A Bayesian approach to diagnosing covariance matrix shifts

In addition to the quick detection of abnormal changes in a multivariate process, it is also critical to provide an accurate fault identification of responsible components following an out-of-control signal. In line with the work of Tan and Shi for diagnosing shifts in the mean vector, this paper develops a Bayesian approach for diagnosing shifts in the covariance matrix. The simulation comparisons favor the proposed approach. A real example is also presented to demonstrate the implementation of the proposed method.     

Variable stage-independent double sampling plan with screening for acceptance quality loss limit inspection scheme

The quality loss suggested by Taguchi has been recognised as a new quality evaluation based on variable property instead of the quality evaluation based on traditional attribute property such as the proportion of nonconforming items. Some variable sampling inspection plans in order to assure the quality have been considered. As one of those sampling inspection plans, the single sampling plan with screening (SSPS) has been constructed for acceptance quality loss limit inspection scheme. Further, the repetitive group sampling plan with screening (RGSPS) has been developed for reducing the average total inspection (ATI). Although ATI by RGSPS has successfully been reduced in comparison with ATI by SSPS, RGSPS may sometimes increase the average sampling frequency (ASF) in return for reducing ATI. In this study, the stage-independent double sampling plan with screening (SIDSPS) based on the concept of the acceptance quality loss limit inspection scheme is proposed under the consideration of the trade-off between ATI and ASF. Through some numerical comparisons about ATI and ASF in SSPS, RGSPS, and SIDSPS, the usefulness of SIDSPS proposed in this study is confirmed.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors, such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators. These random factors cannot be reduced by merging multiple estimators, and existing algorithms cannot remove the deviation caused by such factors. In this paper, we regard the estimation deviation as a random variable and proposed a sampling method, recorded as the linear estimator array step sampling algorithm (L2S), to reduce the influence of the random deviation. L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation. The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm, which takes a lot of time when processing high-speed network data in a serial environment. To solve this problem, a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit (GPU). Experiments on real-world highspeed network traffic show that L2S can reduce the absolute bias by more than 22% on average, and the extra time is less than 61 milliseconds on average.     

质量和成本约束下CSP-T和Spk集成过程控制方案

多水平连续抽样检验方案(CSP-T)是在线过程质量控制工具，但该方案的第一类风险和第二类风险都较高，且不能满足成本约束。提出了CSP-T和过程良率指数(S_pk)集成过程控制方案，该集成方案在满足质量约束的同时，以最小成本运行，并将两类风险控制在既定水平。建立了极限检验能力下的最优CSP-T方案，依据数据的计数特征驱动质量控制方案运行。基于S_pk估计的精确分布建立了风险控制方案，依据数据的计量特征驱动风险控制方案运行。质量控制方案和风险控制方案是独立互补关系。相比于CSP-T方案，检验工作量没有增加。企业案例验证了集成控制方案的可行性和有效性。     

Distribution-free double-sampling precedence monitoring scheme to detect unknown shifts in the location parameter

In most applications, parametric monitoring schemes are used to monitor the majority of industrial and nonindustrial processes in order to improve the quality of the outputs or services. However, parametric monitoring schemes are known to underperform when the normality assumption is not met or when there is not enough information about the symmetry or asymmetry nature of the process underlying distribution. Hence, in this paper, a new nonparametric Phase II Shewhart-type double-sampling (DS) monitoring scheme based on the precedence statistic is proposed in order to efficiently monitor quality processes when the underlying process distribution departs from normality. The performance is investigated using the average run length (ARL), standard deviation of the run length (SDRL), expected ARL (EARL) and expected average number of observations to signal (EANOS), and the average sample sizes (ASS) metrics. The latter metrics are computed using Monte Carlo simulation and exact formulae. In general, it is shown that the new DS precedence scheme outperforms the existing basic Shewhart precedence scheme with and without supplementary runs rules in many situations. A real-life illustrative example based on a filling process of milk bottles is provided to demonstrate the application and implementation of the new DS precedence monitoring scheme.     

Flexible time reduction method for burn-in of high-quality products

Burn-in is an effective method to screen out early failures of electronic devices. Typically, this is achieved by operating the devices under accelerated stress conditions. This paper focuses on a burn-in concept where a random sample of devices is drawn out of the running production, put to burn-in, and investigated for early failures. This procedure is called burn-in study. In parallel, as long as the burn-in study is ongoing, all other produced devices are subjected to burn-in screening. In this article, new flexible sampling plans for burn-in studies are introduced. These are based on the progress of these studies and defined quality targets. Furthermore, these sampling plans enable fast burn-in time reductions and various time reduction strategies. From a statistical point of view, this requires to combine the proportion of early failures in a population with their lifetime distribution function. The new model is illustrated by case studies and simulations. It contributes to burn-in cost reductions, while controlling quality levels at the same time.     

空间曲线的特征识别与高质量非均匀采样

为避免传统均匀采样方法因忽视曲线重要特征而生成不理想的采样结果,获得给定数量且由特征点和辅助点组成的采样点序列,提出基于特征识别的高质量空间曲线非均匀采样方法.首先使用抛物线插值法得到曲线上所有曲率极大值点和挠率极大值点的近似位置,经筛选后产生特征点,以更好地抓住空间曲线的轮廓特征.然后定义基于弧长、曲率和挠率加权组合的特征函数,并以此自适应地选取曲线上的辅助点.与3种主流采样方法比较的实验结果表明,该方法能够获得更高质量的采样结果且具有更好的实用性,从而进一步改善空间曲线的B样条拟合效果.     

浅谈煤炭筛分浮沉试验中存在的问题及改进措施

煤炭筛分浮沉试验是选煤厂建设前期及运行管理中必不可少的重要环节,但在实际操作中还存在着许多问题,由此而严重影响了试验结果的准确性和可靠性。文章探讨了在煤炭采样、制样和浮沉试验过程中存在的问题,并提出了相应的改进措施。     

水动式自动取样机的研制及在选矿厂中的应用

介绍了具有自主知识产权 ,获得国家专利 (专利号ZL 0 12 6970 8.7)的水动式自动取样机基本结构和工作原理 ,以及在选矿厂中的应用情况。该取样机能在恶劣的粉尘和潮湿环境中长期稳定地工作 ,它具有全封闭、远程控制、结构简单、取样准确、维修方便、成本低廉等优点。推广应用情况表明 ,凡采用了该取样机的选矿厂均能获得较好的经济效益