反舰导弹靶场试验精度评定方法

为了对小子样条件下反舰导弹靶场试验精度作出合理的评定,研究了自助法和随机加权法的原理。针对自助法和随机加权法存在的不足,研究了一种改进的方法,仿真结果表明新方法获得了更加稳健、精确的评定。最后,给出一些有益的工程应用建议。     

小子样条件下航天轴承性能可靠性建模与评估

航天油润滑轴承可靠性具有小子样、无失效数据特点,即使采用性能可靠性方法进行建模与分析,仍存在性能数据不足问题。提出小子样条件下航天轴承性能可靠性分析的多层贝叶斯模型及模型求解方法。通过失效分析,建立轴承性能退化过程模型。利用自助法、试验数据、物理模型和专家经验等确定Bayes验前分布,并采用MCMC方法解决Bayes计算问题。实例分析表明,该方法具有较好的适用性,是解决小子样条件下长寿命产品可靠性建模与评估问题的一种有效途径。     

用BMM算法进行机动目标跟踪

本文提出了用自助法多模型——the bootstrap multiple model(BMM)算法进行评估混合(连续-离散)系统的自助法随机仿真方法。并用BMM滤波来进行机动目标跟踪,跟踪效果由计算机仿真进行验征。     

射击精度评估新技术

以某远程武器射击精度鉴定问题为对象,研究了运用Bayes理论从仿真试验数据中提取有用信息,从而实现在少量试验样本情况下有效完成射击精度试验鉴定任务的方法.尝试在准确描述远程武器射击精度的动力学仿真模型的基础上,将均匀设计法应用于射击精度的仿真,介绍了运用自助(bootstrap)法从仿真试验结果中获取验前信息的基本思想和一般方法,以及在获得验前信息后结合现场试验样本进行射击精度鉴定的方法.     

基于Bayes方法的射击密集度评估

以某远程武器射击密集度鉴定问题为对象,研究了运用Bayes 理论从仿真试验数据中提取有用信息,从而实现在少量试验样本情况下有效完成射击密集度试验鉴定任务的方法.将最大熵法应用于射击密集度估计,介绍了运用自助(Bootstrap)法从仿真试验结果中获取验前信息的基本思想和一般方法,以及在获得验前信息后结合现场试验样本进行射击密集度鉴定的方法.     

基于改进灰关联的雷达辐射源识别方法研究

针对灰关联法的局限性,提出了一种改进的灰关联分析法.该方法有效解决了灰关联法中的分辨系数、绝对差和权重的问题,提高了灰关联法的准确性.在此基础上,提出一种基于载频和改进灰关联法的新的雷达辐射源识别模型.经过仿真验证后,改进的灰关联法较传统的灰关联法更有效,建立的识别模型也能对雷达辐射源进行很好识别.     

对边坡稳定分析极限平衡法中简化Bishop法的分析与改进

根据简化Bishop法的基本原理,提出了这种方法既满足整体力矩平衡外还满足整体力平衡的观点,并与严格条分法比较,得出其差异并进行了改进。改进的简化Bishop法保留了计算简单的优点,并使每一条块也保持力与力矩的平衡,从而使其计算方法与计算结果更趋合理,而且安全系数较简化Bishop法稍增大,因此更经济。     

改进的AHP法在目标价值排序中的应用

针对在应用AHP法时,传统的标度法可能导致判断矩阵一致性与思维一致性脱节,进而破坏层次分析法的优选排序功能的问题。首先分析了传统的1～9标度的不足,然后通过引入指数(0,2)标度,建立了满足一致性要求的判断矩阵,改进了层次分析法。最后将改进的AHP法应用于战场炮兵的主要攻击目标价值排序的评估,进一步提高了评估的准确性。     

K-近邻法的文本分类算法分析与改进

文本自动分类的相关技术是数据挖掘的一个分枝,K-近邻法是常用的文本分类算法之一.在研究K-近邻法的基础上,根据其使用情况分析了K-近邻法的不足之处,在满足一定的判定函数前提下,提出改进后的K-近邻法.改进后的K-近邻法避免了K值的搜寻过程.降低了计算复杂性,提高计算效率.最后通过实验证明了改进K-近邻法的有效性.     

解析与数值结合的方法及采用子结构法的改进

本文以常微分方程求解器为基础,采用康脱洛维奇法和线法,分别以矩形截面杆的扭转问题和含裂纹的Motz问题为例,对解析与数值结合的方法,进行了初步的研究.特别是在将子结构法引入后,数值结果得到明显的改进.     

小子样下命中概率的估计

研究了当试验次数较少时 ,命中圆域的概率估计。给出了自助估计法和融合估计方法 ,对于估计的精度进行了分析。方法易于实现 ,适用于工程应用     

基于Fiducial推断的Bayes验前分布的表示

先验分布的确定与表示是Bayes统计推断的出发点和关键点。提出了一种基于信仰推断 (FiducialInfer ence)观点确定Bayes先验分布的设想 ,有助于解决无验前信息或验前历史信息较少时先验分布的确定问题 ,文中给出了一个实例说明了该方法的应用     

Layers of Experiments with Adaptive Combined Design

In the field of nanofabrication, engineers often face unique challenges in resource‐limited experimental budgets, the sensitive nature of process behavior with respect to controllable variables, and highly demanding tolerance requirements. To effectively overcome these challenges, this article proposes a methodology for a sequential design of experiments through batches of experimental runs, aptly named Layers of Experiments with Adaptive Combined Design (LoE/ACD). In higher layers, where process behavior is less understood, experimental regions cover more design space and data points are more spread out. In lower layers, experimental regions are more focused to improve understanding of process sensitivities in a local, data‐rich environment. The experimental design is a combination of a space‐filling and an optimal design with a tuning parameter that is dependent on the amount of information accumulated over the various layers. The proposed LoE/ACD method is applied to optimize a carbon dioxide (epet‐CO2) assisted deposition process for fabricating silver nanoparticles with pressure and temperature variables. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 127–142, 2015     

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。     

正态双边可靠度的Bootstrap下限

研究了正态单元双边可靠度Bootstrap置信下限,并在小样本条件下对Bootstrap估计进行了纠偏处理,以提高区间估计的精度。进行了仿真检验,并与目前经典的使用双边容许限系数表的近似法相比较,证明该方法具有较高的精度,便于程序实现。     

A nonparametric approach to accelerated life testing under multiple stresses

Accelerated life testing (ALT) is concerned with subjecting items to a series of stresses at several levels higher than those experienced under normal conditions so as to obtain the lifetime distribution of items under normal levels. A parametric approach to this problem requires two assumptions. First, the lifetime of an item is assumed to have the same distribution under all stress levels, that is, a change of stress level does not change the shape of the life distribution but changes only its scale. Second, a functional relationship is assumed between the parameters of the life distribution and the accelerating stresses. A nonparametric approach, on the other hand, assumes a functional relationship between the life distribution functions at the accelerated and nonaccelerated stress levels without making any assumptions on the forms of the distribution functions. In this paper, we treat the problem nonparametrically. In particular, we extend the methods of Shaked, Zimmer, and Ball [7] and Strelec and Viertl [8] and develop a nonparametric estimation procedure for a version of the generalized Arrhenius model with two stress variables assuming a linear acceleration function. We obtain consistent estimates as well as confidence intervals of the parameters of the life distribution under normal stress level and compare our nonparametric method with parametric methods assuming exponential, Weibull and lognormal life distributions using both real life and simulated data. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 629–644, 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.