结构设计对混响室场性能影响的仿真研究

通过仿真计算,分别研究了尺寸比例设计对混响室场均匀性的影响和体积变化对混响室电场强度大小的影响。数据结果表明：混响室内工作区域的场均匀性随着混响室长／宽比值增大而变差,且为获得场均匀性较优的测试环境,长宽比例应〈2。同时证明了混响室存在一临界体积上限,小于临界体积上限时,随着体积的增大,室内的电场强度逐渐减小,场均匀性逐渐提高;达到临界体积上限后,室内的场均匀性开始变差,混响室逐渐丧失对电磁波的混响作用。通过仿真计算得出混响室模型临界体积上限为10^4m^3。     

基于均匀实验设计的温压战斗部爆炸冲击波毁伤仿真研究

以温压战斗部爆炸冲击波对地下目标的毁伤为背景,设计毁伤仿真模拟的基本方案,在对影响冲击波毁伤因素合理分析的基础上,利用均匀试验设计方法设计以装药密度、空气温度、大气压强以及混凝土密度为影响因素的均匀试验方案,最后通过正态分布检验图和Bootstrap法等方法对毁伤参数的统计特性进行研究。结果表明,固定条件下温压战斗部爆炸冲击波对地下目标的毁伤中,毁伤参数值具有明显的正态分布特性,对后续结合现场试验数据确定毁伤参数真值和毁伤试验鉴定提供了一定的指导和借鉴作用。     

元器件性能退化信息的系统性能可靠性评估

性能可靠性方法为解决现代工程实际中的高可靠、长寿命、小子样产品的可靠性建模分析提供了途径.目前的性能可靠性研究主要是针对元器件开展的,对系统性能可靠性进行研究时通常需要将其看作一个元器件,需要得到系统的性能退化数据,在某些情况下,得到系统的性能退化数据比较困难.实际上,由于元器件的性能退化导致系统性能的变化,因而可以利...     

匈牙利法的末段两层火力反TBM目标分配优化

介绍了匈牙利法解决Assignment Problem的基本原理,对匈牙利法解决指派问题的解题步骤和特殊情况进行了说明。在给出末段两层火力防御TBM的目标分配原则的基础上,阐述了相关概念,改进了匈牙利法运用方式,并建立了目标分配模型。最后通过实例的解算,证明了该方法应用于末段两层火力防御TBM目标分配中的可行性,为末段协同反TBM的目标分配提供了一种有效的算法。     

概率分布模型的飞机战备完好率评估与预测

飞机战备完好率是军用飞机战斗力的重要体现.在分析目前部队常用的飞机战备完好率算法缺陷的基础上,建立了基于概率分布的飞机战备完好率计算模型,该模型体现了飞机任务可靠性、维修性与战备完好性之间的关系.以某型飞机为例,通过分析飞机任务可靠度,利用飞机可靠性、维修性、保障性及作战训练任务的历史统计数据,依据概率分布模型对飞机的...     

信息熵与TOPSIS装备保障力量部署方案优化

针对装备保障力量部署方案复杂性和模糊性特点,在分析装备保障力量部署的内容、影响因素及方案优化目的基础上,建立了装备保障力量部署方案评估指标体系,采用信息熵理论来确定评估指标的权重,并提出了基于熵权TOPS IS的装备保障力量部署方案优选方法,使装备保障力量部署方案决策更加科学合理,具有一定的实用性。     

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...     

探空火箭级间段的CAD系统

本文在CADKEY绘图软件包的支持下,开发了加筋圆柱结构CAD系统—Hcadx.此系统通过菜单引导用户的操作,可用经验公式或解析法作轴压稳定分析,用罚函数法优化参数,可以在不退出Hcadx的情况下启动CADKEY,自动绘制设计草图。此草图经过适当地编辑、修改、便可成为工程中能接受的工作图纸。     

Equity in genetic newborn screening

State‐level newborn screening allows for early treatment of genetic disorders, which can substantially improve health outcomes for newborns. As the cost of genetic testing decreases, it is becoming an essential part of newborn screening. A genetic disorder can be caused by many mutation variants; therefore, an important decision is to determine which variants to search for (ie, the panel design), under a testing budget. The frequency of variants that cause a disorder and the incidence of the disorder vary by racial/ethnic group. Consequently, it is important to consider equity issues in panel design, so as to reduce disparities among different groups. We study the panel design problem using cystic fibrosis (CF) as a model disorder, considering the trade‐offs between equity and accuracy, under a limited budget. Most states use a genetic test in their CF screening protocol, but panel designs vary, and, due to cost, no state's panel includes all CF‐causing variants. We develop models that design equitable genetic testing panels, and compare them with panels that maximize sensitivity in the general population. Our case study, based on realistic CF data, highlights the value of equitable panels and provides important insight for newborn screening practices.     

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.