• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.455-464
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• 2009

Lee(2009) proposed the boundary-adaptive despeckling method using a Bayesian model which is based on the lognormal distribution for image intensity and a Markov random field(MRF) for image texture. This method employs the Point-Jacobian iteration to obtain a maximum a posteriori(MAP) estimate of despeckled imagery. The boundary-adaptive algorithm is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The boundary-adaptive scheme was comprehensively evaluated using simulation data and the effectiveness of boundary adaption was proved in Lee(2009). This study, as an extension of Lee(2009), has suggested a modified iteration algorithm of MAP estimation to enhance computational efficiency and to combine classification. The experiment of simulation data shows that the boundary-adaption results in yielding clear boundary as well as reducing error in classification. The boundary-adaptive scheme has also been applied to high resolution Terra-SAR data acquired from the west coast of Youngjong-do, and the results imply that it can improve analytical accuracy in SAR application.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.421-424
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• 1990

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic systems is presented. In the learning control structure, tracking and feedforward input converge globally and asymptotically as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of length of trajectories, it may be achieved with only system trajectories of small duration. In addition, these learning control schemes are expected to be effectively applicable to time-varying parametric systems as well as time-invariant systems, for the parameter estimation is performed at each fixed time along the iteration. Finally, no usage of acceleration signal and no in version of estimated inertia matrix in the parameter estimator makes these learning control schemes more feasible.

• Structural Engineering and Mechanics
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• v.37 no.5
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• pp.561-573
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• 2011

For efficient calculation of natural modes of structures, a numerical scheme which accelerates convergence of the subspace iteration method by employing accelerated starting Lanczos vectors was proposed in 2005. This paper is an extension of the study. The previous study simply showed feasibility of the proposed method by analyzing structures with smaller degrees of freedom. While, the present study verifies efficiency of the proposed method more rigorously by comparing closeness of conventional and accelerated starting vectors to genuine eigenvectors. This study also analyzes an example structure with larger degrees of freedom and more complex constraints in order to investigate applicability of the proposed method.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.125-131
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• 2007

An iterative Green's function method (IGFM) is introduced in order to analyze complex electromagnetic waveguide stub structures in view of a university student. The IGFM utilizes a Green's function approach and an regional iteration scheme. A physical iteration mechanism with simple mathematical equations facilitates clear formulations of the IGFM. Scattering characteristics of a standard E-plane T-junction stub in a parallel-plate waveguide are theoretically investigated in terms of the IGFM. Numerical computations illustrate the characteristics of reflection and transmission powers versus frequency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1265-1278
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• 2017

Dynamic task scheduling is one of the most popular research topics in the cloud computing field. The cloud scheduler dynamically provides VM resources to variable cloud tasks with different scheduling strategies in cloud computing. In this study, we utilized a valid model to describe the dynamic changes of both computing facilities (such as hardware updating) and request task queuing. We built a novel approach called Policy Iteration Scheduling (PIS) to globally optimize the independent task scheduling scheme and minimize the total execution time of priority tasks. We performed experiments with randomly generated cloud task sets and varied the performance of VM resources using Poisson distributions. The results show that PIS outperforms other popular schedulers in a typical cloud computing environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.451-458
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• 2012

This paper addresses the problem that policy iteration (PI) for continuous-time (CT) systems requires explorations of the state space which is known as persistency of excitation in adaptive control community, and as a result, proposes a PI scheme explorized by an additional probing signal to solve the addressed problem. The proposed PI method efficiently finds in online fashion the related CT linear quadratic (LQ) optimal control without knowing the system matrix A, and guarantees the stability and convergence to the LQ optimal control, which is proven in this paper in the presence of the probing signal. A design method for the probing signal is also presented to balance the exploration of the state space and the control performance. Finally, several simulation results are provided to verify the effectiveness of the proposed explorized PI method.

• East Asian mathematical journal
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• v.28 no.1
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• pp.63-71
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• 2012

The aim of this article is to study an implicit iteration process with errors for a finite family of non-Lipschitzian asymptotically non expansive mappings in the intermediate sense in Banach spaces. Also we establish some strong convergence theorems and a weak convergence theorem for said scheme to converge to a common fixed point for non Lipschitzian asymptotically nonexpansive mappings in the intermediate sense. The results presented in this paper extend and improve the corresponding results of [1], [3]-[8], [10]-[11], [13]-[14], [16] and many others.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.3-8
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• 2013

We consider some adaptive iteration schemes that provide lower complexity of the iterative receiver by reducing unnecessary iterations. While conventional iterative receiver considers only fixed number of iterations, we apply adaptive iteration schemes, taking into account quality of the received frame. Based on simulation results, proposed schemes reduce average number of iterations while maintaining BER performance compared to the conventional scheme.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.427-438
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• 1991

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.481-490
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• 2003

This paper presents an efficient eigensolution method for non-proportionally damped systems. The proposed method is obtained by applying the accelerated Newton-Raphson technique and the orthonormal condition of the eigenvectors to the linearized form of the quadratic eigenproblem. A step length and a selective scheme are introduced to increase the convergence of the solution. The step length can be evaluated by minimizing the norm of the residual vector using the least square method. While the singularity may occur during factorizing process in other iteration methods such as the inverse iteration method and the subspace iteration method if the shift value is close to an exact eigenvalue, the proposed method guarantees the nonsingularity by introducing the orthonormal condition of the eigenvectors, which can be proved analytically. A numerical example is presented to demonstrate the effectiveness of the proposed method.