• Journal of applied mathematics & informatics
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• v.41 no.4
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• pp.801-810
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• 2023

The aim of this paper, investigated of weighted space which contained the unbounded functions which is to be approximated by linear operators in terms some Well-known approximation tools such as the modulus of smoothness and K-functional. The characteristics of the identical theorem between modulus of smoothness and K-functional are consider. In addition to the establish the direct, converse and identical theorem by using some linear operators in terms modulus Ditzian-Totik.

• Journal of the Korean Mathematical Society
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• v.45 no.6
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• pp.1677-1703
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• 2008

In this paper, we introduce and study various locally convex vector topologies on the space of bounded linear operators between Banach spaces. We also apply these topologies to approximation properties.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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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.

• Korean Management Science Review
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• v.26 no.3
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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.

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.187-192
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• 2004

A method using two dimension Haar functions approximation for solving the problem of a partial differential equation and estimating the parameters of a non-linear distributed parameter system (DPS) is presented. The applications of orthogonal functions, including Haar functions, and their transforms have been given much attention in system control and communication engineering field since 1970's. The Haar functions set forms a complete set of orthogonal rectangular functions similar in several respects to the Walsh functions. The algorithm adopted in this paper is that of estimating the parameters of non-linear DPS by converting and transforming a partial differential equation into a simple algebraic equation. Two dimension Haar functions approximation method is introduced newly to represent and solve a partial differential equation. The proposed method is supported by numerical examples for demonstration the fast, convenient capabilities of the method.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.226-233
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• 2012

In this paper, we propose a 'linear approximation method' which is an accelerated BER (Bit Error Rate) test method for high speed interfaces, based on an analytical BER model. Both the conventional 'Q-factor estimation method' and 'linear approximation method' can predict a timing margin for $10^{-13}$ BER with an error of about 0.03UI. This linear approximation method is implemented on a hardware as an accelerated Built-In Self Test (BIST) with an internal BERT (BET Tester). While a direct measurement of a timing margin in a 3Gbps interface takes about 5.6 hours with $10^{-13}$ BER requirement and 95% confidence level, the accelerated BIST estimates a timing margin within 0.6 second without a considerable loss of accuracy. The test results show that the error between the estimated timing margin and the timing margin from an actual measurement using the internal BERT is less than 0.045UI.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.795-809
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• 2001

In this paper, we apply finite element Galerkin method to a single-ohase linear Stefan problem with a forcing term. We apply the extrapolated Crank-Nicolson method to construct the fully discrete approximation and we derive optimal error estimates in the temporal direction in $L^2$, $H^1$ spaces.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.16 no.1
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• pp.1-13
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• 2012

In this paper we present an a posteriori error estimator for the stabilized P1 nonconforming finite element method of the linear elasticity problem based on a nonsymmetric H(div)-conforming approximation of the stress tensor in the first-order Raviart-Thomas space. By combining the equilibrated residual method and the hypercircle method, it is shown that the error estimator gives a fully computable upper bound on the actual error. Numerical results are provided to confirm the theory and illustrate the effectiveness of our error estimator.

• East Asian mathematical journal
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• v.24 no.2
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• pp.169-176
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• 2008

The aim of this paper is to prove a common fixed point theorem in normed linear spaces for noncompatible, discontinuous mappings without assuming completeness of the space. We also give an application of our theorem to best approximation theory.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.105-122
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• 1995

Based on linear models, the inference about the true measurement x$_{f}$ and the optimal designs x (nx1) for the calibration experiments are considered via Baysian statistical decision analysis. The posterior distribution of x$_{f}$ given the observation y$_{f}$ (qxl) and the calibration experiment is obtained with normal priors for x$_{f}$ and for themodel parameters (.alpha., .betha.). This posterior distribution is not in the form of any known distributions, which leads to the use of a numerical integration or an approximation for the calculation of the overall expected loss. The general structure of the expected loss function is characterized in the form of a conjecture. A near-optimal design is obtained through the approximation nof the conditional covariance matrix of the joint distribution of (x$_{f}$ , y$_{f}$ $^{T}$ )$^{T}$ . Numerical results for the univariate case are given to demonstrate the conjecture and to evaluate the approximation.n.