• 한국의류학회지
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• 제24권7호
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• pp.1044-1055
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• 2000

The purpose of this study is to suggest sizing chart for a clean room wear. 3 control dimensions(Stature, Bust girth, B.N.P.∼Wrist point length) were chosen as 3 axes of clean room wear size chart. A loss function was used to determined intervals of stature, Bust girth and B.N.P.∼Wrist point length of size chart, because the loss function introduces the concept of frequency to size chart for better customer's size satisfaction. From the size table whose intervals had been determined by a loss function. The 4 sizes individually were suggested for clean room wear size chart by sex. The 3 sizes individually were suggested for clean room head cover size chart by sex too. The suggested size chart would be considered more feasible than present size chart. Also they are suggested supply reference measurement chart relevant to clean room wear manufacturing for 13 most frequent sizes.

• 대한인간공학회지
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• 제15권2호
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• pp.1-13
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• 1996

The purpose of this study is to suggest a brassiere sizing chart for elderly women. 2 control dimensions(under bust girth and cup size) were chosen as 2 axes of brassiere size chart. A loss function was used to determined intervals of under bust girth and cup size of size chart, because the loss function introduces the concept of frequency to size chart for better customer's satisfaction. From the dual distribution table whose intervals had been determined by a loss function. The 15 sizs, which had more than 2% of appearance were suggested for brassiere size chart. The suggested brassiere sizes covered 87.6% of all subjects. Considering that KS brassiere size chart consisting of 32 sizes covers 88.5%, the suggested brassiere size chart would be considered quite feasible. Also it is suggested supply reference measurement chart relevant to brassiere manufacturing for 10 most frequent sizes.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1995년도 추계학술대회논문집
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• pp.268-279
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• 1995

The purpose of this study is to suggest a brassiere sizing chart for elderly women. It is found that there is no direct linear relationship between cup size and under bust girth from the analsis of breast measurements. These 2 factors(under bust girth and cup size) were chosen as 2 axes of brassiere size chart. A loss function was used to determined intervals of bust girth and cup size of size chart, because the loss function introduces the concept of frequency to size chart for better customer's satisfaction. From the dual distribution table whose intervals had been determinde by a loss function. The 15 sizes, which had more than 2% of appearance were suggested for brassiere size chart. The suggested brassierc sizes covened 87.6% of all subjects. Considering that KS brassiere size thart consisting of 32 sizes covers 88.5%, the suggested brassiere size chart would be considered quite feasible. Also is suggested supply reference measurement chart relevant to brassiere manufacturing for 10 most frequent sizes.

• 대한가정학회지
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• 제33권6호
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• pp.199-211
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• 1995

The purpose of this study was to provide a brassiere size chart for making the well-fitted brassiere. The subjects are 2811 women whose range of age is 12 to 59. These woman were classified into 5 age groups by seniority ; age group 1(12~19), age group 2(20~29), age group 3(30~39), age group 4(40~49), age group 5(50~59). statistical differences of measurements were analyzed among 5 age groups through ANOVA. Correlation between measurements were analyzed by correlation analysis. In addition, new brassiere size chart and production rate tables were proposed in this study. The results of the study were as follows. 1) Most of the body measurements were significantly different among 5 age groups. The height was decreasing by getting older while weight was increasing significantly. The 3 girth measurements(top bust, bust, under bust girth) in breast, bust width, bust depth were apt to increase definitely. The 3 girth measurements had high Correlation coefficients among 3 girth mesurements. Therefore, it is valid to pick out cup size and under bust girth for representative items of size chart. 2) Under bust girth and cup size were chosen as 2 axes of brassiere size chart. From the dual distribution table whose intervals had been determined by KS size chart, 17 sizes, which had more than 2% of appearance, were suggested for brassiere size chart. Through these new size charts, the suggested brassiere sizes covered 82.5% of all subjects. The suggested brassiere size chart would be more helpful than KS size chart in making the well-fitted brassiere.

• 한국경영과학회지
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• 제34권1호
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• pp.29-37
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• 2009

This paper proposes a curtailed-sequential probability ratio test (SPRT) control chart. For using the conventional SPRT control chart, the number of items inspected in a sampling point should have no restriction since items in a sampling point are inspected one by one until an SPRT Is terminated. The number of observations taken in a sampling point, however, has an upper bound since sampling and testing of an item is time-consuming or expensive. When the sample size reaches the upper bound without evidence of an in-control or out-of-control state of a process, the proposed chart makes a decision using the sample mean of all observations taken in a sampling point. The properties of the Proposed chart are obtained by a Markov chain approach and the performance of the chart is compared with fixed sample size (FSS) and variable sample size (VSS) control charts. A comparative study shows that the proposed chart performs better than VSS control charts as well as conventional FSS control charts.

• 응용통계연구
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• 제25권3호
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• pp.465-470
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• 2012

EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

• 품질경영학회지
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• 제28권3호
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• pp.18-30
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• 2000

Recent studies have shown that the $\bar{X}$ chart with variable sampling intervals(VSI) and the $\bar{X}$ chart with variable sample size(VSS) are much quicker than Shewhart $\bar{X}$ chart in detecting shiks in the process. Shewhart $\bar{X}$ chart has been beneficial to detect large shifts but it is hard to apply Shewhart $\bar{X}$ chart in detecting moderate shifts in the process mean. In this article the $\bar{X}$ chart using variable sample size(VSS) and variable sampling Intervals(VSI) has been proposed to supplement the weak point mentioned above. So the purpose of this paper is to consider finding the design parameters which minimize expected loss costs for unit process time and measure the performance of VSSI(variable sample size and sampling interval) $\bar{X}$ chart. It is important that assignable causes be detected to maintain the process controlled. This paper has been studied under the assumption that one cycle is from starting of the process to eliminating the assignable causes in the process. The other purpose of this article is to represent the expected loss costs in one cycle with three process parameters(sample size, sampling interval and control limits) function and find the three parameters.

• 품질경영학회지
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• 제45권4호
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• pp.943-956
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• 2017

Purpose: This paper proposes a VSS(Variable Sample Size) ${\bar{X}}$ control chart using surrogate variable and shows its effectiveness compared with FSS(Fixed Sample Size) ${\bar{X}}$ control chart using either performance variable or surrogate variable. Methods: The expected cost function of VSS ${\bar{X}}$ control chart is derived. The optimal designs are then found for numerical examples using a GA(genetic algorithm) and compared to those of the FSS ${\bar{X}}$ control charts. Results: Computational results show that VSS ${\bar{X}}$ control chart using surrogate variables is superior to FSS ${\bar{X}}$ control chart using either performance variable or surrogate variable from the economic view points. Conclusion: The proposed VSS ${\bar{X}}$ control chart will be useful in industry fields where a performance variable is not avaliable or too costly.

• 대한산업공학회지
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• 제34권3호
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• pp.328-336
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• 2008

This research proposes a method for economic-statistical design of adaptive moving average (A-MA) charts. The basic idea of the A-MA chart is to accumulate previous samples selectively in order to increase the sensitivity. The A-MA chart is a kind of adaptive chart such as the variable sampling size (VSS) chart. A major advantage of the A-MA chart over the VSS chart is that it is easy to maintain rational subgroups by using the fixed sampling size. A steady state cost rate function is constructed based on Lorenzen and Vance (1986) model. The cost rate function is optimized with respect to five design parameters. Computational experiments show that the A-MA chart is superior to the VSS chart as well as to the Shewhart $\bar{X}$ chart in the economic-statistical sense.

• 품질경영학회지
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• 제25권3호
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• pp.74-85
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• 1997

In this paper we calculate the subgroup size necessary for detecting the change of percent defective with several detection probabilities for orginal population fraction nonconforming p, changed population fraction nonconforming $p^*$, and the ratio k=$p^*$/p in the usage of p control charts. From our calculation we can know the error level of normal a, pp.oximation in detection probability calculation and recommend the subgroup size with lower error levels of normal a, pp.oximation, and then we show the reasonable subgroup size necessary for p, $p^*$, k, and the detection probability of the change of fraction nonconforming in a process. The information that we here show in tables will be useful when p control chart users decide the subgroup size in the p control chart users decide the subgroup size in the p control chart.