• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.395-409
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• 2014

Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.

• Transactions of Materials Processing
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• v.31 no.5
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• pp.267-272
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• 2022

A dual-phase structure refers to a material with two different phases of components or crystal structures. In this study, we analyze the stress distributions for harmonic and composite structured materials which are a kind of dual-phase structure materials. The finite element method (FEM) was used to progress compression test to analyze the strain distribution, and rather than constituted of a fully dense material, a dual-phase structure was designed to make a lightweight structure that has different shapes and volumes of vacancy in each case. As a result of each case, the dual-phase structured materials showed different stress distribution patterns and based on this, the cause was identified through the research.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.98-104
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• 2014

Locality-sensitive hashing techniques have been developed to efficiently handle nearest neighbor searches and similar pair identification problems for large volumes of high-dimensional data. This study proposes a locality-sensitive hashing method that can be applied to nearest neighbor search problems for data sets containing both numerical and categorical attributes. The proposed method makes use of dual hashing functions, where one function is dedicated to numerical attributes and the other to categorical attributes. The method consists of creating indexing structures for each of the dual hashing functions, gathering and combining the candidates sets, and thoroughly examining them to determine the nearest ones. The proposed method is examined for a few synthetic data sets, and results show that it improves performance in cases of large amounts of data with both numerical and categorical attributes.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.4
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• pp.374-378
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• 2014

PID control method usually has problems of overshoot and oscillation in high order control system, therefore, it is important to improve the control method so as to reduce the overshoot and oscillation. Based on MATLAB simulation, a permanent magnet (PM) DC servomotor control system is studied in this paper. The motor is modeled according to the universal motor theory, and with the help of the fourth order Ronge-Kutta method, its speed control is simulated and compared between two different dual closed-loops PWM control methods. This case study helps undergraduate students to better understand theories related to electrical engineering, such as electrical machinery, power electronics and control theory, as well as digital solution of state equations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.488-491
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• 2003

A motion compression method by min s-norm composite fuzzy relational equations (dual-MCF) is proposed, where a motion sequence is divided into intra-pictures (I-pictures) and predictive-pictures (P-pictures). The I-pictures and the P-pictures are compressed by using uniform coders and non-uniform coders, respectively. A design method of non-uniform coders is proposed to perform an efficient compression and reconstruction of the P-pictures, based on the dual overlap level of fuzzy sets and a fuzzy equalization. An experiment using 10 P-pictures confirms that the root means square errors of the proposed method is decreased to 82.9％ of that of the uniform coders, under the condition that the compression rate is 0.0055. An experiment of motion compression and reconstruction is also presented to confirm the effectiveness of the dual-MCF based on the non-uniform coders.

• Korean Management Science Review
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• v.11 no.3
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• pp.1-11
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• 1994

Recent advances in linear programming solution methodology have focused on interior point methods. This powerful new class of methods achieves significant reductions in computer time for large linear programs and solves problems significantly larger than previously possible. These methods can be examined from points of Fiacco and McCormick's barrier method, Lagrangian duality, Newton's method, and others. This study presents a primal-dual log barrier algorithm of interior point methods for linear programming. The primal-dual log barrier method is currently the most efficient and successful variant of interior point methods. This paper also addresses a Cholesky factorization method of symmetric positive definite matrices arising in interior point methods. A special structure of the matrices, called supernode, is exploited to use computational techniques such as direct addressing and loop-unrolling. Two dense matrix handling techniques are also presented to handle dense columns of the original matrix A. The two techniques may minimize storage requirement for factor matrix L and a smaller number of arithmetic operations in the matrix L computation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.1
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• pp.41-53
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• 2009

A primal-dual interior point method(IPM) not only is the most efficient method for a computational point of view but also has polynomial complexity. Most of polynomialtime interior point methods(IPMs) are based on the logarithmic barrier functions. Peng et al.([14, 15]) and Roos et al.([3]-[9]) proposed new variants of IPMs based on kernel functions which are called self-regular and eligible functions, respectively. In this paper we define a new kernel function and propose a new IPM based on this kernel function which has $O(n^{\frac{2}{3}}log\frac{n}{\epsilon})$ and $O(\sqrt{n}log\frac{n}{\epsilon})$ iteration bounds for large-update and small-update methods, respectively.

• Journal of Mechanical Science and Technology
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• v.15 no.12
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• pp.1794-1800
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• 2001

Mass conservation is a key issue for accurate streamline and stream surface visualization of flow fields. This paper complements an existing method (Feng et al. 1997) for CFD velocity fields defined at discrete locations in space that uses dual stream functions to generate streamlines and stream surfaces. Conditions for using the method have been examined and its limitations defined. A complete set of dual stream functions for all possible cases of the linear fields on which the method relies are presented. The results in this paper are important for developing new methods for mass conservative streamline visualization from CFD and using the existing method.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.2
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• pp.101-113
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• 2018

The dual singular function method(DSFM) is a numerical algorithm to get optimal solution including corner singularities for Poisson and Helmholtz equations. In this paper, we apply DSFM to solve heat equation which is a time dependent problem. Since the DSFM for heat equation is based on DSFM for Helmholtz equation, it also need to use Sherman-Morrison formula. This formula requires linear solver n + 1 times for elliptic problems on a domain including n reentrant corners. However, the DSFM for heat equation needs to pay only linear solver once per each time iteration to standard numerical method and perform optimal numerical accuracy for corner singularity problems. Because the Sherman-Morrison formula is rather complicated to apply computation, we introduce a simplified formula by reanalyzing the Sherman-Morrison method.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.981-1000
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• 2009

This paper considers asset liability management problems when their deterministic equivalent formulations are general nonlinear optimization problems. The presented approach uses a nonmonotone trust region strategy for solving a sequence of unconstrained subproblems parameterized by a scalar parameter. The objective function of each unconstrained subproblem is an augmented penalty-barrier function that involves both primal and dual variables. Each subproblem is solved approximately. The algorithm does not restrict a monotonic decrease of the objective function value at each iteration. If a trial step is not accepted, the algorithm performs a non monotone line search to find a new acceptable point instead of resolving the subproblem. We prove that the algorithm globally converges to a point satisfying the second-order necessary optimality conditions.