• 대한안전경영과학회:학술대회논문집
• /
• 대한안전경영과학회 2006년도 춘계공동학술대회
• /
• pp.51-56
• /
• 2006

Recently data mining techniques have been used for analysis and classification of data related to industrial accidents. The main objective of this study is to compare performance of algorithms for data analysis of industrial accidents and this paper provides a comparative analysis of 5 kinds of algorithms including CHAID, CART, C4.5, LR (Logistic Regression) and NN (Neural Network) with ROC chart, lift chart and response threshold. In this study, data on 67,278 accidents were analyzed to create risk groups for a number of complications, including the risk of disease and accident. The sample for this work chosen from data related to manufacturing industries during three years $(2002\sim2004)$ in korea. According to the result analysis, NN has excellent performance for data analysis and classification of industrial accidents.

• Journal of the Korean Data and Information Science Society
• /
• 제16권2호
• /
• pp.467-476
• /
• 2005

Multivariate control charts for monitoring mean vector of several related quality variables with combine-accumulate approach and accumulate-combine apprach were investigated. Shewhart chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and multivariate charts based on accumulate- combine approach is more efficient than corresponding multivariate charts based on combine-accumulate approach.

• Journal of the Korean Data and Information Science Society
• /
• 제28권2호
• /
• pp.451-459
• /
• 2017

There are many researches showing that when a process change has occurred, variable sampling intervals (VSI) control chart is better than the fixed sampling interval (FSI) control chart in terms of reducing the required time to signal. When the process engineers use VSI control procedure, frequent switching between different sampling intervals can be a complicating factor. However, average number of samples to signal (ANSS), which is the amount of required samples to signal, and average time to signal (ATS) do not provide any control statistics about switching performances of VSI charts. In this study, we evaluate numerical switching performances of multivariate VSI EWMA chart including average number of switches to signal (ANSW) and average switching rate (ASWR). In addition, numerical study has been carried out to examine how to improve the performance of considered chart with accumulate-combine approach under several different smoothing constant and sample size. In conclusion, process engineers, who want to manage the correlation coefficients of related quality variables, are recommended to make sample size as large and smoothing constant as small as possible under permission of process conditions.

• 품질경영학회지
• /
• 제33권3호
• /
• pp.149-155
• /
• 2005

Cusum control chart is used for the purpose of controling the process mean. We consider the problem related to cusum chart for controling process variance. Previous researches have considered the same problem. The main difficulty shown in the related researches was to derive the ARL function which characterizes the properties of the chart. Sample variance, differently with sample mean, follows chi-squared type distribution, even when the quality characteristics are assumed to be normally distributed. The ARL function of cusum is described by a type of integral equation. Since the solution of the integral equation for non-normal distribution is not known well, people used simulation method instead of solving the integral equation directly, or approximation method by taking logarithm of the sample variance. Recently a new method to solve the integral equation for Erlang distribution was published. Here we consider the steps to apply the solution to the problem of controling process variance.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 2006년도 춘계학술대회
• /
• pp.135-141
• /
• 2006

Cusum control chart is used for the purpose of controling the process mean. We consider the problem related to cusum chart for controling process variance. Previous researches have considered the same problem. The main difficulty shown in the related researches was to derive the ARL function which characterizes the properties of the chart. Sample variance, differently with sample mean, follows chi-squared type distribution, even when the quality characteristics are assumed to be normally distributed. The ARL function of cusum is described by a type of integral equation. Since the solution of the integral equation for non-normal distribution is not known well, people used simulation method instead of solving the integral equation directly, or approximation method by taking logarithm of the sample variance. Recently a new method to solve the integral equation for Erlang distribution was published. Here we consider the steps to apply the solution to the problem of controling process variance.

• Journal of the Korean Data and Information Science Society
• /
• 제17권3호
• /
• pp.973-982
• /
• 2006

Performances of variable sampling interval(VSI) multivariate control charts with accumulate-combine approach for monitoring mean vector of p related quality variables were investigated. Shewhart control chart is also proposed to compare the performances of CUSUM and EWMA charts. Numerical comparisons show that performances of CUSUM and EWMA charts are more efficient than Shewhart chart for small or moderate shifts, and VSI chart is more efficient than fixed sampling interval(FSI) chart. We also found that performances of the CUSUM or EWMA chart with accumulate-combine approach are substantially efficient than those of Shewhart chart.

• 한국학교보건학회지
• /
• 제22권1호
• /
• pp.61-65
• /
• 2009

AIMS: Korean National Growth Chart was newly developed at 2007. Prevalence of obesity is expected to be changed according to reference data. This study was performed to compare the prevalence of obesity based on the newly developed growth curve with that of previous data. We also investigate the incidence of obesity, overweight, and normal criteria by two kinds of growth curves. Method: We obtained weight, height from 438 middle school students and 1786 high school students. We divided enrolled students into four groups (middle school boys, middle school girls, high school boys, high school girls). The obese, over weight group was defined as BMI(body mass index) more than 95th, 85th${\sim}$94th percentile respectively for age and sex. Obesity by relative weight was defined more than 20% of ideal body weight. We compare the prevalence of obesity in each group by the newly developed Korean National Growth Chart in 2007 and that in 1998. Result: Prevalence of obesity by BMI based on 2007 Korean National Growth Chart and that in 1998 were 8.2-12.9% and 9.0-20.4% respectively. The concordance rate were 99.0%-100% in obese group and 45.0%-91.9% in overweight group according to two kinds of reference data. Prevalence of obesity by relative weight was 11.7-23.0% that was same between 2007 Korean National Growth Chart and that in 1998 except middle school boys group. Conclusion: We found that there is a decrease in prevalence of Obesity by BMI based on 2007 Korean National Growth Chart. It is necessary to evaluate correlation between criteria for obesity and obesity related comorbid conditions for finding proper BMI or relative weight cut off value to prevent obesity and obesity related complications effectively.

• Journal of the Korean Data and Information Science Society
• /
• 제15권4호
• /
• pp.1003-1010
• /
• 2004

Properties of the variable sampling interval(VSI) CUSUM chart for monitoring dispersion matrix of related quality characteristics are investigated. Performances of the proposed charts are evaluated for matched fixed sampling interval(FSI) and VSI charts in terms of average time to signal(ATS) and average number of samples to signal (ANSS). Average number of swiches(ANSW) of the proposed VSI Shewhart and CUSUM charts are also investigated.

• 대한기계학회논문집A
• /
• 제32권2호
• /
• pp.194-201
• /
• 2008

In the present study, fuel system matching was analyzed, and a characteristic chart for common use for design-related parts is presented. Based on the characteristic chart thus presented, controlled fuel system matching was tested for a 35-liter fuel system, and actual mass product movement coils were applied to validate the test. The keynote of the present research is the use of the characteristic chart to devise a preferred fuel system matching method. Through the present study, it will be possible to design standard parts for efficient fuel system matching in the near future.

• 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.