• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.457-468
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• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.560-570
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• 2006

This paper proposes a selectively cumulative sum (S-CUSUM) control chart with variable sampling intervals (VSI) for detecting shifts in the process mean. The basic idea of the VSI S-CUSUM chart is to adjust sampling intervals and to accumulate previous samples selectively in order to increase the sensitivity. The VSI S-CUSUM chart employs a threshold limit to determine whether to increase sampling rate as well as to accumulate previous samples or not. 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 S-CUSUM 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 number L is a decision variable and is called a 'control length'. A Markov chain model is employed to describe the VSI S-CUSUM sampling process. Some useful 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 S-CUSUM chart is proposed. Comparative studies show that the proposed VSI S-CUSUM chart is uniformly superior to the VSI CUSUM chart or to the Exponentially Weighted Moving Average (EWMA) chart with respect to the ATS performance.

• Korean Management Science Review
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• v.31 no.3
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• pp.27-39
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• 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.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.811-823
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• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.126-134
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• 2005

This paper proposes a selectively cumulative sum(S-CUSUM) control chart for detecting shifts in the process mean. The basic idea of the S-CUSUM chart is to accumulate previous samples selectively in order to increase the sensitivity. The S-CUSUM chart employs a threshold limit to determine whether to accumulate previous samples or not. Consecutive samples with control statistics out of the threshold limit are to be accumulated to calculate a standardized control statistic. If the control statistic falls within the threshold limit, only the next sample is to be used. During the whole sampling process, the S-CUSUM 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 number L is a decision variable and is called a 'control length'. A Markov chain approach is employed to describe the S-CUSUM sampling process. Formulae for the steady state probabilities and the Average Run Length(ARL) during an in-control state are derived in closed forms. Some properties useful for designing statistical parameters are also derived and a statistical design procedure for the S-CUSUM chart is proposed. Comparative studies show that the proposed S-CUSUM chart is uniformly superior to the CUSUM chart or the Exponentially Weighted Moving Average(EWMA) chart with respect to the ARL performance.

• Journal of Fisheries and Marine Sciences Education
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• v.22 no.4
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• pp.553-565
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• 2010

The paper aims to evaluate the quality of fisheries census statistic and to provide some desirable directions and improvements for the future fisheries census, conducted by the Government. For the quality diagnosis of fisheries census statistic, specific processes of fisheries census and statistical qualities of each dimension are surveyed and evaluated by a Government's practician, two external examiners and a research group. Results show that census design, data analysis and quality control are evaluated relatively low in specific processes, and accessibility and comparability are evaluated relatively lower than relevance, accuracy, timeliness and consistency in statistical qualities. For minimizing the sampling errors, the probability proportion method should be employed in sampling methods from currently simple sampling method. In addition, fisheries census statistic is desirable to include and compare with those of different countries for consumer oriented data system.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.463-475
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• 2021

Desgagné and de Micheaux (2018) proposed an alternative univariate normality test to the Jarque-Bera test. The proposed statistic is based on the sample second power skewness and kurtosis while the Jarque-Bera statistic uses sample Pearson's skewness and kurtosis that are the third and fourth standardized sample moments, respectively. In this paper, we generalize their statistic to a multivariate version based on orthogonalization or an empirical standardization of data. The proposed multivariate statistic follows chi-squared distribution approximately. A simulation study shows that the proposed statistic has good control of type I error even for a very small sample size when critical values from the approximate distribution are used. It has comparable power to the multivariate version of the Jarque-Bera test with exactly the same idea of the orthogonalization. It also shows much better power for some mixed normal alternatives.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.818-826
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• 2002

Since multipath phenomenon frequently occurs when a Global Positioning System receiver is placed in urban area crowded with large buildings, efficient mitigation of multipath effects is necessary to resolve. In this paper, we propose a new multipath detection technique that is useful in real-time positioning with a general Global Positioning System receiver. The proposed technique is based on a channelwise multipath test statistic that efficiently indicates the degree of fluctuations induced by multipath error. The proposed multipath test statistic is operationally advantageous because it does not require any specialized hardware nor any pre-computation of receiver position, it is directly related to standard $\chi$$^2$-distributions, and it can adjust the detection resolution by increasing the number of successive measurements. Simulation and experiment results verify the performance of the proposed multipath detection technique.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.741-744
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• 2004

본 연구는 손의 형상을 복잡한 배경환경에서 손 영역을 안정적으로 검출, 인식하여 윈도우 플레이어의 기능을 제어하는 시스템을 제안하였다. 손은 형상이 매우 복잡하기 때문에 2차원 형상의 불변량에 해당하는 에지의 방향성 히스토그램을 이용하여 인식을 행한다. 이 방법은 복잡한 배경에서 피부색을 지닌 손 영역이 정확히 추출되며 손 형상을 인식하는데 있어서 수행속도가 빠르고 조명변화에 덜 민감하기 때문에 실시간 손 형상 인식에 적합하다. 본 논문에서 제안한 방법을 윈도우 플레이어 제어에 적용한 결과 안정적으로 제어 할 수 있었다.