划

针对传统优化方法不易解决含有复杂动力学约束的路径规划问题,提出利用勒让德伪谱法(Legendre Pseudospectral Method,LPM)对地面无人驾驶器(unmanned ground vehicle,UGV)的避障路径规划进行研究。通过建立UGV的动力学模型和障碍物模型,整合了基于LPM的路径约束条件。最后应用以LPM为基础开发的MATLAB插件包DIDO,计算得出了优化路径。仿真实验中以UGV移动路径规划为例,设计实现了UGV的避障规划。仿真结果表明此方法具有较快的计算速度和准确性。     

基于直接算法的合成孔径雷达干涉测量几何精度分析

直接将绝对相位差引入距离方程和多普勒方程答解地面点坐标,称为合成孔径雷达干涉测量直接算法。利用引入含有合理误差的模拟InSAR像坐标、绝对相位差和相应的轨道参数作为起算数据,求得含有误差的地面点坐标,实现了InSAR直接算法的几何精度分析。为了把基线长度和姿态的误差引入轨道空间位置,提出了相应的数学模型。计算结果表明,直接算法与经典算法精度基本相同。     

不确定多属性决策的单目标优化方法研究

运用神经网络的非线性映射和遗传算法的寻优特性,建立了不确定多属性决策的单目标优化模型。把属性值区间作为遗传算法染色体的搜索范围,用训练好的神经网络计算适应度。用不同的适应度函数来计算综合属性值区间数的下界和上界,然后对方案进行排序。算例结果表明,该方法是可行的。     

星地距离变化对星地双向时间比对的影响及改正

星地时间同步是卫星导航系统的关键技术,星地双向时间比对能有效提高星地时间同步的精度。在分析了星地距离变化对星地双向时间比对可引起百纳秒量级误差后,提出了前后历元观测数据差分改正方法,通过数学仿真验证该方法能达到纳秒量级精度。     

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.     

Constructing a strict total order for alternatives characterized by multiple criteria: An extension

The problem of finding a strict total order for a finite set of multiple criteria alternatives is considered. Our research extends previous work by us, which considered finding a partial order for a finite set of alternatives. We merge the preference information extracted from the preference cones and corresponding polyhedral sets, with the information derived from pairwise comparisons of two alternatives, yielding a preference matrix. This preference matrix is used as input to an integer programming model to obtain a strict total order that provides a transitive ranking for the set of alternatives. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 155–163, 2014     

模糊群体决策的多方案评价方法

针对多方案评估中因素评价难于精确表示的问题,提出了一种基于模糊语言变量和OWA(OrderedWeightedAveraging)集结算子的群体评价模型与方法,给出各因素的方案两两对比偏好语言评价值,并通过对多决策者、子因素、主因素的三次集结和对模型中α值的恰当选取,可得到方案集的优序排列。该方法可行、有效,可为群体决策提供新的途径。     

基于跟踪精度控制的多传感器管理方法

针对地面机动目标跟踪过程中的多传感器管理问题展开了研究，设计了一种基于跟踪精度控制的多传感器多目标分配方法。首先，在考虑目标与目标之间、目标与传感器之间和传感器与传感器之间等的多种约束条件下运用基于协方差控制的思想建立了多传感器多目标分配问题的优化模型；接着将等价伪量测的异步融合算法与IMM算法结合，计算各目标在不同融合周期的跟踪精度估计值；最后，以目标的跟踪精度需求为出发点，结合蚁群算法的思想，设计了一种求解所建立的多传感器多目标分配问题的优化模型的算法。仿真结果表明：该管理方法能在确保跟踪精度需求的前提下，根据对各目标跟踪任务的重要程度，合理地调度传感器资源。     

地面站对月球探测器的导航

针对一个从地球停泊轨道出发绕地月飞行的月球探测器 ,讨论地面站对月球探测器导航的几个问题 ,包括观测方式的选取、地面站的分布、制导点的调整、导航精度等。结果表明仅依靠局域的地面站 ,采用测距的方式 ,可以对探测器进行实时导航。研究的方法和结果可供月球探测实际工程参考     

基于统计加权的单传感器数据融合算法

单传感器数据融合是十分重要的实际问题。为了提高数据处理的敏感性和精度,在研究基于关系矩阵的数据融合方法的基础上,提出了基于统计加权的数据融合方法,并对两种方法进行了对比分析。实例仿真表明,基于统计加权的数据融合方法优于基于关系矩阵的数据融合方法,该方法能减小无效测量值的范围,并且对测量值的变化更敏感。     

初始误差和制导工具误差估计的非线性方法

准确估计初始误差和制导工具误差是机动发射飞行器精度鉴定必须解决的重要问题之一,提出了一种基于非线性模型的误差估计新方法。给出了平台初始失准角向定向误差的转换方法,采用不动点迭代法实现真实视加速度的精确计算,将真实发射系的轨道参数表示为初始误差和工具误差的非线性函数,结合外测数据建立了同时估计初始误差、工具误差、外测系统误差、遥外测时间零点偏差的非线性模型,避免了初始误差的线性化近似。给出了Bayes极大后验估计方法,利用非线性模型和先验信息获得误差的最优估计,证明了估计方法的收敛性。仿真结果表明,所提方法提高了初始误差和工具误差的估计精度,并实现了测量数据的自校准。     

多约束条件下互联网骨干网路由器级拓扑生成方法

互联网骨干网是网络流量的中枢传输系统,其路由器级拓扑结构对于网络抗毁性分析具有重要意义。由于难以获取互联网骨干网路由器级的真实拓扑,通过分析骨干网的形成因素,将地理位置、节点间联系强度、基础设施费用、鲁棒性等因素结合起来,提出一种多约束条件下的互联网骨干网路由器级拓扑生成方法。该方法既可以构造难以公开获取网络测量数据的骨干网,也可以用来生成某一骨干网的多种替身拓扑集。通过现实中的互联网骨干网作为实例,验证了方法的有效性。     

Exploiting self‐canceling demand point aggregation error for some planar rectilinear median location problems

When solving location problems in practice it is quite common to aggregate demand points into centroids. Solving a location problem with aggregated demand data is computationally easier, but the aggregation process introduces error. We develop theory and algorithms for certain types of centroid aggregations for rectilinear 1‐median problems. The objective is to construct an aggregation that minimizes the maximum aggregation error. We focus on row‐column aggregations, and make use of aggregation results for 1‐median problems on the line to do aggregation for 1‐median problems in the plane. The aggregations developed for the 1‐median problem are then used to construct approximate n‐median problems. We test the theory computationally on n‐median problems (n ≥ 1) using both randomly generated, as well as real, data. Every error measure we consider can be well approximated by some power function in the number of aggregate demand points. Each such function exhibits decreasing returns to scale. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 614–637, 2003.     

Screening and selection procedures with control variates and correlation induction techniques

We consider the problem of identifying the simulated system with the best expected performance measure when the number of alternatives is finite and small (often < 500). Recently, more research efforts in the simulation community have been directed to develop ranking and selection (R&S) procedures capable of exploiting variance reduction techniques (especially the control variates). In this article, we propose new R&S procedures that can jointly use control variates and correlation induction techniques (including antithetic variates and Latin hypercube sampling). Empirical results and a realistic illustration show that the proposed procedures outperform the conventional procedures using sample means or control variates alone. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

三维点云深度模型压缩算法

随着计算机三维视觉的广泛应用,近几年基于深度学习的点云处理算法得到了大量研究,而耗时耗存储的缺陷较大程度限制了其在移动端的部署应用。基于改进损失函数的总体思路,提出了一种新的点云深度模型压缩框架,将知识蒸馏方法引入二值量化模型中,同时考虑点云聚合操作的特殊性引入了辅助损失项,改进的损失函数共包括预测损失项、蒸馏损失项和辅助损失项三部分。实验结果表明,和已有算法相比,所提算法可以获取更高的精度,同时对当前点云主流深度网络模型也具有良好的扩展性。     

高超声速尖双锥流动高精度数值模拟

以25°/55°尖双锥外形的高超声速低焓层流流动模拟为例,对高阶加权紧致非线性格式模拟激波/边界层干扰流动的能力进行验证和确认。空间离散采用二阶MUSCL和三阶、五阶加权紧致非线性格式,时间离散采用二阶精度双时间步方法,通量函数采用混合Roe-Rusanov,AUSMPW+,Van Leer等,对比了不同精度空间离散格式对时间、网格收敛特性和通量函数耗散特性的影响。数值模拟结果表明采用高精度空间离散格式能在较疏的网格上获得收敛解,并能消除结果对通量函数的敏感性,但收敛需要推进更久的计算时间。数值模拟结果与实验测量结果吻合良好,满足工程精度要求。