• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.95-100
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• 2020

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. The representative type of the degeneration is wear of tool or machine. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore a periodic preventive resetting the process is necessary. The total cost consists of three items: adjustment cost (or replacement cost), non-conforming cost due to product out of upper or lower limit specification, and quality loss cost due to difference from the process target value and the product characteristic value among the conforming products. In this case, the problem of determining the adjustment period or wear limit that minimizes the total cost is called the 'process mean shift' problem. It is assumed that both specifications are set and the wear level can be observed directly. In this study, we propose a new model integrating the quality loss cost, process variance, and production volume, which has been conducted in different fields in previous studies. In particular, for the change in production volume according to the increasing in wear level, we propose a generalized production quantity function g(w). This function can be applied to most processes and we fitted the g(w) to the model. The objective equation of this model is the total cost per unit wear, and the determining variables are the wear limit and initial process setting position that minimize the objective equation.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.111-117
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• 1997

The ordinary least squares estimator $S^2$ for the variance of the disturbances is considered in the linear regression model with sutocorrelated disturbances. It is proved that the OLS-estimator of disturbance variance is asymptotically unbiased and weakly consistent, when the distrubances are generated by an MA(q) process. In particular, the asymptotic unbiasedness and consistency of $S^2$ is satisfied without any restriction on the regressor matrix.

• East Asian mathematical journal
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• v.34 no.3
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• pp.239-248
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• 2018

In this paper, we derive a closed-form formula of a second-order approximation for a European corrected option price under stochastic elasticity of variance model mentioned in Kim et al. (2014) [1] [J.-H. Kim, J Lee, S.-P. Zhu, S.-H. Yu, A multiscale correction to the Black-Scholes formula, Appl. Stoch. Model. Bus. 30 (2014)]. To find the explicit-form correction to the option price, we use Mellin transform approaches.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.281-292
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• 2006

The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.145-152
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• 2019

All machines deteriorate in performance over time. The phenomenon that causes such performance degradation is called deterioration. Due to the deterioration, the process mean of the machine shifts, process variance increases due to the expansion of separate interval, and the failure rate of the machine increases. The maintenance model is a matter of determining the timing of preventive maintenance that minimizes the total cost per wear between the relation to the increasing production cost and the decreasing maintenance cost. The essential requirement of this model is that the preventive maintenance cost is less than the failure maintenance cost. In the process mean shift model, determining the resetting timing due to increasing production costs is the same as the maintenance model. In determining the timing of machine adjustments, there are two differences between the models. First, the process mean shift model excludes failure from the model. This model is limited to the period during the operation of the machine. Second, in the maintenance model, the production cost is set as a general function of the operating time. But in the process mean shift model, the production cost is set as a probability functions associated with the product. In the production system, the maintenance cost of the equipment and the production cost due to the non-confirming items and the quality loss cost are always occurring simultaneously. So it is reasonable that the failure and process mean shift should be dealt with at the same time in determining the maintenance time. This study proposes a model that integrates both of them. In order to reflect the actual production system more accurately, this integrated model includes the items of process variance function and the loss function according to wear level.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.114-125
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• 2003

This paper proposes multivariate process capability indices (PCIs) for skewed populations using $T^2$rand modified process region approaches. The proposed methods are based on the multivariate version of a weighted standard deviation method which adjusts the variance-covariance matrix of quality characteristics and approximates the probability density function using several multivariate Journal distributions with the adjusted variance-covariance matrix. Performance of the proposed PCIs is investigated using Monte Carlo simulation, and finite sample properties of the estimators are studied by means of relative bias and mean square error.

• Journal of Korean Society for Quality Management
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• v.35 no.2
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• pp.106-112
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• 2007

In statistical process control, it is assumed that the process data are independent. However, most of chemical processes such as semi-conduct processes do not satisfy the assumption because of presence of autocorrelation between process data. It causes abnormal out of control signal in the process control and misleading estimation in process capability. In this study, we adopted Shore's method to solve the problem and propose an optimal subgroup size to estimate the variance correctly for AR(1) processes. Especially, we focus on finding an actual subgroup size for small samples based on simulation study.

• Journal of the Korea Safety Management & Science
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• v.11 no.2
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• pp.117-126
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• 2009

We develop methods for propagating and analyzing EPCI(Extended Process Capability Index) by using the error type that classifies into accuracy and precision. EPCI developed in this study can be applied to the three combined processes that consist of production, measurement and calibration. Little calibration work discusses while a great deal has been studied about SPC(Statistical Process Contol) and MSA(Measurement System Analysis). EPCI can be decomposed into three indexes such as PPCI(Production Process Capability Index), PPPI(Production Process Performance Index), MPCI(Measurement PCD, and CPCI(Calibration PCI). These indexs based on the type of error classification can be used with various statistical techniques and principles such as SPC control charts, ANOVA(Analysis of Variance), MSA Gage R&R, Additivity-of-Variance, and RSSM(Root Sum of Square Method). As the method proposed is simple, any engineer in charge of SPC. MSA and calibration can use efficientily in industries. Numerical examples are presentsed. We recommed that the indexes can be used in conjunction with evaluation criteria.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.95-104
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• 2005

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation which may amplify the variance of response. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameter setting, a new desirability function is proposed by considering POE as well as distance-to-target of response and response variance. The proposed method is illustrated using a rubber product case in Ribeiro et al. (2000).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.39-44
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• 2004

A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation as well as distance-to-target of response and response variance. The variation of process parameters amplifies the variance of responses. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameters, this variability should be considered in the optimization problem. The proposed method is illustrated using a rubber product case.