Efficient multinomial selection in simulation

Consider a simulation experiment consisting of v independent vector replications across k systems, where in any given replication one system is selected as the best performer (i.e., it wins). Each system has an unknown constant probability of winning in any replication and the numbers of wins for the individual systems follow a multinomial distribution. The classical multinomial selection procedure of Bechhofer, Elmaghraby, and Morse (Procedure BEM) prescribes a minimum number of replications, denoted as v*, so that the probability of correctly selecting the true best system (PCS) meets or exceeds a prespecified probability. Assuming that larger is better, Procedure BEM selects as best the system having the largest value of the performance measure in more replications than any other system. We use these same v* replications across k systems to form (v*)^k pseudoreplications that contain one observation from each system, and develop Procedure AVC (All Vector Comparisons) to achieve a higher PCS than with Procedure BEM. For specific small-sample cases and via a large-sample approximation we show that the PCS with Procedure AVC exceeds the PCS with Procedure BEM. We also show that with Procedure AVC we achieve a given PCS with a smaller v than the v* required with Procedure BEM. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 459–482, 1998     

Pairwise model discrimination with applications in lifetime distributions and degradation processes

Reliability data obtained from life tests and degradation tests have been extensively used for purposes such as estimating product reliability and predicting warranty costs. When there is more than one candidate model, an important task is to discriminate between the models. In the literature, the model discrimination was often treated as a hypothesis test and a pairwise model discrimination procedure was carried out. Because the null distribution of the test statistic is unavailable in most cases, the large sample approximation and the bootstrap were frequently used to find the acceptance region of the test. Although these two methods are asymptotically accurate, their performance in terms of size and power is not satisfactory in small sample size. To enhance the small‐sample performance, we propose a new method to approximate the null distribution, which builds on the idea of generalized pivots. Conventionally, the generalized pivots were often used for interval estimation of a certain parameter or function of parameters in presence of nuisance parameters. In this study, we further extend the idea of generalized pivots to find the acceptance region of the model discrimination test. Through extensive simulations, we show that the proposed method performs better than the existing methods in discriminating between two lifetime distributions or two degradation models over a wide range of sample sizes. Two real examples are used to illustrate the proposed methods.     

图像恢复中的稳健交替方向乘子法

交替方向乘子法在解决线性逆问题(包括图像恢复)中取得了良好的效果,但是其效果对惩罚参数的选择非常敏感,不利于具体的应用。提出基于惩罚参数自适应选择原理的稳健交替方向乘子法,对其优化条件和收敛性进行了详细分析。实验表明,在基于Parseval紧框架的图像恢复应用中,该算法不但对惩罚参数的选择表现出良好的稳健性,而且效果优于交替方向乘子法,并优于其他目前热门的算法。     

大空间钢结构建筑消防安全评估研究

大空间钢结构建筑发生火灾时,火焰及火灾产生的高温羽流可能对钢构件构成一定威胁。采用经验公式计算和FDS模拟分析两种方法计算火灾环境中大空间建筑钢构件的温度,评估钢构件是否达到或超过其失效温度。     

Fully sequential procedures for comparing constrained systems via simulation

We consider the problem of finding the system with the best primary performance measure among a finite number of simulated systems in the presence of a stochastic constraint on a single real‐valued secondary performance measure. Solving this problem requires the identification and removal from consideration of infeasible systems (Phase I) and of systems whose primary performance measure is dominated by that of other feasible systems (Phase II). We use indifference zones in both phases and consider two approaches, namely, carrying out Phases I and II sequentially and carrying out Phases I and II simultaneously, and we provide specific example procedures of each type. We present theoretical results guaranteeing that our approaches (general and specific, sequential and simultaneous) yield the best system with at least a prespecified probability, and we provide a portion of an extensive numerical study aimed at evaluating and comparing the performance of our approaches. The experimental results show that both new procedures are useful for constrained ranking and selection, with neither procedure showing uniform superiority over the other.© 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

基于蚁群算法和禁忌搜索的部队机动路径选择

高技术条件下的现代战争对部队机动路径选择问题提出了新要求,一方面道路网的扩大使问题求解的规模大大增加,另一方面作战环境和任务的变化使得问题本身不得不从多角度进行综合评价.增加了问题的复杂性.针对问题建立了数学模型,引入信息熵理论作为目标评价准则,借助改进的蚁群算法、禁忌搜索算法相结合的启发式方法对问题进行求解,得到了令人满意的结果.仿真结果分析认为,算法是切实有效的,并且具有良好的稳定性.     

炮兵雷达阵地选择仿真系统的设计与实现*

虚拟现实技术是20世纪末发展起来的一项涉及众多学科的高新应用技术,主要针对在MultiGen Creator和Vega软件平台下开发三维仿真系统。针对目前炮兵雷达阵地选择虚拟仿真的需求,设计了炮兵雷达阵地选择仿真训练系统,介绍了仿真系统的组成及功能模块,论述了二维数字地图生成与显示、三维场景生成与显示、视景仿真的实现等关键技术,最后给出了仿真效果图。结果表明了仿真系统的可行性和实用性,极大地提高了训练效率和训练水平。     

基于案例决策理论的仿真任务共同体服务选择算法

资源分配优化是面向服务（ SOA）的大型多用途仿真系统的关键问题,而含有不确定状态或效果的服务选择是资源分配优化中的一个难点。针对以上问题,提出了构建仿真任务共同体的方法,基于案例的决策理论（ CBDT）的思想,设计了一种不确定型仿真任务共同体服务选择算法（ CBDSSA）。算法通过服务选择案例相似度的层次化运算,生成相似历史案例集,计算得到相似历史案例的方案效用值,最终评估出目标服务选择方案综合效用值并排序备选。算例验证表明,算法约束条件少,层次结构分明,运算结果直观,为仿真任务共同体服务选择问题提供了一种新的思路和实践手段,对大型多用途仿真系统资源分配优化研究具有一定的参考价值。     

分布式麦克风阵列的声源定位

声定位探测技术的无源被动特性,使其在定位技术研究领域内独树一帜,时延估计和定位算法是支持这一技术的核心。DUET时延估计算法简单,通过对接收到的信息分段帧移加窗处理,能够有效识别有用语音信息,时延估计精度较高,在高噪声的环境中非常适用;球面定位算法是基于时延估计提出的,对麦克风的排列形式不固定,适用于任意排布的麦克风阵列,并且在时延估计精度较高的情况下,能够很准确地对声源目标进行定位。     

基于改进蜂群算法的非均匀阵列综合

针对蜂群算法易陷入局部极值的缺陷,提出一种改进蜂群算法实现非均匀阵列综合。首先,通过引入概率选择机制,以概率因子控制蜂群算法对精英蜂的选择,从而改善蜂群算法的全局优化性能;然后,采用改进蜂群算法优化阵元间距降低最大峰值旁瓣电平并抑制干扰;最后,仿真实验表明该改进算法在非均匀阵列综合中的有效性和稳健性。研究成果可为非均匀阵列综合提供一种新的途径。