• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.816-826
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• 2017

In this paper, a generalized discretization scheme is proposed that can derive general-order finite difference equations representing the joint probability density function of dynamic response of stochastic systems. The various order of finite difference equations are applied to solutions of the Fokker-Planck-Kolmogorov (FPK) equation. The finite difference equations derived by the proposed method can greatly increase accuracy even at the tail parts of the probability density function, giving accurate reliability estimations. Compared with exact solutions and finite element solutions, the generalized finite difference method showed increasing accuracy as the order increases. With the proposed method, it is allowed to use different orders and types (i.e. forward, central or backward) of discretization in the finite difference method to solve FPK and other partial differential equations in various engineering fields having requirements of accuracy or specific boundary conditions.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.4
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• pp.4232-4241
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• 1976

This study was carried out to clarify the stochastic characteristics of monthly rainfalls and to select a proper model for generating the sequential monthly rainfall amounts. The results abtained are as follows: 1. Log-Normal distribution function is the best fit theoretical distribution function to the empirical distribution of monthly rainfall amounts. 2. Seasonal and random components are found to exist in the time series of monthly rainfall amounts and non-stationarity is shown from the correlograms. 3. The Monte Carlo model shows a tendency to underestimate the mean values and standard deviations of monthly rainfall amounts. 4. The 1st order Markov model reproduces means, standard deviations, and coefficient of skewness with an error of ten percent or less. 5. A correlogram derived from the data generated by 1st order Markov model shows the charaterstics of historical data exactly. 6. It is concluded that the 1st order Markov model is superior to the Monte Carlo model in their reproducing ability of stochastic properties of monthly rainfall amounts.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.43-50
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• 2013

In this paper, the single-period inventory problem, what is called newsboy problem, has been revisited with two different conditions, uncertain supply and risk-averseness. Eeckhoudt et al. [5] investigated the effect of risk-averseness of a newsboy on the optimal order quantity in a stochastic demand setting. In contrary to Eeckhoudt et al. [5] this paper investigates the effect of risk-averseness in a stochastic supply setting. The findings from this investigation say that if ${\alpha}^*$ represents the optimal order quantity without risk-averseness then the risk-averse optimal order quantity can be greater than ${\alpha}^*$ and can be less than ${\alpha}^*$ as well.

• Journal of the Korean Data and Information Science Society
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• v.1
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• pp.78-78
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• 1990

• Bulletin of the Korean Mathematical Society
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• v.44 no.1
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• pp.125-130
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• 2007

We propose a simple and intuitive method to derive the exact convergence rate of global $L_{2}-norm$ error for strong numerical approximation of stochastic differential equations the result of which has been reported by Hofmann and $M{\"u}ller-Gronbach\;(2004)$. We conclude that any strong numerical scheme of order ${\gamma}\;>\;1/2$ has the same optimal convergence rate for this error. The method clearly reveals the structure of global $L_{2}-norm$ error and is similarly applicable for evaluating the convergence rate of global uniform approximations.

• Journal of the Korean Statistical Society
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• v.31 no.2
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• pp.251-259
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• 2002

Lagged regressor models with general stationary errors independent of the regressors are considered. The regressor process is unstable having characteristic roots on the unit circle. If the order of the lag matches the number of roots on the unit circle, the ordinary least squares estimator (OLSE) is asymptotically efficient in that it has the same limiting distribution as the generalized least squares estimator (GLSE) under the same normalization. This result extends the well-known result of Grenander and Rosenblatt (1957) for asymptotic efficiency of the OLSE in deterministic polynomial and/or trigonometric regressor models to a class of models with stochastic regressors.

• Communications of the Korean Mathematical Society
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• v.33 no.3
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• pp.985-999
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• 2018

This paper is devoted to the existence and uniqueness of $L^p$ (p > 1) solutions for general time interval multidimensional backward stochastic differential equations (BSDEs for short), where the generator g satisfies a ($p{\wedge}2$)-order weak monotonicity condition in y and a Lipschitz continuity condition in z, both non-uniformly in t. The corresponding stability theorem and comparison theorem are also proved.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.139-144
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• 1993

In this paper, a robust reduced order adaptive controller is proposed based on Internal Model Control(IMC) structure for stochastic linear stable systems. The concept of gain margin is utilized to make the adaptive IMC controller robust. We prove the stability of the proposed adaptive IMC system for stable plants under the assumption that upper bounds for system orders are known. Simulation results show that the proposed method has good performance and stability robustness.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.550-553
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• 1986

This paper presents a procedure for the time-scale separation and a design method for the sub-optimal composite regulator and Kalman filter of the large-scale discrete stochastic system with two time-scale properties. Provided that the fast sub-system is asymptotically stable, the reduced-order regulator and Kalman filter for the slow sub-system with dominant modes is designed as a sub-optimal regulator for the system.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.168-173
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• 1987

The optimal Input design problem for linear system Which have the common parameters in the system and noise transfer functions. Exploiting the assumed Model structure and deriving the information matrix structure in detail, D-optimal open-loop stochastic input can be realized as an ARMA process under the Input or output variance constraints. In spite of the reduced order, It Is necessary to develop an efficient algorithms for the optimation with respect to the .rho..