• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.9-14
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• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3694-3702
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• 1999

OSL algorithm has an advantage that repeated algorithm is easily derived, even though penalty function which has a complicated transcendental function. In order to solve this problem, we suggested MPEMG algorithm. However, though this algorithm extend convergence rate of smoothing constant, it include the problem that is not faster than OSL algorithm in the convergence rate increasing penalized log-likelihood. Accordingly, in this paper, we will suggest SMOSLG digital image restoration algorithm which is fast in the convergence rate as well as extend convergence region of smoothing constant. And also we will study the usefulness of algorithm suggested through digital image simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.307-312
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• 2021

This paper proposes an adaptive time-delayed control approach with the integral sliding-mode surface for the fast convergence rate of robot manipulators. Adaptive switching gain aims to guarantee the system stability in such a way as to suppress time-delayed estimation error in the proposed control approach. Moreover, it makes an effort to increase the convergence ability in reaching the phase. An integral sliding-mode surface is employed to achieve a fast convergence rate in the sliding phase. The stability of the proposed one is proved to be asymptotically stable in the Lyapunov stability. The efficiency of the proposed control approach is illustrated with a tutorial example in robot manipulator, which is compared to that of the existing control approach.

• East Asian Economic Review
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• v.25 no.4
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• pp.403-424
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• 2021

AdaBoost tweaks the sample weight for each training set used in the iterative process, however, it is demonstrated that it provides more correlated errors as the boosting iteration proceeds if models' accuracy is high enough. Therefore, in this study, we propose a novel way to improve the performance of the existing AdaBoost algorithm by employing heterogeneous models and a stochastic twist. By employing the heterogeneous ensemble, it ensures different models that have a different initial assumption about the data are used to improve on diversity. Also, by using a stochastic algorithm with a decaying convergence rate, the model is designed to balance out the trade-off between model prediction performance and model convergence. The result showed that the stochastic algorithm with decaying convergence rate's did have a improving effect and outperformed other existing boosting techniques.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.90-94
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• 2008

It is well-blown that the convergence rate gets worse when an input signal to an adaptive filter is correlated. In this paper we propose a new adaptive filtering algorithm that makes the convergence rate much improved even for highly correlated input signals. By introducing an orthogonal constraint between successive input signal vectors we overcome the slow convergence problem of the LMS algorithm with the correlated input signal. Simulation results show that the proposed algerian yields fast convergence speed and excellent tracking capability under both time-invariant and time-varying environments, while keeping both computation and implementation simple.

• Journal of the Korean Mathematical Society
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• v.33 no.3
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• pp.495-505
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• 1996

Let $\Omega$ be a bounded domain in $R^d, d \geq 1$, with smooth boundary $\partial\Omega$, and let $0 < T < \infty$ be given.

• Journal of the Korean Mathematical Society
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• v.32 no.1
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• pp.7-15
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• 1995

Multigrid method has been used widely to solve elliptic problems because of its applicability to many class of problems and fast convergence ([1], [3], [9], [10], [11], [12]). The estimate of convergence rate of multigrid is one of the main objectives of the multigrid analysis ([1], [2], [5], [6], [7], [8]). In many problems, the convergence rate depends on the regularity of the solutions([5], [6], [8]). In this paper, we present an improved estimate of reduction factor of multigrid iteration based on the proof in [6].

• Journal of the Korean Mathematical Society
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• v.53 no.3
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• pp.691-707
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• 2016

In this paper, we first introduce two one-parameter relaxation (OPR) iterative methods for solving singular saddle point problems whose semi-convergence rate can be accelerated by using scaled preconditioners. Next we present formulas for finding their optimal parameters which yield the best semi-convergence rate. Lastly, numerical experiments are provided to examine the efficiency of the OPR methods with scaled preconditioners by comparing their performance with the parameterized Uzawa method with optimal parameters.

• Bulletin of the Korean Mathematical Society
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• v.54 no.2
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• pp.359-373
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• 2017

In this paper, we propose the Stancu type generalization of a kind of modified q-Gamma operators. We estimate the moments of these operators and give the basic convergence theorem. We also obtain the Voronovskaja type theorem. Furthermore, we obtain the local approximation, rate of convergence and weighted approximation for these operators.

• Communications of the Korean Mathematical Society
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• v.18 no.2
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• pp.375-383
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• 2003

Under some conditions on an array of rowwise independent random variables, Hu et at. (1998) obtained a complete convergence result for law of large numbers with rate {a$\_$n/, n $\geq$ 1} which is bounded away from zero. We investigate the general situation for rate {a$\_$n/, n $\geq$ 1) under similar conditions.