软件可靠性评估的重要抽样方法

基于统计测试的马尔可夫使用模型对软件可靠性评估提出了一种有效的估计方法.该方法利用重要抽样技术在保证可靠性估计无偏性的条件下,利用交叉熵度量操作剖面与零方差抽样分布之间的差异,通过启发式迭代过程调整各个状态之间的转移概率来修正测试剖面.从理论上证明了利用修正测试剖面测试估计的可靠性是方差为0的无偏估计.最后给出了软件可靠性估计的最优测试剖面生成的启发式迭代算法.仿真结果表明,该方法与模拟退火算法相比,能够明显降低估计的方差,在提高估计精度的同时加快统计测试速度.     

抽样分析模拟实验(图形)系统

在Windows 95／98、Visual Foxpro5．0／6．0 及Excel 97环境下实现对频数直方图、平均值()控制图、极差(R)控制图的绘制,形成初步的分析基础.可作为抽样分析模拟实验系统,并可作为对各种实际的统计系统的分析、评价及预测的工具.     

拉丁超立方体抽样遗传算法求解图的二划分问题

图的二划分问题是一个典型的NP-hard组合优化问题, 在许多领域都有重要应用. 近年来, 传统遗传算法等各种智能优化方法被引入到该问题的求解中来, 但效果不理想. 基于理想浓度模型的机理分析, 利用拉丁超立方体抽样的理论和方法, 对遗传算法中的交叉操作进行了重新设计, 并在分析图二划分问题特点的基础上, 结合局部搜索策略, 给出了一个解决图二划分问题的新的遗传算法, 称之为拉丁超立方体抽样遗传算法. 通过将该算法与简单遗传算法和佳点集遗传算法进行求解图二划分问题的仿真模拟比较, 可以看出新的算法提高了求解的质量、速度和精度.     

自相似网络的自适应系统双抽样方法研究

针对网络流量的自相似、重尾分布等特征,对传统的系统抽样进行改进,设计出一种新的抽样方法--自适应系统双抽样.该算法以传统的系统抽样为基础进行改进,充分考虑了网络流量重尾分布的特点,能正确估算Hurst参数,实现简单,参数自适应且能控制资源消耗.通过真实网络数据的实验分析表明,在链路负载估计、包到达时间间隔等方面较传统抽样方法都有明显的改进,提高了测量系统的精确性和实用性.     

基于抽样的影响力传播成本最小化算法

定义具有概率影响扩散保证的最小代价种子选择问题,验证了该问题是NP难的,且其影响函数是单调且次模的.将LT模型下的传播网络看成一个不确定图,对不确定图的可能世界进行抽样.为降低计算复杂度,提出一种对抽样图进行路径计数的算法来估计影响传播,使用VC维估计抽样图的数量.基于贪婪方法,提出一种求解该问题的算法,对该算法的误差进行分析.实验结果表明,该算法比其它方法具有更高的性能.     

基于洗牌算法的大数据抽样有效性分析

基于折叠技术的洗牌算法具有较好的数据置乱效果,可以满足大数据抽样的前提条件.为证明置乱后的数据集在经过抽样后内部规则不被破坏,通过数据挖掘的方法对抽样前后的数据进行关联规则分析.对比所得关联规则的支持度、置信度,以及事务出现的频率,发现经过折叠洗牌算法置乱后的数据在抽样前后所得到的关联规则变化相对稳定,并通过与现有算法的时间效率以及抽样总体误差作对比,进一步在理论上得出大数据抽样具有有效性,即可以通过抽样样本来推测数据整体情况.     

SNS:一种快速无偏的分层图抽样算法

抽样作为一种有效的统计分析方法,常被用于大规模图数据分析领域以提升性能。现有的图抽样算法大多存在高度节点或低度节点过度入样的问题,较大程度地影响了算法的性能。复杂网络具有无标度特性,即节点的度服从幂律分布,节点个体之间存在较大差异。在基于点选择策略的抽样方法的基础上,通过结合节点的近似度分布策略,设计并实现了高效无偏的分层图抽样算法SNS。在3个真实的图数据集上的实验结果表明,SNS算法比其他图抽样算法保留了更多的拓扑属性,且执行效率比FFS更高。SNS算法在度的无偏性、抽样结果拓扑属性近似性方面的表现均优于现有算法。     

利用快速无偏分层图抽样算法的MapReduce负载平衡方法

提出一种基于快速无偏分层图抽样的MapReduce负载平衡方法.将聚类算法融合到MapReduce连接操作中,提出MapReduce并行聚类连接算法的实现方法;根据聚类结果动态调整抽样率的无偏分层图抽样算法,从而实现连接操作目标数据的准确、平衡抽样.通过合成数据集和真实数据集下的数据处理实验,与Hash连接算法及基于NS抽样的聚类算法进行对比,验证了所提出的算法方案在不同数据倾斜程度下都具有良好的负载平衡性能,其运行效率也没有因为新采样算法的采用而受到影响.     

一种无抽样Contourlet变换的图像去噪方法

提出一种基于Contourlet变换的图像去噪方法,Contourlet变换采用无抽样形式,系数萎缩处理阈值门限与尺度相关。通过模拟产生不同方差噪声信号进行Contourlet变换,确定各尺度子带系数阈值,得到噪声方差与各尺度子带阈值对应表。对噪声污染图像进行Contourlet变换并估计噪声方差,查表得到各子带阈值,进行系数萎缩处理。实验表明提出的处理方法简单有效,去噪结果具有良好去噪视觉效果和较高峰值信噪比。     

基于结构分析的局部Gibbs抽样自动推理算法

提出一种基于结构分析的局部Gibbs抽样的贝叶斯网络推理算法(S-LGSI).S-LGSI算法基于联合树算法的概率图模型分析思想,对贝叶斯网络进行精确分解,然后根据查询结点和证据结点生成具有强相关性的局部网络模型,进而对局部网络模型进行Gibbs抽样推理.与当前基于抽样的其它近似推理算法相比,该算法降低推理的计算维数.同时,由于局部抽样模型包含了与查询结点相关的重要信息,因此该算法保证局部抽样推理的精度.算法分析和在Alarm网的实验结果表明,S-LGSI算法较显著降低时间复杂度,同时也提高推理精度.S-LGSI算法应用于上海证券交易所股票网络的推理结果与实际情况基本一致,表现出较强的实用性.     

Cumulative conformance count charts with variable sampling intervals for correlated samples

The cumulative conformance count (CCC) control chart is often employed to monitor the fraction nonconforming of high-yield processes. Traditional CCC chart is used when the items from a process are inspected one-at-a-time following the production order. In recent years, the CCC chart has been generalized to accommodate some industrial practices where items from a process are inspected sample by sample and not according to the production order. In order to increase the sensitivity of the generalized CCC (GCCC) chart to changes in fraction nonconforming, the variable sampling interval (VSI) scheme is used in this study. The output characteristic within each sample is assumed with correlation. The statistical properties of the GCCC chart with the VSI scheme are deduced using the Markov chain method. In evaluating the usefulness of the VSI feature, GCCC charts with VSI and fixed sampling interval (FSI) schemes are compared in terms of their statistical properties. The comparison results show that using the VSI scheme can improve the speed of GCCC chart in detecting changes in fraction nonconforming. Finally, according to the comparison results, a design procedure is applied to an industrial example to validate its practicability.     

Adaptive CUSUM control chart with variable sampling intervals

The standard cumulative sum chart (CUSUM) is widely used for detecting small and moderate process mean shifts, and its optimal detection ability for any pre-specified mean shift has been demonstrated by its equivalence to continuous sequential tests. In real practice, the assumption of knowing the true mean shift in prior cannot be always met. So it is desirable to design a procedure that is efficient for detecting a range of future expected but unknown mean shifts. Adaptive CUSUM control chart, which can continuously adjust itself by a one-step forecasting operator, has been proposed to detect efficiently and robustly for a range of mean shifts in the early literature. Moreover, in terms of sampling time to signal, control chart with the VSI (variable sampling intervals) feature can detect the process changes more quickly than the traditional FSI (fixed sample intervals) chart. In this paper, a new CUSUM control chart which is based on both adaptive and VSI features is discussed. Also, a two-dimensional Markov chain model is developed to evaluate its run-time performance.     

A multivariate synthetic double sampling T2 control chart

In this article, we propose a multivariate synthetic double sampling T² chart to monitor the mean vector of a multivariate process. The proposed chart combines the double sampling (DS) T² chart and the conforming run length (CRL) chart. On the whole, the proposed chart performs better than its standard counterparts, namely, the Hotelling’s T², DS T², and synthetic T² charts, in terms of the average run length (ARL) and average number of observations to sample (ANOS). The proposed chart also outperforms the multivariate exponentially weighted moving average (MEWMA) chart for moderate and large shifts but the latter is more sensitive than the former towards small shifts. For a variable sample size chart, like the synthetic DS T² chart, ANOS is a more meaningful performance measure than ARL. ANOS relates to the actual number of observations sampled but ARL merely deals with the number of sampling stages taken. Interpretation based on ARL is more complicated as either n₁ or n₁ + n₂ observations are taken in each sampling stage.     

An exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles

This paper proposes an exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles. A computer program in Fortran is available to assist in the design of the control chart and the algorithm of the Fortran program is also given. Some useful guidelines are also provided to aid users in choosing parameters for a particular application. Simulation results on the detection performance of the proposed control chart, compared with some other competing methods show that it provides quite robust and satisfactory performance in various cases, including intercept shifts, slope shifts and standard deviation shifts. A real data example from an optical imaging system is employed to illustrate the implementation and the use of the proposed control scheme.     

An exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles

This paper proposes an exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles. A computer program in Fortran is available to assist in the design of the control chart and the algorithm of the Fortran program is also given. Some useful guidelines are also provided to aid users in choosing parameters for a particular application. Simulation results on the detection performance of the proposed control chart, compared with some other competing methods show that it provides quite robust and satisfactory performance in various cases, including intercept shifts, slope shifts and standard deviation shifts. A real data example from an optical imaging system is employed to illustrate the implementation and the use of the proposed control scheme.     

A new exponentially weighted moving average sign chart using repetitive sampling

In this paper, a new nonparametric control chart based on the exponentially weighted moving average (EWMA) sign statistic is proposed using repetitive sampling. The control chart is proposed to effectively detect the process mean shift away from the target value without the distributional assumption on the quality characteristic. The proposed control chart is based on two pairs of upper and lower control limits having different control coefficients. The in-control and the out-of-control average run lengths of the proposed control chart are evaluated through the Monte Carlo simulation. The proposed control chart is shown to be more efficient than the existing EWMA sign control chart in terms of the average run length.     

Economic design of variable sampling intervals T2 control charts using genetic algorithms

Control charting is a graphical expression and operation of statistical hypothesis testing. In this paper, we develop the economic design of the variable sampling intervals (VSI) T² control chart to determine the values of the five test parameters of the chart (i.e. the sample size, the long sampling interval, the short sampling interval, the warning limit, and the control limit) such that the expected total cost, associated with the test procedure, is minimized. The genetic algorithm (GA) is employed to search for the optimal values of the five test parameters of the VSI T² chart, and an example is provided to illustrate the solution procedure. Sensitivity analysis is then carried out to investigate the effects of model parameters on the solution of the economic design.     

A quantile estimator under two-phase sampling for stratification

Recently, the estimation of a population quantile has received quite attention. Existing quantile estimators generally assume that values of an auxiliary variable are known for the entire population, and most of them are defined under simple random sampling without replacement. Assuming two-phase sampling for stratification with arbitrary sampling designs in each of the two phases, a new quantile estimator and its variance estimator are defined. The proposed estimators can be used when the population auxiliary information is not available, which is a common situation in practice. Desirable properties such as the unbiasedness are derived. Suggested estimators are compared numerically with an alternative stratification estimator and its variance estimator, and desirable results are observed. Confidence intervals based upon the proposed estimators are also defined, and they are compared via simulation studies with the confidence intervals based upon the stratification estimator. The proposed confidence intervals give desirable coverage probabilities with the smallest interval lengths.     

A control chart based on likelihood ratio test for detecting patterned mean and variance shifts

Control charts based on generalized likelihood ratio test (GLRT) are attractive from both theoretical and practical points of view. Most of the existing works in the literature focusing on the detection of the process mean and variance are almost based on the assumption that the shifts remain constant over time. The case of the patterned mean and variance changes may not be well discussed. In this research, we propose a new control chart which integrates the exponentially weighted moving average (EWMA) procedure with the GLRT statistics to monitor the process with patterned mean and variance shifts. The attractive advantage of our control chart is its reference-free property. Due to the good properties of GLRT and EWMA procedures, our simulation results show that the proposed chart provides quite effective and robust detecting ability for various types of shifts. The implementation of our proposed control chart is illustrated by a real data example from chemical process control.     

Self-tuning regulator based on dual-rate sampling

A self-tuning regulator based on dual-rate sampling is described in this note. A pseudostate analysis technique, which can also be used to analyze plants where control interval and sample interval are different, is introduced. Comparison of the results with standard and dual-rate sampling self-tuning regulators shows that the variance can be reduced with the proposed technique.