• Journal of the Chungcheong Mathematical Society
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• v.25 no.2
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• pp.367-377
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• 2012

The goal of the image restoration is to find a good approximation of the original image for the degraded image, the blurring matrix, and the statistics of the noise vector given. Fast truncated Lagrange (FTL) method has been proposed by G. Landi as a image restoration method for large-scale ill-conditioned BTTB linear systems([3]). We implemented FTL method for the image restoration problem with reflective boundary condition which gives better reconstructions of the unknown, the true image.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3E
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• pp.99-108
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• 2005

To investigate the equalizer performance in underwater acoustic communication m the presence of intersymbol interference (ISI) due to multipath, computer simulations are carried out in discrete multipath shallow water channels for three different horizontal ranges. For the purpose of computation simplicity, least mean square (LMS) algorithm is adopted both in linear equalizer and nonlinear equalizer, decision feedback equalizer (DFE) to cancel out ISI effects. Binary phase shift keying (BPSK) signals have been transmitted with high data rate of 2000bps through the use of equalization technique. The results demonstrate that equalization is an efficient way to achieve high transmission data rate in the shallow water channel.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.4
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• pp.136-144
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• 1983

In this paper, a new scheme of implicit MRAC is presented for single input single output discrete system. The MRAC can be applied to the nonminimum phase system, too. They have simple structure because the parameters of the controller are estimated directly by changing the plant output equation properly. In this scheme, the observation process is well seperated from the adaptation process, so the adaptation algorithm is derived from the exponentially weighted least square method which has fast convergence characteristics and can deal with the time varying plant. The consistency of the estimated parameter is proved. And it is also proved the whole system has the stabilizing property. The effectiveness of the algorithm and the structure is illustrated by the computer simulation of the model reference adaptive control for a third order plant. It is proposed how to select the selectable parameters in the adaptive control system from the simulation results.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.

• Publications of The Korean Astronomical Society
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• v.25 no.4
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• pp.167-176
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• 2010

In the region of the intermediate open cluster M35 (NGC 2168), the time-series of V CCD images was taken for 12 nights from December 18, 2007 to September 25, 2010. From this observation, we detected 22 variable stars including 15 new ones. They are 6 $\delta$ Scuti, a Cepheid, an RR Lyrae, 9 eclipsing binaries and 5 semi-long periodic and/or slow irregular type variable stars. For the V photometry of the $\delta$2 Scuti stars, the multi-frequency analysis was performed using the Discrete Fourier Transform and the linear least-square fitting.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.7
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• pp.1360-1365
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• 2011

Electro-hydraulic servo motor is used to a lot of in the field of industrial equipment which requires one of the control functions among pressure, flow, and power output. In this paper, linear discrete reference model of the electro-hydraulic servo motor system are made for 1-step ahead predictive control. The parameters of electro-hydraulic servo motor system are estimated using the recursive least square method. 1-step ahead predictive model output of electro-hydraulic servo motor system corresponded to reference model output in spite of estimated parameters are not meet real parameters. Control performance affections are studied due to the forgetting factors variation.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.151-157
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• 2000

The MRAC theory has proved to be one of the most popular algorithms in the field of adaptive control, particularly for practical application to devices such as an hydraulic servosystem of which parameters are unknown or varying during operation. For small sampling period, the discrete time system becomes a nonminimal phase system. The $\delta$-MRAC was introduced to obtain the control performance of nonminimal phase system, because the z-MRAC can not control the plant for small sampling period. In this paper, $\delta$-MRAC is applied to the control of an hydraulic servosystem which is composed of servovalve, hydraulic cylinder and inertia load.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.88-95
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• 2005

This paper presents a design scheme of model reference adaptive control incorporating a Neural Network for a pneumatic servo system. The parameters of discrete-time model of plant are estimated by using the recursive least square method. Neural Network is utilized in order to compensate the nonlinear nature of plant such as compressibility of air and frictions present in cylinder. The experiment of a trajectory tracking control using the proposed control scheme has been performed and its effectiveness has been proved by comparing with the results of a model reference adaptive control.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.625-630
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• 1986

In this paper, an adaptive observer based upon the exponentially weighted least-square method is implemented in the design of a model reference adaptive controller for an unknown time-invariant discrete single-input single-output linear plant. The adaptive observer estimates the padrameter vectors and initial state vector. The control input is determined so that the output of the plant converges to the output of the stable model reference.

• Journal of the Korea Computer Graphics Society
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• v.22 no.5
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• pp.1-16
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• 2016

In this paper, we propose and efficient curve reconstruction method based on the classical least-square fitting scheme. Specifically, given planar sample points equipped with normals, we reconstruct the objective curve as the zero set of a hierarchical implicit ZP(Zwart-Powell)-spline that can recover large holes of dataset without loosing the fine details. As regularizers, we adopted two: a Tikhonov regularizer to reduce the singularity of the linear system and a discrete Laplacian operator to smooth out the isocurves. Benchmark tests with quantitative measurements are done and our method shows much better quality than polynomial methods. Compared with the hierarchical bi-quadratic spline for datasets with holes, our method results in compatible quality but with less than 90% computational overhead.