• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.165-167
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• 2001

This paper presents a study on successive approximation measurement of mechanical parameters for motor control system. At the first step of servo system installation, control system gain tuning is troublesome work. Recently, auto-tuning method of motion controller for motor drive system is based on parameter measurement and identification. On the case of first order mechanical system (mechanical parameters are modified by simple inertia and friction), it is necessary for good response to get the accurate measurement or identification of the mechanical parameters. In this paper, novel method applies the binary successive approximation measurement to the inertia and friction coefficient. Computer simulation and experiment for the proposed method will show verification of accuracy and usefulness.

• Structural Engineering and Mechanics
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• 제73권1호
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• pp.1-16
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• 2020

Finite elements based on the partition of unity (PU) approximation have powerful capabilities for p-adaptivity and solutions with high smoothness without remeshing of the domain. Recently, the PU approximation was successfully applied to the three-node shell finite element, properly eliminating transverse shear locking and showing excellent convergence properties and solution accuracy. However, the enrichment with the PU approximation results in a significant increase in the number of degrees of freedom; therefore, it requires greater computational cost, thus making it less suitable for practical engineering. To circumvent this disadvantage, we propose a new strategy to decrease the total number of degrees of freedom in the existing PU-based shell element, without loss of optimal convergence and accuracy. To alleviate the locking phenomenon, we use the method of mixed interpolation of tensorial components and perform convergence studies to show the accuracy and capability of the proposed shell element. The excellent performances of the new shell elements are illustrated in three benchmark problems.

• Journal of Applied and Pure Mathematics
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• 제5권1_2호
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• pp.69-93
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• 2023

Here we give multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or ℝ^N, N ∈ ℕ, by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We treat also the case of approximation by iterated operators of the last four types. These approximations are derived by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a parametrized Gudermannian sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network is with one hidden layer.

• Nonlinear Functional Analysis and Applications
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• 제28권4호
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• pp.1035-1049
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• 2023

Approximation of functions of Lipschitz and Zygmund classes have been considered by various researchers under different summability means. In the proposed study, we investigated an estimation of the order of convergence of a function associated with Hardy-Littlewood series in the weighted Zygmund class W(Z^(𝜔)_r)-class by applying Euler-Hausdorff summability means and subsequently established some (presumably new) results. Moreover, the results obtained here represent the generalization of several known results.

• 한국정보처리학회논문지
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• 제3권7호
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• pp.1753-1762
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• 1996

지형 정보 시스템에서 공간 객체수가 방대하여, 보조 기억 장치에 저장하는데 공간 객체의 엑세스를 빠르게 하기 위해서 공간 데이터베이스 시스템을 연구 하였다. 공간 객체들은 SAM으로 구성되었지만, 공간 다각형이 직접 SAM을 구성 할수 없다. 공간 다각형을 처리하기 위해 가장 대표적으로 MBR이 지형 키로서 공간 다각형 대신 사용한다. 질의 처리시 MBR은 빠르지만 부정확하다. 따라서, 공간 객체를 근사화하는 데 어떤 근사화 방법이 사용되느냐가 질의 처리시 성능에 영향을 미친다. 적절한 근사화 방법이 후보 집합을 줄일수 있다. 근사화의 질이 놓을수록 필요 없는 엑세스를 줄일수 있다. 본 논문에서는 Slice 분리라는 다중 용기를 이용한 근사화 방법을 제안하였고 다른 근사화 방법과 비교하였다.

• 정보처리학회논문지D
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• 제19D권2호
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• pp.151-160
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• 2012

지금까지 제안된 분산 고차원 색인의 대부분은 균일한 분포를 가지는 데이터 집합에서 좋은 검색 성능을 나타내나, 편향되거나 클러스터를 이루는 데이터의 집합에서는 그 성능이 크게 감소된다. 본 논문은 강하게 클러스터를 이루거나 편향된 분포를 가지는 데이터 집합에 대한 분산 벡터 근사 트리의 k-최근접 검색 성능을 향상시키는 방법을 제안한다. 기본 아이디어는 전체 데이터를 클러스터링하는 상위 트리의 말단 노드가 담당하는 데이터 공간의 크기를 계산하고, 그 공간 상의 특징 벡터를 근사하는 데 사용되는 비트의 수를 달리하여 벡터 근사의 식별 능력을 보장하는 것이다. 즉, 고밀도 클러스터에는 더 많은 수의 비트를 할당하는 것이다. 우리는 합성 데이터와 실세계 데이터를 가지고 분산 hybrid spill-tree와 기존 분산 벡터 근사 트리와의 성능 비교 실험을 수행하였다. 실험 결과는 확장된 분산 벡터 근사 트리의 검색 성능이 균일하지 않은 분포의 데이터 집합에서 크게 향상되었음을 보인다.

• Wind and Structures
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• 제34권6호
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• pp.469-482
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• 2022

The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.386-391
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• 2001

This paper presents a new two-point approximation method based on the exponential intervening variable. To avoid the lack of definition of the conventional exponential intervening variables due to zero- or negative-valued design variables the shifting level into each exponential intervening variable is introduced. Then a new quadratic approximation, whose Hessian matrix has only diagonal elements of different values, is proposed in terms of these intervening variables. These diagonal elements are computed in a closed form, which correct the typical error in the approximate gradient of the TANA series due to the lack of definition of exponential type intervening variables and their incomplete second-order terms. Also, a correction coefficient is multiplied to the pre-determined quadratic term to match the value of approximate function with that of the original function at the previous point. Finally, the authors developed a sequential approximate optimizer, solved several typical design problems used in the literature and compared these optimization results with those of TANA-3. These comparisons show that the proposed method gives more efficient and reliable results than TANA-3.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권11호
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• pp.1431-1438
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• 2004

강화학습(reinforcement learning)은 온라인으로 환경(environment)과 상호작용 하는 과정을 통하여 목표를 이루기 위한 전략을 학습한다. 강화학습의 기본적인 알고리즘인 Q-learning의 학습 속도를 가속하기 위해서, 거대한 상태공간 문제(curse of dimensionality)를 해결할 수 있고 강화학습의 특성에 적합한 함수 근사 방법이 필요하다. 본 논문에서는 이러한 문제점들을 개선하기 위해서, 온라인 퍼지 클러스터링(online fuzzy clustering)을 기반으로 한 Fuzzy Q-Map을 제안한다. Fuzzy Q-Map은 온라인 학습이 가능하고 환경의 불확실성을 표현할 수 있는 강화학습에 적합한 함수근사방법이다. Fuzzy Q-Map을 마운틴 카 문제에 적용하여 보았고, 학습 초기에 학습 속도가 가속됨을 보였다.

• Journal of Mechanical Science and Technology
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• 제15권7호
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• pp.876-888
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• 2001

For a large scaled optimization based on response surface methods, an efficient quadratic approximation method is presented in the context of the trust region model management strategy. If the number of design variables is η, the proposed method requires only 2η+1 design points for one approximation, which are a center point and tow additional axial points within a systematically adjusted trust region. These design points are used to uniquely determine the main effect terms such as the linear and quadratic regression coefficients. A quasi-Newton formula then uses these linear and quadratic coefficients to progressively update the two-factor interaction effect terms as the sequential approximate optimization progresses. In order to show the numerical performance of the proposed method, a typical unconstrained optimization problem and two dynamic response optimization problems with multiple objective are solved. Finally, their optimization results compared with those of the central composite designs (CCD) or the over-determined D-optimality criterion show that the proposed method gives more efficient results than others.