• Journal of the Korean Society of Industry Convergence
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• v.12 no.1
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• pp.35-42
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• 2009

In Taguchi's quadratic expected loss function used as robustness metric of performance characteristics, the mean and variance contributions are confounded. The consolidation of the mean and variance in the expected loss function may not always be the ideal approach. This paper presents a procedure for multi-attributes design optimization, where the mean and variance of performance characteristics are considered as separate attributes having designer's relative preferences for them and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) is introduced to attain robust optimal design. The effectiveness of proposed approach is shown with an example of a weld line minimization problem in the injection molding process.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.575-589
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• 2010

Exponential L$\acute{e}$evy models have become popular in modeling price processes recently in mathematical finance. Although it is a relatively simple extension of the geometric Brownian motion, it makes the market incomplete so that the option price is not uniquely determined. As a trial to find an appropriate price for an option, we suppose a situation where a hedger wants to initially invest as little as possible, but wants to have the expected squared loss at the end not exceeding a certain constant. For this, we assume that the underlying price process follows a variance-gamma model and it converges to a geometric Brownian motion as its quadratic variation converges to a constant. In the limit, we use the mean-variance approach to find the asymptotic minimum investment with the expected squared loss bounded. Some numerical results are also provided.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.291-300
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• 2006

In this research, the performance of widely used Bayesian estimators such as Bayes estimator, empirical Bayes estimator, constrained Bayes estimator and constrained empirical Bayes estimator are compared by means of a measurement under balanced loss function for the typical normal-normal situation. The proposed measurement is a weighted sum of the precisions of first and second moments. As a result, one can gets the criterion according to the size of prior variance against the population variance.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.371-382
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• 2005

The paper compares the performance of some widely used Bayesian estimators such as Bayes estimator, empirical Bayes estimator, constrained Bayes estimator and constrained Bayes estimator by means of a new measurement under squared error loss function for the typical normal-normal situation. The proposed measurement is a weighted sum of the precisions of first and second moments. As a result, one can gets the criterion according to the size of prior variance against the population variance.

• IE interfaces
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• v.7 no.1
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• pp.75-80
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• 1994

In a recent article, Leon et al. lucidly explained the ideas of the Taguchi two-stage procedure for parameter design optimization, and proposed alternative performance measures called PerMIA to the signal-to-noise ratios. On the other hand, Box proposed an empirical approach to the problem based upon monotone transformations of the performance characteristic(y). This paper develops procedures for parameter design optimization under the assumptions that the expected loss(not necessarily a mean squared error loss) is increasing with respect to the variance of the error in y, and that the mean of y satisfies certain conditions of adjustability. It turns out that the variance of the error in y can play the role of PerMIA, and it is further shown that the derived PerMIA can be adapted to the Box empirical procedure for the minimization of the expected loss in the original metric.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.165-172
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• 2017

Machines are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. Under the process mean shift, production cost, failure cost and quality loss function cost are increasing continuously. Therefore a periodic preventive resetting the process is necessary. We suppose that the wear level is observable. In this case, process mean shift problem has similar characteristics to the maintenance policy model. In the previous studies, process mean shift problem has been studied in several fields such as 'Tool wear limit', 'Canning Process' and 'Quality Loss Function' separately or partially integrated form. This paper proposes an integrated cost model which involves production cost by the material, failure cost by the nonconforming items, quality loss function cost by the deviation between the quality characteristics from the target value and resetting the process cost. We expand this process mean shift problem a little more by dealing the process variance as a function, not a constant value. We suggested a multiplier function model to the process variance according to the analysis result with practical data. We adopted two-side specification to our model. The initial process mean is generally set somewhat above the lower specification. The objective function is total integrated costs per unit wear and independent variables are wear limit and initial setting process mean. The optimum is derived from numerical analysis because the integral form of the objective function is not possible. A numerical example is presented.

• 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 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.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1376-1384
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• 2019

The cosmetic and behavioral aspects of aging have become increasingly evident over the years. Physical aging in people can easily be observed on their face, posture, voice, and gait. In contrast, bone aging only becomes apparent once significant bone degeneration manifests through degenerative bone diseases. Therefore, a more accurate and timely assessment of bone aging is needed so that the determinants and its mechanisms can be more effectively identified and ultimately optimized. This study proposed a deep learning approach to assess the bone age of an adult using whole-body bone scintigraphy. The proposed approach uses multiple inputs deep neural network architectures using a loss function, called mean-variance loss. The data set was collected from Chonnam National University Hwasun Hospital. The experiment results show the effectiveness of the proposed method with a mean absolute error of 3.40 years.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.110-117
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• 2018

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. By the result of degeneration, non-conforming products and malfunction of machine occur. Therefore a periodic preventive resetting the process is necessary. This type of preventive action is called 'preventive maintenance policy.' Preventive maintenance presupposes that the preventive (resetting the process) cost is smaller than the cost of failure caused by the malfunction of machine. The process mean shift problem is a field of preventive maintenance. This field deals the interrelationship between the quality cost and the process resetting cost before machine breaks down. Quality cost is the sum of the non-conforming item cost and quality loss cost. Quality loss cost is due to the deviation between the quality characteristics from the target value. Under the process mean shift, the quality cost is increasing continuously whereas the process resetting cost is constant value. The objective function is total costs per unit wear, the decision variables are the wear limit (resetting period) and the initial process mean. Comparing the previous studies, we set the process variance as an increasing concave function and set the quality loss function as Cpm+ simultaneously. In the Cpm+, loss function has different cost coefficients according to the direction of the quality characteristics from target value. A numerical example is presented.