• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.161-166
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• 2003

In this paper, a new form of generalized linear models is proposed. The proposed models consist of a distribution function of the mean response and a weighted linear combination of distribution functions of covariates. This form addresses a structural problem of the link function in the generalized linear models. Markov chain Monte Carlo methods are used to estimate the parameters within a Bayesian framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1377-1383
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• 2008

Parameterization of a 3D triangular mesh is a fundamental problem in various applications of geometric modeling and computer graphics. There are two major paradigms in mesh parameterization: energy functional minimization and the convex combination approach. In general, the convex combination approach is wifely used because of simple concept and one-to-one mapping. However, the approach has some problems such as high distortion near the boundary and time complexity. Moreover, the stability of the linear system may not be preserved according to the geometric information of the mesh. In this paper, we present an extension of the convex combination approach based on the mean value coordinates, which resolves the drawbacks of the convex combination approach. This may be a more practical solution because it is able to generate a stable linear system in a short time.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1037-1040
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• 2000

We introduce the forecasting method for a next day electric peak load that uses the optimal combination of two types of neural networks. First network uses learning data that are past 10days of the target day. We name the neural network Short Term Neural Network (STNN). Second network uses those of last year. We name the neural network Long Term Neural Network (LTNN). Then we get the forecasting results that are the linear combination of the forecasting results by STNN and the forecasting results by LTNN. We name the method Combination Forecasting Method (CFM). Then we discuss the optimal combination of STNN and LTNN. Using CFM of the optimal combination of STNN and LTNN, we can reduce the forecasting error.

• Kyungpook Mathematical Journal
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• v.18 no.1
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• pp.31-36
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• 1978

This paper studies some problems suggested by Stirling numbers, and defines generalized Stirling numbers s(n, k, r), S(n, k, r) and proves that generalized Stirling numbers and certain linear combination of generalized Stirling numbers are strong logarithmic concave functions of k for fixed n and r.

• ETRI Journal
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• v.37 no.1
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• pp.165-174
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• 2015

In this paper, we focus on a novel technique called the cube-linear attack, which is formed by combining cube attacks with linear attacks. It is designed to recover the secret information in a probabilistic polynomial and can reduce the data complexity required for a successful attack in specific circumstances. In addition to the different combination strategies of the two attacks, two cube-linear schemes are discussed. Applying our method of a cube-linear attack to a reduced-round Trivium, as an example, we get better linear cryptanalysis results. More importantly, we believe that the improved linear cryptanalysis technique introduced in this paper can be extended to other ciphers.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.787-800
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• 2000

In this paper we consider an optimal control system described by n-dimensional heat equation with a thermal source. Thus problem is to find an optimal control which puts the system in a finite time T, into a stationary regime and to minimize a general objective function. Here we assume there is no constraints on control. This problem is reduced to a moment problem. We modify the moment problem into one consisting of the minimization of a positive linear functional over a set of Radon measures and we show that there is an optimal measure corresponding to the optimal control. The above optimal measure approximated by a finite combination of atomic measures. This construction gives rise to a finite dimensional linear programming problem, where its solution can be used to determine the optimal combination of atomic measures. Then by using the solution of the above linear programming problem we find a piecewise-constant optimal control function which is an approximate control for the original optimal control problem. Finally we obtain piecewise-constant optimal control for two examples of heat equations with a thermal source in one-dimensional.

• Proceedings of the Acoustical Society of Korea Conference
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• 1985.10a
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• pp.57-58
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• 1985

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.361-370
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• 2003

Those who are interested in making inferences concerning linear combination of valiance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods. The methods are applied to a numerical example and recommendations are given for choosing a proper interval.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.651-658
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• 2001

The projection matrix plays an important role in the linear model theory. In this paper we derive an algebraic relationship between the projection matrices of submatrices of the design matrix. Using this relationship we can easily obtain the projection matrices of any submatrices of the design matrix. Also we show that every projection matrix can be obtained as a linear combination of Kronecker products of identity matrices and matrices with all elements equal to 1.

• Magazine of the Korean Society of Agricultural Engineers
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• v.28 no.2
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• pp.42-52
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• 1986

This study involves the estimation of optimal ranges of parameters for a linear watershed model. A well-known TANK model was chosen and a linear combination of four tanks assumed. The model was used to simulate daily streamflow for six watersheds of different sizes and by a trial-and-error approach a set of optimal parameters defined. The parameters were related to watershed sizes and land use conditions. Optimal parameters for ungaged conditions were defined from the relationships; daily streamflow simulated and compared to the observed date. The simulated results were in a general agreement with the data.