• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.97-105
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• 2015

Multiresponse optimization (MRO) seeks to find the setting of input variables, which optimizes the multiple responses simultaneously. The approach of weighted mean squared error (WMSE) minimization for MRO imposes a different weight on the squared bias and variance, which are the two components of the mean squared error (MSE). To date, a weighted sum-based method has been proposed for WMSE minimization. On the other hand, this method has a limitation in that it cannot find the most preferred solution located in a nonconvex region in objective function space. This paper proposes a Tchebycheff metric-based method to overcome the limitations of the weighted sum-based method.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.77-82
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• 2001

• 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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• 2002.05a
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• pp.755-761
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• 2002

It Is often required to choose the optimum operating conditions for several responses simultaneously. In solving this multiresponse problem, the correlation of several responses, quality of prediction and the robustness of each response variable are must be considered. This paper proposes a new loss function approach that allows to consider these three important aspects. A numerical example illustrates the proposed methodology

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.31-34
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• 2004

For solving multiresponse problems, a variety of loss function approaches have been proposed assuming that a cost matrix is known and fixed. However a cost matrix is also an important factor in loss function approaches, because the optimal solution is very sensitive to the cost matrix. In this paper. we propose a novel method for adjusting the cost matrix by considering the predictive ability of the estimated response models. Simulation results for the generated data set show that the proposed method is competitive with previously reported methods.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.2
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• pp.164-172
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• 2005

A loss function approach to a multiresponse problem is considered, when process parameters are regarded as random variables. The variation of each response may be amplified through so called propagation of error (POE), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. The forms of POE for each response and for a pair of responses are proposed and they are reflected in our loss function approach to determine the optimal condition. The proposed method is illustrated using a polymer case. The result is compared with the case where parameter fluctuation is not considered.

• Preventive Nutrition and Food Science
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• v.21 no.4
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• pp.355-360
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• 2016

The influence of extraction temperature, powder concentration, and extraction time on the antioxidant properties of aqueous ginger extract was investigated. The possibility of estimating the antioxidant properties of the extract from its absorbance and colour properties was also investigated. Results indicated that powder concentration was the most significant factor to consider in optimizing antioxidant extraction. However, temperature and time still influenced the 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging activity while extraction temperature influenced the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity of the extract. Using the total phenol content, total flavonoid content, ABTS radical scavenging activity, and DPPH radical scavenging activity of the extract, the multiresponse optimization condition for extraction of antioxidant based on the experimental range studied is $96^{\circ}C$, 2.10 g/100 mL, and 90 min. The absorbance of the ginger extract at 610 nm could be exploited for rapid estimation of its total flavonoid and polyphenol with a $R^2$ of 0.713 and 0.753, respectively.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.1-6
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• 2005

In this paper we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.433-448
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• 2002

Revelation is emphasized in exploratory data analysis. Hence, many graphical techniques are used in exploratory data analysis. Three applications of graphs in exploratory data analysis are presented.