• Journal of Korean Society for Quality Management
• /
• v.45 no.4
• /
• pp.943-956
• /
• 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.

• Journal of Korean Society for Quality Management
• /
• v.24 no.1
• /
• pp.73-86
• /
• 1996

Adaptive EWMA(Exponentially Weighted Moving Average)-x control chart using the Kalman gain recursive average is designed. The designed control chart is effective to on-line process monitoring as continuous flow processes. Performance evaluation between the designed control chart and traditional one is implemented. For this, ARL(Average Run Length) is adopted as a criterion. Results show that the designed adaptive EWMA-x control chart has shorter ARL than EWMA-x control chart when process mean is shifted. This model can be extended to process prevention control. The methodology proposed in this research is turned out to show the high performance than that of the given methodologies.

• Journal of Korean Society for Quality Management
• /
• v.33 no.3
• /
• pp.126-134
• /
• 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 Korean Society of Industrial and Systems Engineering
• /
• v.32 no.1
• /
• pp.94-101
• /
• 2009

Complex Products may present more than one type of defects and these defects are not always of equal severity. These defects are classified according to their seriousness and effect on product quality and performance. Demerit systems are very effective systems to monitoring the different types of defects. So, classical demerit control chart used to monitor counts of several different types of defects simultaneously in complex products. S.M. Na et al.(2003) proposed the Demerit-CUSUM for the improvement of the demerit control chart performance and Nembhard, D. A. et al.(2001) and G.Y Cho et al.(2004) developed a Demerit control chart using the EWMA technique and evaluated the performance of the control chart. In this paper, we present an effective method for process control using the Demerit-CUSUM with fast initial response. Moreover, we evaluate exact performance of the Demerit-CUSUM control chart with fast initial response, Demerit-CUSUM and Demerit-EWMA according to changing sample size or parameters.

• Journal of Korean Society for Quality Management
• /
• v.38 no.3
• /
• pp.313-321
• /
• 2010

The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from each other. Recently, the CV control chart is developed for monitoring processes in such situations. However, the CV control chart has low performance in detecting small shift. Due to the development of equipment and technique, currently, small shift of process occurs more frequently than large shift. In this paper, we proposes the CV-CUSUM control chart using CUSUM scheme which is cumulative sum of the deviations between each data point and a target value to detect a small shift in the process. We also found that the FIR(fast initial response) CUSUM control chart is especially valuable at start-up or after a CV-CUSUM control chart has signaled out-of-control.

• Journal of Korean Ophthalmic Optics Society
• /
• v.15 no.1
• /
• pp.67-71
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.33 no.2
• /
• pp.161-170
• /
• 2020

The combined ${\bar{X}}-S^2$ chart is a traditional control chart for simultaneously detecting mean and variance. Control limits for the combined ${\bar{X}}-S^2$ chart are determined so that each chart has the same individual false alarm rate while maintaining the required false alarm rate for the combined chart. In this paper, we provide flexibility to allow the two charts to have different individual false alarm rates as well as evaluate the effect of flexibility. The individual false alarm rate of the ${\bar{X}}$ chart is taken to be γ times the individual false alarm rate of the S² chart. To evaluate the effect of selecting the value of γ, we use the out-of-control average run length and relative mean index as the performance measure for the combined ${\bar{X}}-S^2$ chart.

• Journal of Korean Ophthalmic Optics Society
• /
• v.18 no.4
• /
• pp.549-554
• /
• 2013

Purpose: To determine the minimal illumination for visual acuity with the decimal vision chart and individual difference with varying contrast and illumination. Methods: Landolt's vision chart with 10 different grade of contrast was made. Minimal illumination was measured for 69 eyes using 100% contrast vision chart. Minimal contrast for identifying the chart was measured in conditions of the minimal illumination and 230 lx, respectively. Results: Minimal illumination was gradually increased with decrease of the chart size. Mean of minimal illumination to identify the 1.0 visual acuity was $74.39{\pm}25.90$ lx with range of 17~107 lx. In conditions of the minimal illumination and 230 lx, the minimal contrast for identifying the chart were gradually increased with decrease of the chart size in both conditions, the those deviation for 69 eyes was also increased. Conclusions: For refractive correction, examiners need to consider the individual difference for identifying the visual chart according to illumination and contrast.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.7 no.2
• /
• pp.151-156
• /
• 2012

In this paper, we gave 3 suitable modified control chart at the same time shows. First control chart is plot analysis control chart by sampling data, Second control chart is including Upper Spec and Lower Spec in first control chart. Third control chart is including target value and new calculated upper/lower control line by using known average and standard deviation of long term data. If 3 control charts are seen at the same time, there is advantage that can see problem of process at a look. We hope this method proposed in this treatise give many help in spot.

• The Korean Journal of Applied Statistics
• /
• v.21 no.3
• /
• pp.509-521
• /
• 2008

Two control charts are usually required to monitor both the process mean and variance. In this paper, we introduce control procedures for jointly monitoring the process mean and variance with one control chart, and investigate efficiency of the introduced charts by comparing with the combined two EWMA charts. Our numerical results show that the GLR chart, the Omnibus EWMA chart, and the Interval chart have good ARL properties for simultaneous changes in the process mean and variance.