A nonparametric exponentially weighted moving average signed-rank chart for monitoring location

Nonparametric control charts can provide a robust alternative in practice to the data analyst when there is a lack of knowledge about the underlying distribution. A nonparametric exponentially weighted moving average (NPEWMA) control chart combines the advantages of a nonparametric control chart with the better shift detection properties of a traditional EWMA chart. A NPEWMA chart for the median of a symmetric continuous distribution was introduced by Amin and Searcy (1991) using the Wilcoxon signed-rank statistic (see Gibbons and Chakraborti, 2003). This is called the nonparametric exponentially weighted moving average Signed-Rank (NPEWMA-SR) chart. However, important questions remained unanswered regarding the practical implementation as well as the performance of this chart. In this paper we address these issues with a more in-depth study of the two-sided NPEWMA-SR chart. A Markov chain approach is used to compute the run-length distribution and the associated performance characteristics. Detailed guidelines and recommendations for selecting the chart’s design parameters for practical implementation are provided along with illustrative examples. An extensive simulation study is done on the performance of the chart including a detailed comparison with a number of existing control charts, including the traditional EWMA chart for subgroup averages and some nonparametric charts i.e. runs-rules enhanced Shewhart-type SR charts and the NPEWMA chart based on signs. Results show that the NPEWMA-SR chart performs just as well as and in some cases better than the competitors. A summary and some concluding remarks are given.     

A multivariate control chart for simultaneously monitoring process mean and variability

Recently, monitoring the process mean and variability simultaneously for multivariate processes by using a single control chart has drawn some attention. However, due to the complexity of multivariate distributions, existing methods in univariate processes cannot be readily extended to multivariate processes. In this paper, we propose a new single control chart which integrates the exponentially weighted moving average (EWMA) procedure with the generalized likelihood ratio (GLR) test for jointly monitoring both the multivariate process mean and variability. Due to the powerful properties of the GLR test and the EWMA procedure, the new chart provides quite robust and satisfactory performance in various cases, including detection of the decrease in variability and individual observation at the sampling point, which are very important cases in many practical applications but may not be well handled by existing approaches in the literature. The application of our proposed method is illustrated by a real data example in ambulatory monitoring.     

OWave control chart for monitoring the process mean

In this paper a control chart for monitoring the process mean, called OWave (Orthogonal Wavelets), is proposed. The statistic that is plotted in the proposed control chart is based on weighted wavelets coefficients, which are provided through the Discrete Wavelets Transform using Daubechies $\mathit{db}2$ wavelets family. The statistical behavior of the wavelets coefficients when the mean shifts are occurring is presented, and the distribution of wavelets coefficients in the case of normality and independence assumptions is provided. The on-line algorithm of implementing the proposed method is also provided. The detection performance is based on simulation studies, and the comparison result shows that OWave control chart performs slightly better than Fixed Sample Size and Sampling Intervals control charts ($\overline{X}$, EWMA, CUSUM) in terms of Average Run Length. In addition, illustrative examples of the new control chart are presented, and an application to Tennessee Eastman Process is also proposed.     

Average run lengths of a control chart based on the sum of squared errors

A new control chart is proposed for the purpose of controlling both process mean and variability simultaneously, which is based on the sum of squared errors within a subgroup. Theoretical average run lengths are derived and numerically solved for given process changes in mean and variability. It is compared with control charts and s² control charts in terms of the average run lengths.     

The quality control chart for monitoring multivariate autocorrelated processes

Previously, quality control and improvement researchers discussed multivariate control charts for independent processes and univariate control charts for autocorrelated processes separately. We combine the two topics and propose vector autoregressive (VAR) control charts for multivariate autocorrelated processes. In addition, we estimate AR(p) models instead of ARMA models for the systematic cause of variation. We discuss the procedures to construct the VAR chart. We examine the effects of parameter shifts and by example present procedures to show the feasibility of VAR control charts. We simulate the average run length to assess the performance of the chart.     

Control chart based on likelihood ratio for monitoring linear profiles

A control chart based on the likelihood ratio is proposed for monitoring the linear profiles. The new chart which integrates the EWMA procedure can detect shifts in either the intercept or the slope or the standard deviation, or simultaneously by a single chart which is different from other control charts in literature for linear profiles. The results by Monte Carlo simulation show that our approach has good performance across a wide range of possible shifts. We show that the new method has competitive performance relative to other methods in literature in terms of ARL, and another feature of the new chart is that it can be easily designed. The application of our proposed method is illustrated by a real data example from an optical imaging system.     

A generalized Conforming Run Length control chart for monitoring the mean of a variable

The attribute Conforming Run Length (CRL) control chart has attracted increasing research interests in Statistical Process Control (SPC). It decides the process status based on the interval or distance between two nonconforming units. This article proposes a Generalized CRL chart (namely GCRL chart) for monitoring the mean of a measurable quality characteristic x under 100% inspection. To run a GCRL chart, each unit will be classified as a passing or nonpassing unit depending on whether the sample value of x falls within or beyond a pair of lower and upper inspection limits LIL and UIL. When a nonpassing unit is detected, the GCRL chart checks the distance between the current and last nonpassing units in order to determine the process status (in control or out of control). The inspection limits LIL and UIL are determined by an optimization design. The GCRL chart not only solves a dead-corner problem suffered by the conventional CRL chart, but also considerably outperforms the latter for detecting mean shifts. The most interesting finding is that the attribute GCRL chart excels the variable X chart to a significant degree in SPC for variables. It suggests that the simple attribute chart may replace the variable chart in some SPC applications. The design of the GCRL chart has to be carried out by a computer program, but the design can be completed almost in no time in a personal computer.     

An economic design of cumulative sum charts to control non-normal process means

This paper proposes a method for the economic design of Cumulative sum (Cusum) control charts to maintain the current control of the process means, where the observations are independently, but non-normally distributed. The economic design of the Cusum charts involves the determination of the design parameters that minimize a relevant cost function. The design parameters are the sample size n, sampling interval s, the reference value jk, and the decision interval h. Approximating the non-normal probability density function of the process by an Edgeworth series, and deriving the average run lengths in Cusum control schemes by the use of a system of linear algebraic equations, an expression for the expected loss-cost function for the process is identified. An algorithm for near-optimal determination of the design parameters, by minimizing the loss-cost function, is presented, and its application is demonstrated through a numerical example. Finally, the effects of changes in various parameters and the cost coefficients on the loss-cost function are discussed.     

A sum of squares double exponentially weighted moving average chart

The sum of squares double exponentially weighted moving average (SS-DEWMA) chart is proposed to improve the performance of the single SS-EWMA chart, in the detection of initial out-of-control signals. The SS-DEWMA chart uses the sum of squares statistic and it simultaneously monitors the process mean and variance in a single chart. A simulation study is conducted to show that the optimal SS-DEWMA chart provides better zero state average run length (ARL) and standard deviation of the run length (SDRL) performances than the optimal SS-EWMA chart. In addition, as suggested by one of the reviewers, the cyclical steady state ARLs and SDRLs of the SS-DEWMA and SS-EWMA charts are compared, where it is found that the former did not perform as well as the latter. Note that to the best of the authors’ knowledge, a study on DEWMA type charts’ steady state ARL and SDRL performances has yet to be made in the literature. A situation in which the SS-DEWMA chart could be more useful than the SS-EWMA chart is explained in Sections 4 and 6.     

一种利用统计中值的加权定位算法

接收信号强度指示(RSSI)常被作为无线传感器网络定位算法的测距技术.通常获取的RSS1信号序列中同时存在随机误差和粗差,利用以往常用的算法难以消除混合误差对RSSI统计数据的影响.在分析误差特征的基础上,提出了一种基于统计中值的加权定位算法.算法在去除粗差的基础上,能在一定程度上平滑了随机误差.算法不仅提高了定位的精...     

一种基于地理位置的无线传感器网络分簇路由算法

针对大规模无线传感器网络,提出了一种基于地理位置的双基站分簇路由算法。该算法在网络覆盖区域边缘设置两个基站,按照地理位置将区域划分为若干均匀分布网格。每个网格根据节点剩余能量和到网格内其它节点平均距离远近选择簇头。通过仿真分析,证明该算法能减少网络能耗,延长网络生存时间。     

A new monitoring design for uni-variate statistical quality control charts

In this research, an iterative approach is employed to analyze and classify the states of uni-variate quality control systems. To do this, a measure (called the belief that process is in-control) is first defined and then an equation is developed to update the belief recursively by taking new observations on the quality characteristic under consideration. Finally, the upper and the lower control limits on the belief are derived such that when the updated belief falls outside the control limits an out-of-control alarm is received. In order to understand the proposed methodology and to evaluate its performance, some numerical examples are provided by means of simulation. In these examples, the in and out-of-control average run lengths (ARL) of the proposed method are compared to the corresponding ARL’s of the optimal EWMA, Shewhart EWMA, GEWMA, GLR, and CUSUM[11] methods within different scenarios of the process mean shifts. The simulation results show that the proposed methodology performs better than other charts for all of the examined shift scenarios. In addition, for an autocorrelated AR(1) process, the performance of the proposed control chart compared to the other existing residual-based control charts turns out to be promising.     

An improved attribute control chart for monitoring non-conforming proportion in high quality processes

Processes with very low rate of nonconformities are frequently observed in practice. These processes are known as “high quality processes”. Traditionally, the study of the rate of nonconformities was carried out using the conventional 3-sigma p control chart (Shewhart), constructed by the normal approximation. But this p chart suffers a serious inaccuracy in the modeling process and in control limits specification when the true rate of nonconforming items is small. This paper shows that, with simple adjustments to the control limits of the p-chart, achieving equal or even better improvement while still working on the original data scale, is feasible. In particular, an improved p chart which can provide a large improvement over the usual p chart for attributes is presented. This new chart, based on the Cornish–Fisher quantile correction, is also better than a previous simpler correction proposed in the literature. The improved p chart is compared with both, normal-based chart and modified p chart with one correction term and the benefits of including a new term of correction for monitoring high-quality processes is illustrated with real data.     

A new cluster based adaptive fuzzy switching median filter for impulse noise removal

A new clustering technique based on most allied directional neighbors is proposed to suppress low and high-density impulse noise from digital images. Most allied neighbors exhibit a vital role in estimation as well restoration of appropriate gray level value of corrupted pixels. In first phase, most allied directional neighbors, i.e., pixels directly attached to central pixel and the directional pixels (horizontal, vertical and two diagonal directions) next to attached pixels in the processing window are partitioned into two equal size clusters based on gradient values. Cluster with a minimum sum of gradient values (most similar neighbors) and the one with relatively large gradient values are passed to fuzzy inference system to infer the current pixel to be noisy-free, edge or a noisy. In second phase, a switching technique opts one of the three options depending upon fuzzy membership degrees and local information to restore the corrupted pixel value. A non-parametric approach based on local information for dynamic threshold setting using fuzzy logic makes the proposed filter computationally effective and adaptive to process a large number of images without user-defined parameters. The proposed algorithm is simple to implement and simulation results based on well know quantitative measures indicate the supremacy of the proposed filter for random-valued impulse noise as well as salt and peppers noise.     

基于CIMS 环境下质量控制图的研究

计算机辅助质量（CAQ）是计算集成制造系统（CIMS）不可分割的重要组成部分,而质量控制图是其中的关键技术。分析了传统控制图应用于CIM系统存在的缺陷和不足,提出了适于小批量生产的均值（x^-）控制图,使其在产品批量较少或生产加工的初期阶段等环境下得到很好的应用,而且随着检测数据的增加,其控制界限与传统方法得到的控制界限相一致,同样适合于大批量的生产环境。     

基于Internet浏览器的工业信息监视系统

本文通过对计算机网络在工业信息监视系统上应用的分析,将Ｉｎｔｅｒｎｅｔ技术与工业现场监视系统结合起来,从而形成一条新的基于浏览器的工业信息监视系统 。     

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.     

1种基于信息理论的新分子序列度量法

传统的方法度量序列之间的距离需要序列比对,使一些主观因素破坏数据的原始状态,导致计算结果因人而异。本文拟介绍一些基于信息理论的度量法,提出1种类似的新度量法,建立数学依据。这些度量法度量序列之间的距离不需要序列比对,没有主观因素干涉。同时,选取了20种胎生哺乳动物的线粒体全基因序列,分别使用这些度量法计算出他们的距离,再利用NEIGHBOR法构建系统树。由比较结果看来,新方法用较少时间构建的系统树完全不逊色于以往的方法。这为研究分子序列的差异性提供了1种新方法。     

基于Access数据库的辅助电极管理信息系统

利用Visual Basic 6.0程序,设计开发了基于Access 2003数据库的辅助电极管理信息系统,以减少工作量,提高管理效率,实现锻棒和辅助电极日常管理的系统化、信息化和高效化。同时,通过查询功能,实时掌握锻棒和辅助电极库存信息,可以为合理安排生产进度提供依据。