• Journal of Integrative Natural Science
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• v.9 no.3
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• pp.215-222
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• 2016

When the linear correlation of the quality variables are considerably high, multivariate control charts may be a more effective way than univariate charts which operate quality variables and process parameters individually. Performances and efficiencies of the multivariate control charts under multivariate normal process has been considered. Some numerical results are presented under small scale of the shifts in the process to see the improvement of the efficiency of EWMA chart and CUSUM chart, which use past quality information, comparing to Shewart chart, which do not use quality information. We can know that the decision of the optimum value of the smoothing constant in EWMA structure or the reference value in CUSUM structure are very important whether we adopt combine-accumulate technique or accumulate-combine technique under the given condition of process.

• Journal of Korean Society for Quality Management
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• v.23 no.4
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• pp.1-12
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• 1995

Sample skewness has not received much attention from researchers to design a control chart. In this paper, control charts based on two skewness measures are studied to control a manufacturing process. One skewness measure is the third central moment about mean, the other is the third L-moment which is a linear combination of order statistics. Since the exact sampling distributions of two skewness measures are unknown, empirical sampling distributions are studied using simulation. The sampling distributions are used to design control charts for skewness and performance of two skewness measures is compared.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.69-79
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• 1997

We investigate both of central and dispersion tendencies of the observed test statistics in control charts in order to judge whether a production process is abnormal or not. In order to do it, first, we study about detection of changes of the population mean as a central tendency The $\bar{x}$ and x control charts are used for detecting the change of the population mean $\mu$. We shows the probability detecting the change of population mean using the $\bar{x}$ and x control charts. Secondly, we study about detection of changes of the population standard deviation as a dispersion tendency in the s control chart. In our studies, for the given several parameters the detection probabilities of changes of central and dispersion tendencies are calculated, the necessary sample size values n are suggested for detecting the changes, and their informations are given as various tables.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.117-122
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• 2005

Variable sampling rate(VSR) scheme varies the sampling rate for the current sample depending on the previous value of the control statistic. In this paper, we propose EWMA control charts with variable parameter(VP) scheme, which allows both the sample rate(the sample size or the sampling interval) and the weight to vary. We investigate the effectiveness of the VP scheme relative to the fixed parameter(FP) scheme and the VSR scheme in EWMA control charts. It is shown that using the VP scheme gives some improvements to the ability in detecting small and moderate shifts in the process normal mean.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.597-604
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• 2007

There are many uses of control charts in health-care monitoring and in public-health surveillance. For example, control charts are used in monitoring and improvement of hospital performance, in monitoring chronic diseases and infectious diseases, and so on. We introduce the Variable Life-Adjusted Display (VLAD) chart and propose the method for choosing control limits of the VLAD chart to give specified in-control properties.

• Journal of Korean Society for Quality Management
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• v.26 no.1
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• pp.126-142
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• 1998

Traditional statistical process control(SPC) assumes that consective observations from a process are independent. In industrial practice, however, observations are ofter serially correlated. A common a, pp.oach to building control charts for autocorrelatd data is to a, pp.y classical SPC to the residuals from a time series model fitted. Unfortunately, one cannot completely escape the effects of autocorrelation by using charts based on residuals of time series model. For the detection of variance change in the process we propose a CUSUM of squares control chart which does not require the model identification. The proposed CUSUM of squares chart and the conventional control charts are compared by a Monte Carlo simulation. It is shown that the CUSUM of squares chart is more effective in the presence of dependency in the processes.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.107-115
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• 2006

Control charts are important tools of statistical quality control. In 1956, Duncan first proposed the economic design of $\bar{x}-control$ charts to control normal process means and insure that the economic design control chart actually has a lower cost, compared with a Shewhart control chart. An moving average (MA) control chart is more effective than a Shewhart control chart in detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. An economic design of MA control chart has also been proposed in 2005. The weaknesses to only the economic design are poor statistics because it dose not consider type I or type II errors and average time to signal when selecting design parameters for control chart. This paper provides a construction of an economic-statistical model to determine the optimal parameters of an MA control chart to improve economic design. A numerical example is employed to demonstrate the model's working and its sensitivity analysis is also provided.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.105-112
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• 2002

In this paper we establish bivariate exponentially weighted moving average (EWMA) control charts for autocorrelated processes using residual vectors. We first derive the residual vectors, their expectation, variance-covariance matrix, then evaluate the control chart based on the average run length (ARL).

• IE interfaces
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• v.8 no.3
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• pp.249-257
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• 1995

Expert system is an effective approach for quality system to be automated and thus to be an essential integrating mechanism in any move towards CIM(Computer Integrated Manufacturing). A quality control expert system is introduced and its relationship to CIM is illustrated in a case study. Process control expert system developed by Kim and Sin[6] has been improved via ODBC(Open DataBase Connectivity) for efficient information network, graph representation using Windows API for rapid response and some new rules for identification of patterns in control charts.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.12 no.sup1
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• pp.1-5
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• 2009

2007 Korean National Growth Charts were published by The Korean Pediatric Society and Korea Centers for Disease Control and Prevention in October, 2007. These Growth Charts are composed of Growth Curves and Tables, which are based on the principles such as pooling data of the different generations and application of LMS method. In this review, several tips of application using these new charts are summarized with points of view both in clinical and research fields.