• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1098-1107
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• 1990

Decentralized iterative learning control methods are presented for a class of large scale interconnected linear dynamic systems, in which iterative learning controller in each subsystem operates on its local subsystem exclusively with no exchange of information between subsystems. Suffcient conditions for convergence of the algorithms are given and numerical examples are illustrated to show the validity of the algorithms. In particular, the algorithms are useful for the systems having large uncertainty of inter-connected terms.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.79-82
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• 2002

In this paper, we propose an adaptive repetitive control algorithm for the system the task of which is repetitive. The feedforward controller in the repetitive control system is modified by using the system parameter identifier in order to improve the convergence characteristics. The proposed algorithm is applied to the tracking control of a linear BLDC motor to which a periodic reference input is applied. It is illustrated by simulation results that the proposed adaptive repetitive control method yields better control performance than existing repetitive control even when modeling errors exist.

• Journal of the Chungcheong Mathematical Society
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• v.7 no.1
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• pp.141-152
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• 1994

We consider a numerical scheme for solving a nonlinear boundary integral equation (BIE) obtained by reformulation the nonlinear boundary value problem (BVP). We give a simple alternative to the standard collocation method for the nonlinear BIE. This method consists of one conventional linear system and another coupled linear system resulting from an auxiliary BIE which is obtained by differentiating both side of the nonlinear interior integral equations. We obtain an analogue BIE through the perturbation of the fundamental solution of Laplace's equation. We procure the super-convergence of approximate solutions.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.733-739
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• 1988

Adaptive controllers for linear system whose nominal values of coefficients only are known, that is corrupted by disturbance, are designed by signal synthesis model reference adaptive control (MRAC). This design is stemmed from the Lyapunov direct method. To reduce the model following error and to improve the conrergence rate of the design, an indirect suboptimal control law is de rived using the Hamilton Jacobi Beellman equation. Proper compensaton for the effects of time varying coefficients and plant disturbance are suggested. In the design procedure no complete identification of unknown coefficients are required.

• Korean Journal of Mathematics
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• v.29 no.3
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• pp.527-545
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• 2021

The aim of this article is to give a numerical method for solving Abel's general fuzzy linear integral equations with arbitrary kernel. The method is based on approximations of fractional integrals and Caputo derivatives. The convergence analysis for the proposed method is also given and the applicability of the proposed method is illustrated by solving some numerical examples. The results show the utility and the greater potential of the fractional calculus method to solve fuzzy integral equations.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.213-228
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• 2008

Using a linear loss function, this paper considers the one-sided testing problem for the exponential distribution via the empirical Bayes(EB) approach. Based on right censored data, we propose an EB test for the exponential parameter and obtain its convergence rate and asymptotic optimality, firstly, under the condition that the censoring distribution is known and secondly, that it is unknown.

• Honam Mathematical Journal
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• v.32 no.3
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• pp.399-412
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• 2010

In this paper, we consider a preconditioned accelerated overrelaxation (PAOR) method to solve systems of linear equations. We show the convergence of the PAOR method. We also give com-parison results when the coefficient matrix is an L- or H-matrix. Finally, we provide some numerical experiments to show efficiency of PAOR method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.57-60
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• 2003

We have proposed the adaptive noise reduction algorithm using the MDFT. The algorithm proposed use the linear prediction coefficients of the AR method based on Sign algorithm that is the modified LMS instead of the least mean square(LMS). The signals with a random noise tracking performance are examined through computer simulations and confirmed that the high speed adaptive noise reduction processing system is realized with rapid convergence.

• Journal of the Korean Mathematical Society
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• v.54 no.3
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• pp.1031-1047
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• 2017

In this paper, we introduce two general iterative algorithms (one implicit algorithm and other explicit algorithm) for nonexpansive mappings in a reflexive Banach space with a uniformly $G{\hat{a}}teaux$ differentiable norm. Strong convergence theorems for the sequences generated by the proposed algorithms are established.

• Journal of the Korean Society of Industry Convergence
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• v.5 no.3
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• pp.261-268
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• 2002

In this paper, the shape optimization is considered over the upper slab of reinforced concrete culvert. The sequential linear programming method (SLP) is used as a rational approach to this shape optimization. To make a comparison between the arch shaped member and the straight member for the upper slab, the culverts with 5~20m earth height were adopted. It is shown that the optimum rise/span is about 7%-13%, and the arch shaped member is more cheap (over 10%) than the straight member for the construction cost.