• 품질경영학회지
• /
• 제25권3호
• /
• pp.62-73
• /
• 1997

Previous VSI control chart works have been done on quality variable whose distribution is normal. But there are many situations in which hte assumption of not a, pp.opriate. Also, in many industrial processes, the interest is to monitor the number of defectives. In this paper, we will take the existing properties of VSI control chart developed for the normal distribution and a, pp.y them to the np-chart based on the discrete binomial distribution. We will consider the CUSUM chart for the number of defectives. Here, the interesting object is to compute the VSI ATS for CUSUM control chart using Markov chain a, pp.oach and to compare FSI ATS and VSI ATS.

• 한국태양에너지학회 논문집
• /
• 제22권4호
• /
• pp.10-17
• /
• 2002

This study aims to develop the analysis tool that assesses the sunlight at any given point of a window or solar collector array shaded by surrounding obstacles. The development of this software, named SunChart, focused to the user-friendliness and the reliability. This SunChart can calculate the solar radiation as well as shading on the certain face. The calculation results by SunChart show by both numerically and graphically and are in a good agreement with ones obtained from "Sunrise Sunset" developed at Korea Astronomy Observatory and from TRNSYS.

• 대한산업공학회지
• /
• 제33권4호
• /
• pp.457-468
• /
• 2007

This paper proposes an adaptive moving average (A-MA) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI A-MA chart is to adjust sampling intervals as well as to accumulate previous samples selectively in order to increase the sensitivity. The VSI A-MA chart employs a threshold limit to determine whether or not to increase sampling rate as well as to accumulate previous samples. If a standardized control statistic falls outside the threshold limit, the next sample is taken with higher sampling rate and is accumulated to calculate the next control statistic. If the control statistic falls within the threshold limit, the next sample is taken with lower sampling rate and only the sample is used to get the control statistic. The VSI A-MA chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L-consecutive control statistics fall outside the threshold limit. The control length L is introduced to prevent small mean shifts from being undetected for a long period. A Markov chain model is employed to investigate the VSI A-MA sampling process. Formulae related to the steady state average time-to signal (ATS) for an in-control state and out-of-control state are derived in closed forms. A statistical design procedure for the VSI A-MA chart is proposed. Comparative studies show that the proposed VSI A-MA chart is uniformly superior to the adaptive Cumulative sum (CUSUM) chart and to the Exponentially Weighted Moving Average (EWMA) chart, and is comparable to the variable sampling size (VSS) VSI EWMA chart with respect to the ATS performance.

• Journal of the Korean Data and Information Science Society
• /
• 제22권1호
• /
• pp.29-35
• /
• 2011

결점수를 모니터링하기 위한 통계적 공정관리는 생산공정에 널리 사용된다. 결점 수를 모니터링 하는데는 c-관리도가 사용된다. 전통적인 c-관리도는 표본에서 결점의 발생은 포아송분포를 따른다는 가정 하에서 만들어진다. 포아송분포에 대한 가정이 맞지 않을 때에는 X-관리도가 사용될 수 있다. 누적합 관리도는 공정의 작은 변화를 찾는데 유용한 것으로 알려져 있다. 본 논문에서는 다양한 Katz 분포족으로부터 생성된 계수자료에 대하여 3시그마 X-관리도와 누적합 관리도의 효율을 평균런의길이에 근거하여 비교 한다. 즉, 자료가 어떤 분포로부터 생성되었는지 알 수 없을 때, X-관리도와 누적합 관리도를 비교하는 것이다.

• 응용통계연구
• /
• 제25권3호
• /
• pp.465-470
• /
• 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.

• 한국의류학회지
• /
• 제16권2호
• /
• pp.255-262
• /
• 1992

A computer simulation methiod for costume design has been developed using LUMENA, a generic-purpose 2-dimensional graphic software. In this study the palette, tone chart, fabric chart, styling chart, and costume drawing were constructed on the computer. In costume design simulation, fabric swatches with various colors and patterns were applied to the base garment image taken by using a scanner or a video camera. In this procedure the original 3-dimensional effect was fully retained. Using this simulation method, a number of costume designs could be carried out in short time without actually making the garment. A portfolio including the tone chart, fabric chart, styling chart, costume drawing, and simulation results were made for the purpose of demonstration, using the animation tools of LUMENA.

• 경영과학
• /
• 제31권3호
• /
• pp.27-39
• /
• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

• 산업경영시스템학회지
• /
• 제35권4호
• /
• pp.118-125
• /
• 2012

When monitoring an instrumental process, one often collects a host of data such as characteristic signals sent by a sensor in short time intervals. Characteristic data of short time intervals tend to be autocorrelated. In the instrumental processes often the practice of adjusting the setting value simply based on the previous one, so-called 'adjacent point operation', becomes more critical, since in the short run the deviations are harder to detect and in the long run they have amplified consequences. Stochastic modelling using ARIMA or AR models are not readily usable here. Due to the difficulty of dealing with autocorrelated data conventional practice is resorting to choosing the time interval where autocorrelation is weak enough then to using I-MR control chart to judge the process stability. In the autocorrelated instrumental processes it appears that using the Shewhart chart and the time interval data where autocorrelation is relatively not existent turns out to be a rather convenient and very useful practice to determine the process stability. However in the autocorrelated instrumental processes we intend to show that one would presumably do better using the EWMA control chart rather than just using the Shewhart chart along with some arbitrarily intervalled data, since the former is more sensitive to shifts given appropriate weights.

• 응용통계연구
• /
• 제31권4호
• /
• pp.485-495
• /
• 2018

CCC-r 관리도는 고품질공정에서 공정불량률을 관리하는 경우 매우 효율적이라고 알려져 있다. 이 관리도를 사용할 때 관리상태의 공정모수는 일반적으로 알려져 있지 않기 때문에 제1국면의 표본을 추출하여 이를 추정해야 한다. 제2국면에서 관리도의 성능은 제1국면에서 추정한 모수와 관리한계에 영향을 받기 때문에, 추정 오차의 영향을 살펴보는 것은 중요하다. 이 논문에서는 일반적으로 많이 사용하는 평균런길이의 평균(average of average run length) 이외에 평균런길이의 표준편차(standard deviation of average run length)를 사용하여 CCC-r 관리도의 관리상태의 성능을 평가하였다. 그 결과 CCC-r 관리도에서 안정적인 관리상태의 성능을 유지하기 위해서는 이전에 권장하던 제1국면의 표본 크기보다 훨씬 더 큰 표본이 필요하다는 사실을 알 수 있었다.

• 한국가스학회지
• /
• 제16권4호
• /
• pp.8-15
• /
• 2012

GO-FLOW기법은 시스템이 정상적으로 작동할 가능성을 평가할 수 있는 기법으로써 신호선과 연산자를 사용하여 시스템을 GO-FLOW Chart로 모델화하고, 이 GO-FLOW Chart를 순차적으로 해석하여 시스템의 신뢰도를 평가하는 기법이다. 그러나 GO-FLOW기법은 1개의 시스템 구성요소를 여러 개의 연산자로 모델링하므로 시스템 흐름도와 상이한 GO-FLOW Chart가 작성될 수 있고, 시간점을 지정하여 시간을 모델링하므로 실제 운전시간에 따른 신뢰도 변화를 평가하기 어렵다. 따라서 본 연구에서는 구성요소의 기능(정상/고장)을 기준으로 시스템의 신뢰도를 평가하는 기법을 개발하였다. 본 기법은 구성요소의 운전상태와 상관없이 그 기능을 유지할 가능성을 기준으로 시스템의 신뢰도를 평가하며, 1개의 구성요소는 1개의 연산자로 모델링하므로 시스템 흐름도와 거의 유사한 모델도를 작성할 수 있고, 실제시간을 반영한 연산자를 사용하여 실제시간에 따른 시스템의 신뢰도를 쉽게 평가할 수 있다.