• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.135-140
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• 2002

변량추출비 관리도는 현재의 관측값에 기초하여 다음 시점의 표본크기와 표본추출간격을 변화시키면서 공정의 변화를 탐지하는 관리도 절차이다. 만일 공정에서 추출한 현재의 관측값을 살펴볼 때 공정변화의 징후가 있는 경우에는 다음 시점의 표본추출비를 증가시켜, 즉 표본크기를 크게 하고 표본추출간격을 작게 하여 예상되는 공정변화를 더 빠르고 정확하게 탐지함으로보다 효율적인 공정관리를 수행하는 것이다. 본 연구는 변량추출비 ${\bar{X}}$ 관리도에서 사용하는 표본크기와 표본추출간격의 수를 달리하며 각각의 경우에 대한 통계적 효율을 계산하고 이를 비교하고자 한다.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.429-440
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• 2003

Variable sampling rate (VSR) control charts vary the sampling interval and/or the sample size according to value of the control statistic. It is known that $\bar{X}$ charts with VSR scheme lead to large improvements in performance over those with fixed sampling rate (FSR) scheme. In this paper, we studied $\bar{X}$ charts with several VSR schemes, and compared their statistical performance each other.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.67-72
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• 2005

이 논문은 표본크기와 표본추출간격 이외의 관리모수인 EWMA 관리도의 가중치를 이전 시점의 관리통계량 값에 기초하여 변화시키는 VW(variable weight) 방법에 대한 것이다. 이 방법을 VSR(variable sampling rate)과 병행하는 절차를 제안하고, 절차의 효율에 대하여 알아보았다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.30
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• pp.101-106
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• 1994

The basic of the VSI charts is that if the sample statistic computed after a sample is taken shows some indication of a process change than the sampling interval before the next sample should be short otherwise long. This paper was shown the VSS chart can be considerably more efficient than the FSS chart and the effectiveness of VSI R chart with S chart used for monitoring a process standard deviation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.143-151
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• 1995

In this design the sampling interval that each sampling time changes according to the valuse of the previous to sample statistics, sample mean and ranges. The VSI scheme uses large sample if the sample statistics appear near in side the control limits and smaller sample otherwise. The efficiency of the VSI scheme is compare to the FSI. It is shown that VSI control chart improves the confidence of the procedure and performens better than FSI control chart.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.