• 한국경영과학회지
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• 제16권2호
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• pp.118-127
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• 1991

In this paper, criteria and algorithms for the optimal service rate in a closed queueing network have been established. The objective is to minimize total cost. It is shown that system throughput is increasing concave over the service rate of a node and cycle time is increasing convex over the set of service times with a single calss of cubsomers. This enables developing an algorithm using a steepest descent method when the cost function for service rate is convex. The efficiency of the algorithm rests on the fact that the steepest descent direction is readily obtained at each iteration from the MVA algorithm. Several numerical examples are presented. The major application of this research is optimization of facility capacity in a manufacturing system.

• 대한수학회보
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• 제43권3호
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• pp.589-598
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• 2006

In the present investigation, sharp upper bounds of $|a3-{\mu}a^2_2|$ for functions $f(z)=z+a_2z^2+a_3z^3+...$ belonging to certain subclasses of starlike and convex functions with respect to symmetric points are obtained. Also certain applications of the main results for subclasses of functions defined by convolution with a normalized analytic function are given. In particular, Fekete-Szego inequalities for certain classes of functions defined through fractional derivatives are obtained.

• Journal of Multimedia Information System
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• 제1권1호
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• pp.87-93
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• 2014

The aim of microscopic image restoration is to recover the image by applying the inverse process of degradation, and the results facilitate automated and improved analysis of the image. In this work, we consider the problem of image restoration as a minimization problem of convex cost function, which consists of a least-squares fitting term and regularization terms with non-negative constraints. The finite step method is proposed to solve this constrained convex optimization problem. We demonstrate the convergence of this method. Efficiency and restoration capability of the proposed method were tested and illustrated through numerical experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.988-991
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• 1996

The purpose of this thesis is to develop methods of designing robust LQR/LQG controllers for time-varying systems with real parametric uncertainties. Controller design that meet desired performance and robust specifications is one of the most important unsolved problems in control engineering. We propose a new framework to solve these problems using Linear Matrix Inequalities (LMls) which have gained much attention in recent years, for their computational tractability and usefulness in control engineering. In Robust LQR case, the formulation of LMI based problem is straightforward and we can say that the obtained solution is the global optimum because the transformed problem is convex. In Robust LQG case, the formulation is difficult because the objective function and constraint are all nonlinear, therefore these are not treatable directly by LMI. We propose a sequential solving method which consist of a block-diagonal approach and a full-block approach. Block-diagonal approach gives a conservative solution and it is used as a initial guess for a full-block approach. In full-block approach two LMIs are solved sequentially in iterative manner. Because this algorithm must be solved iteratively, the obtained solution may not be globally optimal.

• 전자공학회논문지C
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• 제34C권7호
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• pp.82-91
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• 1997

In this paper, we analyze the dynamics of langevine competitive learning neural network based on its fokker-plank equation. From the viewpont of the stochastic differential equation (SDE), langevine competitive learning equation is one of langevine stochastic differential equation and has the diffusin equation on the topological space (.ohm., F, P) with probability measure. We derive the fokker-plank equation from the proposed algorithm and prove by introducing a infinitestimal operator for markov semigroups, that the weight vector in the particular simplex can converge to the globally optimal point under the condition of some convex or pseudo-convex performance measure function. Experimental resutls for pattern recognition of the remote sensing data indicate the superiority of langevine competitive learning neural network in comparison to the conventional competitive learning neural network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.628-631
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• 2005

In this paper, we propose tuning method of PID-PD controller to satisfy design specifications in frequency domain as well as time domain. The proposed tuning method of PID-PD controller that consist of the convex set of PID and PI-PD controller controls the closed-loop response to locate between the step responses, and Bode magnitudes of closed-loop transfer functions controlled by PID and PI-PD controller. The controller is designed by the optimum tuning method to minimize the proposed specific cost function subject to sensor noise insensitivity and robust stability. Its effectiveness is examined by the case study and analysis.

• Journal of applied mathematics & informatics
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• 제26권5_6호
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• pp.961-972
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• 2008

In this paper, we consider the global exponential stability of cellular neural networks with time-varying delays. Based on the Lyapunov function method and convex optimization approach, a novel delay-dependent criterion of the system is derived in terms of LMI (linear matrix inequality). In order to solve effectively the LMI convex optimization problem, the interior point algorithm is utilized in this work. Two numerical examples are given to show the effectiveness of our results.

• 대한전자공학회논문지TE
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• 제37권3호
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• pp.114-122
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• 2000

본 논문에서는 기존의 Fuzzy C-Means, Nearest Neighborring Classification, FMMCNN, Fuzzy -ART등에서 사용하였던 정형에 근거한 분류에서 유기될 수 있던 판단 오류를 최소화하기 위해 단 한가지의 형태적 특징을 갖고 있는 정형에 의존하지 않고 분류를 수행하는 방법을 제안하고i파 한다. 이를 위해 본 논문에서는 주어진 학습 데이터로 학습하는 과정에서 볼록 다면체를 적응적으로 생성하고 다면체의 구조를 수정하는 퍼지 신경회로망을 설계하였다. 따라서, 본 방법은 순차적으로 입력되는 데이터를 분류하여 패턴 유형들을 생성하는 기능을 갖게된다. 본 방법의 유용성을 증명하기 위해, Hyperbox를 정형으로 하는 FMMCNN과의 다양한 시뮬레이션 비교를 수행하였다.

• 대한수학회보
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• 제29권2호
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• pp.227-232
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• 1992

In this paper we prove that if (.ohm., .SIGMA., .mu.) is a non-purely atomic measure space and X is strictly convex, then X has the asymptotic-norming property II if and only if $L_{p}$ (X, .mu.), 1 < p < .inf., has the asymptotic-norming property II. And we prove that if $X^{*}$ is an Asplund space and strictly convex, then for any p, 1 < p < .inf., $X^{*}$ has the .omega.$^{*}$-ANP-II if and only if $L_{p}$ ( $X^{*}$, .mu.) has the .omega.$^{*}$-ANP-II.*/-ANP-II.

• 대한수학회보
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• 제52권6호
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• pp.1925-1935
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• 2015

In this paper, we study right half-plane harmonic mappings $f_0$ and f, where $f_0$ is fIxed and f is such that its dilatation of a conformal automorphism of the unit disk. We obtain a sufficient condition for the convolution of such mappings to be convex in the direction of the real axis. The result of the paper is a generalization of the result of by Li and Ponnusamy [11], which itself originates from a problem posed by Dorff et al. in [7].