采用弹道修正技术的红外干扰弹性能优化

针对弹道修正弹的高维非线性特性导致的性能优化难题,改变概念设计阶段传统的串行设计方式,提出了一种基于实验设计(Design Of Experiments,DOE)和响应面(Response Surface Methodology,RSM)的智能优化算法,定义基本的弹丸结构模型以及相关的设计参数.在DOE的基础上,将设计...     

多目标环境下基于分布式融合思想的误差估计方法

针对多雷达多目标跟踪过程中分布未知的系统误差估计问题,提出了基于"分布式融合思想"的误差估计方法。给出相应误差估计方法的计算公式,利用改进截断奇异值方法来减轻矩阵病态性的影响,提高误差估计的稳健性。设置了两种不同的系统误差仿真场景,对"分布式"误差估计方法在两种情形下的估计性能进行了仔细对比分析。结合"分布式"误差估计方法与"集中式估计"方法所体现出的优缺点,提出了一种将两种方法结合起来的系统误差估计算法,算法通过合理选择阈值门限η,能够在多雷达多目标且系统误差分布未知的复杂环境下对两种误差估计算法自适应地进行切换,从而充分发挥两种误差估计算法各自的优点,给出更好的误差估计结果。     

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

融合神经网络与超像素的候选区域优化算法

为解决目标检测中候选区域召回率低的问题,提出融合神经网络与超像素的目标候选区域算法。该算法利用神经网络提取更能清楚表达目标边界的特征,并使用聚类、相似性等策略,计算每个滑动窗口所含有的边缘信息量;将待测图像使用简单线性迭代聚类算法分割成若干个超像素,并利用超像素的空间位置、完整性、相邻超像素间的对比度信息,计算各个超像素的显著性得分及每个滑动窗口的显著性得分;根据每个滑动窗口的边缘信息及显著性得分筛选滑动窗口。在PASCAL VOC 2007测试集上进行对比实验,其实验结果表明:所述算法能够快速产生定位质量高的候选区域。     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.     

坦克稳定器的神经滑模控制方法

针对坦克稳定器这一类非线性和不确定性的复杂控制对象,提出一种神经滑模控制方法。该方法将滑模控制与神经网络相结合,解决了控制系统跟踪性能和鲁棒性能之间的矛盾。系统中的滑模控制器保证了系统的快速跟踪性能;而神经网络具有很强的自学习功能,通过学习能够保证系统的稳定性,同时可对扰动和参数变化进行有效的抑制补偿,从而在不牺牲系统鲁棒性的同时达到削弱抖振的目的。从理论上证明了滑动平面的稳定性,并且通过仿真验证了该结果。仿真结果表明该设计方法优于经典设计,为实际设计提供了一种可行的新方法。     

基于随机Petri网的维修质量管理组织结构建模与分析

简述了利用随机Petri网进行维修质量管理组织结构建模的方法。根据实际需求,建立了2种组织结构形式的仿真模型。以模型为基础,对维修质量管理组织结构的静态结构和动态行为进行了研究,解决了维修质量管理组织结构的优选问题,对组织结构设计具有一定的指导作用。     

一种基于神经网络的变换域数字水印提取算法

从理论上分析了变换域数字水印嵌入和提取过程,研究了图像处理和攻击对水印产生破坏的机理,并在此基础上提出了一种基于神经网络的变换域数字水印提取算法。实验证明,该算法对各种常见攻击具有较强的鲁棒性。     

武器系统效能评估数学模型描述

在武器系统效能评估中,武器系统的战技指标是评估武器系统效能的重要因素.对武器系统的可靠性和维修性进行了研究,并应用于可用性矩阵计算中.利用随机过程理论,建立了武器系统可信性矩阵和武器系统效能能力指数模型,最后建立了武器系统效能评估模型.     

基于随机Petri网的综合航电系统建模及分析

提出了一种利用随机Petri网对综合航电系统进行建模和性能分析的方法.首先,建立了综合航电系统数据总线传输系统的随机Petri网系统模型,通过系统模型进行综合航电系统的性能分析;其次,给出了航空电子综合系统数据总线传输基于固定主控端周期指令的总线性能分析结果;最后,通过两种分析结果的比较,得出了基于随机Petri网对航空电子综合系统数据总线传输建模分析是可行的结论.