• Korean Management Science Review
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• v.28 no.1
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• pp.141-158
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• 2011

This study concerns with an applicability of the management science approach to the score adjustment among the College Scholastic Aptitude Test(CSAT) optional subjects. A linear programming model is developed to minimize the sum of score distortions between optional subjects. Based on the analysis of the 377,089 CSAT(2010) applicants' performances in social science test section, this study proposes a new approach for the score equating or linking method of the educational measurement theory. This study makes up for the weak points in the previous linear programming model. First, the model utilize the standard score which we can get. Second, the model includes a goal programming concept which minimizes the gap between the adjusting goal and the result of the adjustment. Third, the objective function of the linear programing is the weighted sum of the score distortion and the number of applicants. Fourth, the model is applied to the score adjustment problem for the whole 11 optional subjects of the social science test section. The suggested linear programming model is a generalization of the multi-tests linking problem. So, the approach is consistent with the measurement theory for the two tests and can be applied to the optional three or more tests which do not have a common anchor test or a common anchor group. The college admission decision with CSAT score can be improved by using the suggested linear programming model.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.400-402
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• 2000

This paper presents a game theoretic approach for power transactions analysis in a competitive market. The considered competitive power market is regarded as PooICo model, and the participating players are restricted by only two generating entities for simplicity in this paper. The analysis is performed on the basis of marginal cost based relations of bidding price and bidding generations. That is, we assume that the bidding price of each player is determined by the marginal cost when the bidding generation is pre-determined. This paper models the power transaction as a two player game and analyzes by applying the Nash eauilibrium idea. The generalized game model for power transactions covering constant-sum(especially zero-sum), and nonconstant-sum game is developed in this paper. Also, the analysis for each game model are performed in the case studies. Here, we have defined the payoff of each player as the weighted sum of both player's profits.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1344-1346
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• 1999

This paper presents a game theoretic approach for power transactions analysis in a competitive market. The considered competitive power market is regarded as PoolCO model, and the participating players are restricted by only two generating entities for simplicity in this paper. The analysis is performed on the basis of marginal cost based relations of bidding price and bidding generations. That is, we assume that the bidding price of each player is determined by the marginal cost when the bidding generation is pre-determined. This paper models the power transaction as a two player game and analyzes by applying the Nash eauilibrium idea. The generalized game model for power transactions covering constant-sum(especially zero-sum), and nonconstant-sum game is developed in this paper. Also, the analysis for each game model are Performed in the case studies. Here, we have defined the payoff of each player as the weighted sum of both player's profits.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.251-268
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• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.5
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• pp.812-820
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• 1995

This paper proposes an approach of identifying nonlinear mappings from input/output data. The approach is based on the universal approximation by the fuzzy integration of local affine mappings. A connectionist model realizing the universal approximator is suggested by using a processing unit based on both the radial basis function and the weighted sum scheme. In addition, a learning method with self-organizing capability is proposed for the identifying of nonlinear mapping relationships with the given input/output data. To show the effectiveness of our approach, the proposed model is applied to the function approximation and the prediction of Mackey-Glass chaotic time series, and the performances are compared with other approaches.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.936-938
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• 1996

The $H_{\infty}$ robust controller is designed for on-line adaptive control application by using polynomial approach. The $H_{\infty}$ robust controllers for adaptive system were designed first by Grimble. But they have a problem that two minimum costs can exist and did not minimize the conventional $H_{\infty}$ cost function which is the $H_{\infty}$ sum of weighted sensitivity and complementary sensitivity terms. In this paper, the two minimum costs problem can be avoided and the conventional $H_{\infty}$ cost function is minimized by employing the Youla parameterization and polynomial approach at the same time. In addition pole placement is possible without any relation with weighting function.

• The KSFM Journal of Fluid Machinery
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• v.7 no.2 s.23
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• pp.7-13
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• 2004

In an attempt to solve multiobjective optimization problems, weighted sum method is most widely used for the advantage that a designer can consider the relative significance of each object functions by weight values but it can be highly sensitive to weight vector and occasionally yield a deviated optimum from the relative weighting values designer designated because the multiobjective function has the form of simple sum of the product of the weighting values and the object functions in traditional approach. To search the design solution agree well to the designer's weighting values, we proposed new multiobjective function which was the functional of each normalized objective functions and considered to find the design solution comparing the distance between the characteristic line and the ideal optimum. In this study, proposed multiobjective function was applied to design high efficiency and low noise axial flow fan and the result shows this approach is effective for the case that the quality of the design can be highly affected by the designer's subjectiveness represented as weighting values in multiobjective design optimization process.

• 유체기계공업학회:학술대회논문집
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• 2003.12a
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• pp.397-403
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• 2003

In an attempt to solve multiobjective optimization problems, weighted sum method is most widely used for the advantage that a designer can consider the relative significance of each object functions by weight values but it can be highly sensitive to weight vector and occasionally yield a deviated optimum from the relative weighting values designer designated because the multiobjective function has the form of simple sum of the product of the weighting values and the object functions in traditional approach. To search the design solution well agree to the designer's weighting values, we proposed new multiobjective function which is the functional of each normalized objective functions and considered to find the design solution comparing the distance between the characteristic line and the ideal optimum. In this study, proposed multiobjective function was applied to design high efficiency and low noise axial flow fan and the result shows this approach will be effective for the case that the qualify of the design can be highly affected by the designer's subjectiveness represented as weighting values in multiobjective design optimization process.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.537-537
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• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.