• 산업경영시스템학회지
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• 제34권2호
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• pp.30-34
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• 2011

Variable precision rough set models have been successfully applied to problems whose domains are discrete values. However, there are many situations where discrete data is not available. When it comes to the problems with interval values, no variable precision rough set model has been proposed. In this paper, we propose a variable precision rough set model for interval values in which classification errors are allowed in determining if two intervals are same. To build the model, we define equivalence class, upper approximation, lower approximation, and boundary region. Then, we check if each of 11 characteristics on approximation that works in Pawlak's rough set model is valid for the proposed model or not.

• 한국기계가공학회지
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• 제10권5호
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• pp.117-124
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• 2011

Tire's performance plays important roles in improving vehicle's performances. Tire makers carry out a lot of research to improve tire's performance. They are making effort to meet multi purposes using various optimization methods. Recently, the tire makers perform the shape optimization using approximation models, which are surrogate models obtained by statistical method. Generally, the reason why we increase sampling points during optimization process, is to get more reliable approximation models, but the more we adopt sampling points, the more we need time to test. So it is important to select approximation model and proper number of sampling points to balance between reliability and time consuming. In this research, we studied to compare two kind cases for a approximation construction. First, we compare RSM and Kriging which are Curve Fitting Method and Interpolation Method, respectively. Second, we construct approximation models using three different number of sampling points. And then, we recommend proper approximation model and orthogonal array adopt tire's design optimization.

• International Journal of Reliability and Applications
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• 제13권2호
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• pp.71-80
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• 2012

This paper introduces a new approach for evaluating reliability of a complex system in terms of distributional parameters where analytical determination of reliability is intractable. The concept of discrete approximation, reported in the literature so far, fails to meet the latter requirement in terms of distributional parameters. The current work aims at offering a bound based approach where reliability planners not only get a clear idea about the extent of error but also can manipulate in terms of distributional parameters. This reliability approximation has been under taken under the Weibull frame work which is the most widely used model for reliability analysis. Numerical study has been carried out to examine the strength of our proposed reliability approximation via closeness between the two reliability bounds. This approach will be very useful during the early stages of product design as the distributional parameters can be adjusted.

• 대한전자공학회논문지
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• 제24권4호
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• pp.583-589
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• 1987

A new approximation method is proposed for the linear model reduction of high order dynamic systems. This mehtod is based upon the denominator table(D-table) and time moment-matching technique. The denominator table(D-table) is used to obtain the denominator polynomial of reduced-order model, and the numerator polynomial is obtained by time moment-matching method. This proposed method does not require the calculation of the alpha-beta expansion and reciprocal transformation which should be calculadted by Routh approximation method. The advantages of the proposed method are that it is computationally every attractive better than Routh approximation method and the reduced model is stable Il the original system is stable.

• 한국국방경영분석학회지
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• 제18권2호
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• pp.96-113
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• 1992

A time-varying nonhomogeneous poisson process approximation of the nonexponential stochastic Lanchester model is defined and evaluated over a range of combat parameters including initial force sizes. breakpoints. and interkilling random variables. The proposed approximation is far excellent and takes much less CPU time than the existing models. The sensitivity analysis was peformed to evaluate the efficiency of the proposed model and three recommended factors are suggested to guide the combat operators.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2008년도 추계학술대회 및 정기총회
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• pp.65-75
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• 2008

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• 경영과학
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• 제26권3호
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• pp.117-131
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• 2009

Over the last three decades, there are many researches focusing on the practice and theory of RM in airlines. Most of them have dealt with a seat assignment problem for maximizing the total revenue. In this study, we focus on a seat assignment problem in airlines. The seat assignment problem can be modeled as a stochastic programming model which is difficulty to solve optimally. However, with some assumptions on the demand distribution functions and a linear approximation technique, we can transform the complex stochastic programming model to a Linear Programming model. Some computational experiments are performed to evaluate out model with randomly generated data. They show that our model has a good performance comparing to existing models, and can be considered as a basis for further studies on improving existing seat assignment models.

• 한국미생물·생명공학회지
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• 제27권3호
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• pp.223-229
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• 1999

The two optimal control algorithms, singular approximation and minimum principle, were compared in this paper. The switching time with singular approximation was determined with mathematical derivation and the optimal control profile of specific growth rate was also calculated with minimum principle. The optimal control profiles were calculated by making simple model correlating the specific cell growth rate and specific product formation rate. The optimal control profiles calculated by singular approximation approach were similar to stepwise form of those calculatd by minimum principles. With the minimum principle, the product concentration was 8% more than that of singular approximation. This performance difference was due to a linearization of a nonlinear function with singular approximation. This optimal approaches were applicable to any system with different optimal cell growth and product formation.

• Journal of the Korean Statistical Society
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• 제20권2호
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• pp.99-107
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• 1991

Behrens-Fisher problem is viewed from a model selection approach. Normal distribution is regarded as an approximating model, A criterion, called TIC, is derived and is compared with selection criteria such as AIC and a bootstrap estimator. Stochastic approximation is used since no closed form expression is available for the bootstrap estimator.

• 대한전기학회논문지
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• 제36권8호
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• pp.584-593
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• 1987

A new combined approximation method using Routh stability array and mean-square error (MSE) method is proposed for deriving reduced-order z-transter functions for discrete time systems. The Routh stability array is used to obtain the reduced-order denominator polynomial, and the numerator polynomial is obtained by minimizing the mean-square error between the unit step responses of the original system and reduced model. The advantages of the new combined approximation method are that the reduced model is always stable provided the original model is stable and the initial and steady-state characteristics of the original model can be preserved in the reduced model.