• International Journal of Quality Innovation
• /
• 제1권1호
• /
• pp.16-23
• /
• 2000

• Journal of the Korean Statistical Society
• /
• 제33권4호
• /
• pp.381-399
• /
• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.

• Communications for Statistical Applications and Methods
• /
• 제23권6호
• /
• pp.497-515
• /
• 2016

Modern processes often monitor more than one quality characteristic that are referred to as multivariate statistical process control (MSPC) procedures. The MSPC is the most rapidly developing sector of statistical process control and increases interest in the simultaneous inspection of several related quality characteristics. Most multivariate detection procedures based on a multi-normality assumptions are independent, but there are many processes that assume non-normality and correlation. Many multivariate control charts have a lack of related joint distribution. Copulas are tool to construct multivariate modelling and formalizing the dependence structure between random variables and applied in several fields. From copula literature review, there are a few copula to apply in MSPC that have multivariate control charts, and represent a successful tool to identify an out-of-control process. This paper presents various types of copulas modelling for the multivariate control chart. The performance measures of the control chart are the average run length (ARL) and the average number of observations to signal (ANOS). Furthermore, a Monte Carlo simulation is shown when the observations were from an exponential distribution.

• 한국컴퓨터정보학회논문지
• /
• 제25권12호
• /
• pp.187-194
• /
• 2020

오늘날 반도체나 석유 화학 공정과 같이 프로세스 중심의 산업에서는 관측된 자료들 사이에 자기 상관이 존재한다. 자기상관이 존재하는 공정에 대한 관리 방법으로는 관측치를 이용하여 뱃치 평균이 독립에 가까워지도록 뱃치를 구성하여 관리하거나, 관측치의 EWMA (지수 가중치 이동 평균) 통계량을 EWMA 관리도에 적용하는 방법등이 주로 사용되고 있다. 본 논문에서는 관찰치에 대한 관리 방법으로 일반적으로 사용되는 UBM 의 뱃치 크기를 결정하는 방법을 소개하고, ARL(평균 실행 길이)을 기반으로 최적 뱃치 크기를 정하는 방법과 그러한 뱃치 구성에서 공정의 표준 편차를 추정하는 방법을 제안 한다. 자기상관이 존재하는 공정에 대한 개선된 관리도를 제안하고자 한다.

• 품질경영학회지
• /
• 제35권1호
• /
• pp.24-34
• /
• 2007

This paper investigates the average run length (ARL) of a selectively moving average (S-MA) control chart. The S-U chart is designed to detect shifts in the process mean. The basic idea of the S-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The ARL of the S-MA chart was shown to be monotone decreasing with respect to the decision length in a previous research [3]. This paper derives the steady-state ARL in a closed-form and shows that the monotone property is resulted from head-start assumption. The steady-state ARL is shown to be a sum of head-start ARL and an additional term. The statistical design procedure for the S-MA chart is revised according to this result. Sensitivity study shorts that the steady-state ARL performance is still better than the CUSUM chart or the Exponentially Weighted Moving Average (EWMA) chart.

• 한국멀티미디어학회논문지
• /
• 제20권1호
• /
• pp.43-50
• /
• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• Journal of the Korean Statistical Society
• /
• 제23권1호
• /
• pp.89-102
• /
• 1994

A new method for approximating the average run length (ARL) of cumulative sum (CUSUM) chart is proposed. This method uses the conditional expectation for the test statistic before the stopping time and its asymptotic conditional density function. The values obtained by this method are compared with some other methods in normal and exponential case.

• Journal of the Korean Data and Information Science Society
• /
• 제15권3호
• /
• pp.633-644
• /
• 2004

Exponentially Weighted Moving Average(EWMA) control chart for variance-covariance matrix of several quality characteristics based on accumulate-combine approach has proposed. Numerical computations show that multivariate EWMA chart based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Communications for Statistical Applications and Methods
• /
• 제6권1호
• /
• pp.69-76
• /
• 1999

A new statistic {{{{ {d }`_{2 } ^{s } }}}} is introduced for constructing co ntrol limits. It is easier and more convienient than d2 We will show the characteristic of {{{{ {d }`_{2 } ^{s } }}}} and evaluate {{{{ {d }`_{2 } ^{s } }}}} through average run length(ARL).

• Communications for Statistical Applications and Methods
• /
• 제5권3호
• /
• pp.645-657
• /
• 1998

During the start-up of a process or in a job-shop environment conventional use of control charts may lead to erroneous results due to the limited number of subgroups used for the construction of control limits. This article considers the effect of using estimated control limits based on a limited number of subgroups. Especially we investigate the performance of $\overline{X}$ and R control charts when the data are independent, and X control chart when the data are serially correlated in terms of average run length(ARL) and standard deviation run length(SDRL) using simulation. It is found that the ARL and SDRL get larger as the number of subgroups used for the construction of the chart becomes smaller.