• The Korean Journal of Applied Statistics
• /
• v.27 no.7
• /
• pp.1269-1278
• /
• 2014

Despite the D-optimality criterion becomes very popular in designing an experiment for nonlinear models because of theoretical foundations it provides, it is very critical that the criterion depends on the unknown parameters of the nonlinear model. But some nonlinear models turned out to be partially nonlinear in sense that the optimal design depends on the subset of parameters only. It was a strong belief that the maximin approach to find a robust design to protect against the uncertainty of parameters is not guaranteed to be successful in nonlinear models. But the maximin approach could be a success for the partial nonlinear model, because often the optimal design depends on only one unknown value of parameter, easier to handle than the full parameters. We deal with maximin approach for Michaelis-Menten model with respect to D- and $D_s$-optimality.

• The Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.561-571
• /
• 2015

When we consider an optimal design to predict the response corresponding to the point outside the design region, we are extremely careful about choosing the design criteria for selecting the support points. The assumed model and its accompanying error structure should be assumed to extend beyond the design region for the selected design criteria to be valid. Thus, we modify the existing design criteria such as extrapolation-optimality to be suited to those situations. We propose some maximin approaches in this paper. Simple and quadratic regression models are tested to find the basic characteristics of such maximin approaches. Some main findings are discussed in the conclusion.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.4
• /
• pp.369-374
• /
• 2015

In order to efficiently optimize the problem involving complex computer codes or computationally expensive simulation, surrogate models are widely used. Because their accuracy significantly depends on sample points, many experimental designs have been proposed. One approach is the sequential design of experiments that consider existing information of responses. In earlier research, the correlation coefficients of the kriging surrogate model are introduced as weight parameters to define the scaled distance between sample points. However, if existing information is incorrect or lacking, new sample points can be misleading. Thus, our goal in this paper is to propose a weight function derived from correlation coefficients to generate new points robustly. To verify the performance of the proposed method, several existing sequential design methods are compared for use as mathematical examples.

• Journal of Korea Water Resources Association
• /
• v.46 no.9
• /
• pp.897-907
• /
• 2013

Climate change is expected to worsen the depletion of streamflow in urban watershed. In this study, we therefore considered the treated wastewater (TWW) use as an adaptation strategy and devised a framework to identify prioritized areas for TWW use. An integrated framework that includes hydrological factors as well as social and environmental components were employed to determine the criteria for decision making. Fuzzy theory was employed to consider the uncertainties in the climate change scenarios and the weights of the performance value. All alternatives were evaluated using the fuzzy TOPSIS method. In addition, statistical method and decision making methods under complete uncertainty were used for robust decision making. As a result, ranking the alternatives using the fuzzy TOPSIS method and robust approach such as maximin, maximax, Hurwicz and equal likelihood criterion mitigated the level of uncertainty and ambiguity in each alternative. The finding of this study can be helpful in prioritizing water resource management projects considering various climate change scenarios.

• Environmental and Resource Economics Review
• /
• v.13 no.2
• /
• pp.245-269
• /
• 2004

While the role of local community is getting important in reducing greenhouse gases, current financial supports have not considered these efforts. Instead, development activities have been the only basis of fiscal distribution, which have led to unsustainable development. In this context, this paper analyzes the emission and absorb of air pollutants by local districts in Daegu Metropolitan city and constructs an empirical energy database for local energy use and environmental emissions. Based on the data, it prioritized environmental contribution by region through using MCDM methods, that include maximin & maximax method, simple additive weighting (SAW) and hierarchical additive weighting method (HAW). This concludes the possibility of policy methodology through which we can input environmental variables in distributing local budget.

• Journal of the military operations research society of Korea
• /
• v.33 no.1
• /
• pp.59-74
• /
• 2007

Computer simulation models are in general quite complex and time-consuming to run, and therefore, a simpler meta-model is usually constructed for further analysis. In this paper, JANUS, a war-game simulator, is used to describe a certain tank combat situation. Then, second-order response surface and artificial neural network meta-models are developed using the data from eight different experimental designs. Relative performances of the developed meta-models are compared in terms of the mean squared error of prediction. Computational results indicate that, for the given problem, the second-order response surface meta-model generally performs better than the neural network, and the orthogonal array-based Latin hypercube design(LHD) or LHD using maximin distance criterion may be recommended.

• The Korean Journal of Applied Statistics
• /
• v.27 no.5
• /
• pp.693-703
• /
• 2014

When setting up an experiment for extrapolation at multiple points outside the design space, we often face a difficulty in which point we should emphasize even if the polynomial model under consideration is given. In this paper we propose various methods under two possible scenarios that deal with extrapolations. One considered in this paper is the situation when the model assumed can be extended beyond the design space. In this setting, the many classical methods(including various approaches the authors proposed before) were revisited in the context of extrapolation. But the real problem arises when there is an uncertainty concerning the validity of the assumed model. Therefore, the second scenario is to develop an appropriate procedure when we have limited information about model. Consequently, a hybrid approach is suggested to deal with this issue of how to handle the multiple extrapolating under model uncertainty. A search algorithm was implemented because the classical exchange algorithm was found difficult to handle the complexity of the problem.

• Journal of the Society of Disaster Information
• /
• v.8 no.4
• /
• pp.419-431
• /
• 2012

This paper takes precautions proposals against prospective disasters from the space weather maximum in 2013. A geomagnetic space storm sparked by a solar maximum like the one that flared toward earth is bound to strike again and could wreak havoc across the modern world. The purpose of the study is that the disaster reduction and safety service implementation study on the ultimate space weather systems by the information systems of the space weather. The process methods of the study are that an implementation of preparation for the smart IT and GIS based disaster management systems of the solar maximum deal with analysis on the flare, solar proton event, and geomagnetic storm from space blasters, These approach and methods for the solar maximin display national policy implementation of the pattern of the radio wave disasters from the protection and preparation methods. This research can provide affective methods for the saving lives and property protections that implementation of the disaster prediction and disaster prevention systems adapts the smart IT systems and converged decision making support systems using uGIS methodology.