测量过程控制图参数的自助最大熵融合优化

Shewhart控制图是核查数据分析的主要工具,在核查数据为小样本情况下Shewhart控制图容易导致“误发警报”。针对这一问题,提出一种自助最大熵融合方法,优化Shewhart控制图控制参数。首先,通过自助法充分挖掘核查数据自身特征,扩大样本容量。在此基础上,应用最大熵原理,描述出核查数据概率分布参数的密度函数,估计核查数据样本的均值和方差,从而优化Shewhart控制图控制参数。实验表明,经自助最大熵优化后的Shewhart控制图控制参数更加接近理论值,降低了发生“误发警报”的概率。     

小样本岩土参数的Bootstrap估计及边坡稳定分析

 针对岩土参数小样本下t分布法区间估计较宽将导致工程设计偏于保守的问题,根据描述土体固有变异性的c,?总体开展小样本随机取样勘察模拟,运用Bootstrap法进行数据重构得到区间估计和点估计结果,对比与t分布法所得参数估计的差异。基于充足次数重复抽样构建的参数估计空间,分析与之映射的边坡稳定安全系数离散特征。研究表明：对应于同一置信水平,能充分利用小样本经验分布信息的Bootstrap法可有效收窄c,?均值置信区间,保证率下限值较传统t分布法结果更接近真值;虽然2种方法得到点估计结果的均值和方差相同,即对应的边坡稳定可靠指标相等,但采用Bootstrap法保证率下限值计算的边坡稳定安全系数均值相对较大,离散性更小,且这种改善效果随参数变异水平上升愈加明显;在保证相同可靠度条件下,Bootstrap法能更合理地对小样本统计不确定性导致的边坡安全风险进行补偿。     

Reliability of Space-Shuttle Pressure Vessels With Random Batch Effects

In this article we revisit the problem of estimating the joint reliability against failure by stress rupture of a group of fiber-wrapped pressure vessels used on Space-Shuttle missions. The available test data were obtained from an experiment conducted at the U.S. Department of Energy Lawrence Livermore Laboratory (LLL) in which scaled-down vessels were subjected to life testing at four accelerated levels of pressure. We estimate the reliability assuming that both the Shuttle and LLL vessels were chosen at random in a two-stage process from an infinite population with spools of fiber as the primary sampling unit. Two main objectives of this work are (1) to obtain practical estimates of reliability taking into account random spool effects and (2) to obtain a realistic assessment of estimation accuracy under the random model. Here, reliability is calculated in terms of a “system” of 22 fiber-wrapped pressure vessels, taking into account typical pressures and exposure times experienced by Shuttle vessels. Comparisons are made with previous studies. The main conclusion of this study is that, although point estimates of reliability are still in the “comfort zone,” it is advisable to plan for replacement of the pressure vessels well before the expected lifetime of 100 missions per Shuttle Orbiter. Under a random-spool model, there is simply not enough information in the LLL data to provide reasonable assurance that such replacement would not be necessary.     

基于Bootstrap 法的机载浮标搜潜系统作战效能指标分析

为解决试验样本量较少导致评估精度较低的问题,引入Bootstrap方法对机载浮标搜潜系统部分指标进行评估。介绍对于成败型指标的目标发现率评估步骤,并对一定环境下的潜艇目标发现率、浮标信号有效接收距离和某分机灵敏度这3项有代表性的指标进行仿真。分析结果说明该方法确实能够提高评估精度。     

Bootstrap Unit‐Root Tests: Comparison and Extensions

Abstract. In this article, we study and compare the properties of several bootstrap unit‐root tests recently proposed in the literature. The tests are Dickey–Fuller (DF) or Augmented DF, based either on residuals from an autoregression and the use of the block bootstrap or on first‐differenced data and the use of the stationary bootstrap or sieve bootstrap. We extend the analysis by interchanging the data transformations (differences vs. residuals), the types of bootstrap and the presence or absence of a correction for autocorrelation in the tests. We show that two sieve bootstrap tests based on residuals remain asymptotically valid. In contrast to the literature which focuses on a comparison of the bootstrap tests with an asymptotic test, we compare the bootstrap tests among themselves using response surfaces for their size and power in a simulation study. This study leads to the following conclusions: (i) augmented DF tests are always preferred to standard DF tests; (ii) the sieve bootstrap performs better than the block bootstrap; (iii) difference‐based tests appear to have slightly better size properties, but residual‐based tests appear more powerful.     

动态测量不确定度贝叶斯评定的改进方法研究

动态测量不确定度的评定一直是比较复杂的问题,到目前为止,国内外有许多学者对这一问题进行了探索与研究,给出了一些评定的方法,但这些方法都存在自身的缺点与不足,还不能给出十分精确的评定结果。因此,通过对现有基于贝叶斯理论的动态测量不确定度评定方法进行分析,用最大熵原理改进其先验分布的选择与计算方法,从而有效提高计算后验分布的精度,最终提高不确定度的计算准确度。     

非线性模型中M-估计的Bootstrap逼近

讨论了非线性模型中回归系数向量基于M-估计的Bootstrap逼近,并在一定的条件下证明了Bootstrap逼近成立。     

物流中心选址的神经网络集成模型研究

针对在建立物流中心选址模型中,单个人工神经网络模型难以确定参数、容易产生“过拟合”等问题,提出一种神经网络二次集成模型,利用Bootstrap可重复采样技术得到不同的训练集来训练产生不同的个体神经网络,采用粒子群优化算法结合个体输出获得神经网络集成,并在此基础上将集成视为个体再次结合。实验结果表明,该模型易于设计且能够提高泛化能力。     

基于Bootstrap方法的密封寿命可靠性评估

针对高可靠、长寿命密封件在小样本试验下的可靠性寿命评估需求,提出一种基于虚拟增广样本和Bootstrap方法的密封寿命小样本数据可靠性评估方法。并以轴用阶梯圈为例,进行寿命可靠性评估。首先通过虚拟增广法将轴用阶梯圈台架寿命试验数据样本数增广至10个,使得样本数满足Bootstrap方法的适用条件;然后再利用Bootstrap方法得到轴用阶梯圈的寿命可靠性评估结果。     

P-S-N曲线的Bootstrap置信带

由小子样疲劳试验数据估计出的疲劳可靠性设计P-S-N曲线可能偏于危险,目前常采用C-P-S-N(置信度-可靠度-应力-寿命)曲线提高预测结果的可信性,文中基于Bootstrap方法提出一种新的估计C-P-S-N曲线的方法.给出所提方法的具体实现,所提方法借助Bootstrap再抽样技术,可以拟合任意置信度和任意可靠度下的C-P-S-N曲线,并获得更安全可信的估计结果,有效改善常规方法出现的危险问题.采用所提方法对材料LY12CZ铝合金板材疲劳试验数据进行分析讨论,结果表明所提方法是合理可行的.