• 품질경영학회지
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• 제45권2호
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• pp.209-225
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• 2017

Purpose: Since traditional p chart is unable to deal with the variation of attribute data, this paper proposes a new attribute control chart for nonconforming proportions incorporating overdispersion with a beta-binomial model. Methods: Statistical theories for control chart developed under the beta-binomial model and a new approach using this control chart are presented Results: False alarm probabilities of p chart with the beta-binomial model are evaluated and demerits of p chart under overdispersion are discussed from three examples. Hence a concrete procedure for the proposed control chart is provided and illustrated with examples Conclusion: The proposed chart is more useful than traditional p chart, individual chart to treat observed proportions nonconforming as variable data and Laney p' chart.

• 한국터널지하공간학회 논문집
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• 제10권4호
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• pp.383-395
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• 2008

연약지반개량 기술 중의 하나인 그라우팅이 차수 보강 목적으로 다양한 건설공사에서 널리 활용되고 있지만 그라우팅공법이 지반내부에서 시행되는 기술적 특성상 그 시공과정의 적합성 및 시공 후 효과확인 방법에 대한 문제점이 많다. 본 연구 대상의 그라우팅 p-q-t chart는 주입속도(q)와 주입압(p)의 경시변화(t)에 대한 관계를 나타내는 지표로서 그라우팅 시공과정을 모니터링하여 시공인자의 적합성을 평가하여 조정할 수 있는 유일한 수단이다. 본 연구에서는 시험 및 시공과정을 일괄적으로 관리할 수 있는 자동 주입관리 시스템(AGS; automatic grouting system)를 이용하여 정밀한 p-q-t chart를 검출할 수 있었으며, 검출된 p-q-t chart의 유형을 표준유형과 비교분석을 통해서 대상지반의 주입특성에 대한 정보를 추정할 수 있었다. 향후 본 연구에서 적용한 자동 주입관리 시스템을 이용하여 도출된 p-q-t chart를 활용한다면 주입재료, 주입공법, 주입속도 및 주입압 등 시공인자의 적합성을 판정하고 조정하는 객관적 지표로 활용하여 그라우팅 시공의 신뢰성을 획기적으로 향상시킬 수 있을 것으로 기대된다.

• 한국정밀공학회지
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• 제15권2호
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• pp.75-80
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• 1998

Process information is known to have the continuous distribution in many manufacturing processes. Generalized p-chart has been developed for controlling processes by classifying the information characteristics into several groups. But it is improper to describe continuous processes with the classified process informal ion, which is based on the classical set concept. Fuzzy control chart, has been developed for the control of linguistic data, but it is also based on the dichotomous notion of classical set theory. In this paper, fuzzy sampling method is studied in order to process the uncertain data properly. The method is incorporated with the fuzzy control chart. Statistical characteristics of the fuzzy representative value are utilized to device the fuzzy-statistical control chart. The fuzzy-statistical control chart is compared with the generalized p-chart and both the sensitivity to the process information distribution change pared robustiness against the noise on the process information of the fuzzy-statistical control chart are shown to be superior.

• 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.

• Industrial Engineering and Management Systems
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• 제7권2호
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• pp.101-112
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• 2008

As charts to monitor the process fraction defectives, P, in the high-yield processes, Mishima et al. (2002) discussed a synthetic chart, the Synthetic CS chart, which integrates the CS (Confirmation Sample)$_{CCC(\text{Cumulative Count of Conforming})-r}$ chart and the CCC-r chart. The Synthetic CS chart is designed to monitor quality characteristics in real-time. Recently, Kotani et al. (2005) presented the EWMA (Exponentially Weighted Moving-Average)$_{CCC-r}$ chart, which considers combining the quality characteristics monitored in the past with one monitored in real-time. In this paper, we present an alternative chart that is more superior to the $EWMA_{CCC-r}$ chart. It is an integration of the $EWMA_{CCC-r}$ chart and the CCC-r chart. In using the proposed chart, the quality characteristic is initially judged as either the in-control state or the out-of-control state, using the lower and upper control limits of the $EWMA_{CCC-r}$ chart. If the process is not judged as the in-control state by the $EWMA_{CCC-r}$ chart, the process is successively judged, using the $EWMA_{CCC-r}$ chart. We compare the ANOS (Average Number of Observations to Signal) of the proposed chart with those of the $EWMA_{CCC-r}$ chart and the Synthetic CS chart. From the numerical experiments, with the small size of inspection items, the proposed chart is the most sensitive to detect especially the small shifts in P among other charts.

• 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.

• Industrial Engineering and Management Systems
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• 제4권1호
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• pp.75-81
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• 2005

Borror et al. discussed the EWMA(Exponentially Weighted Moving Average) chart to monitor the count of defects which follows the Poisson distribution, referred to the $EWMA_c$ chart, as an alternative Shewhart c chart. In the $EWMA_c$ chart, the Markov chain approach is used to calculate the ARL (Average Run Length). On the other hand, in order to monitor the process fraction defectives P in high-yield processes, Xie et al. presented the CCC(Cumulative Count of Conforming)-r chart of which quality characteristic is the cumulative count of conforming item inspected until observing $r({\geq}2)$ nonconforming items. Furthermore, Ohta and Kusukawa presented the $CS(Confirmation Sample)_{CCC-r}$ chart as an alternative of the CCC-r chart. As a more superior chart in high-yield processes, in this paper we present an $EWMA_{CCC-r}$ chart to detect more sensitively small or moderate shifts in P than the $CS_{CCC-r}$ chart. The proposed $EWMA_{CCC-r}$ chart can be constructed by applying the designing method of the $EWMA_C$ chart to the CCC-r chart. ANOS(Average Number of Observations to Signal) of the proposed chart is compared with that of the $CS_{CCC-r}$ chart through computer simulation. It is demonstrated from numerical examples that the performance of proposed chart is more superior to the $CS_{CCC-r}$ chart.

• 품질경영학회지
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• 제44권1호
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• pp.167-180
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• 2016

Purpose: The problem for the traditional control chart is that it is unable to monitor the very small fraction of nonconforming and the underlying distribution is the normal distribution. $Z_p$ control chart is useful where it controls the vert small fraction on nonconforming. In this study, we will design the $Z_p$ control chart in order to use under non-normal process. Methods: $Z_p$ is calculated not by failure rate based on attribute data but using variable data. Control limit for non-normal $Z_p$ control chart is designed based on ${\alpha}$-risk calculated by cumulative distribution function of Burr distribution. ${\beta}$-risk, which is for performance evaluation, obtains in the Burr distribution's cumulative distribution function and control limit. Results: The control limit for non-normal $Z_p$ control chart is designed based on Burr distribution. The sensitivity can be checked through ARL table and OC curve. Conclusion: Non-normal $Z_p$ control chart is able to control not only the very small fraction of nonconforming, but it is also useful when $Z_p$ distribution is non-normal distribution.

• 한국안광학회지
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• 제15권1호
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• pp.67-71
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• 2010

Purpose: The effect of two different levels of illumination and the effect of three letter chart types on subjective refraction findings were investigated. Methods: This study involved thirty Malay university students aged between 19 to 23 years old (7 males, 23 females), with their spherical refractive error ranged between plano to -7.75D astigmatism ranged from plano to -1.75D, anisometropia less than 1D and with no history of ocular injury and pathology. Monocular subjective refraction was measured under two levels of illumination (with and without room light) and with three different letter charts (Snellen letter chart, wall mounted letter chart and projected letter chart). Subjective refraction finding was calculated in spherical equivalent in unit diopter (D). Results: There was no significant effect in the subjective refraction findings with Snellen letter chart (t-test=0.15, p-value=0.88), projected letter chart (t-test=-0.19, p-value=0.85) as well as wall mounted letter chart (t-test=0.12, p-value=0.94). One Way ANOVA also revealed when the subjective refractive measures were compared under two different level of room illumination (with and without room light), no significant effect of letter chart types on subjective refraction readings with room light ($F_{2.185}$=0.11, p-value=0.89) and without room light ($F_{2.185}$=0.02, p-value=0.98). Conclusions: Subjective refraction findings were not affected whether the room light was on or off. They were also not affected by the types of letter chart used.

• 대한안전경영과학회지
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• 제14권3호
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• pp.167-174
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• 2012

In the manufacturing process the most widely used $\bar{X}$ chart has been applied to control the process mean. Also, Accelerated Life Test(ALT) is commonly used for efficient assurance of product life in development phases, which can be applied in production reliability acceptance test. When life data has lognormal distribution, through censored ALT design so that censored ALT data has asymptotic normal distribution, $ALT\bar{X}$ control chart integrating $\bar{X}$ chart and ALT procedure could be applied to control the mean of process in the manufacturing process. In the situation that process variation is controlled, $Z_p$ control chart is an effective method for the very small fraction nonconforming of quality characteristic. A simultaneous control scheme with $ALT\bar{X}$ control chart and $Z_p$ control chart is designed for the very small fraction nonconforming of product lifetime.