• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.449-465
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• 1994

The two common numerical methods to approximate the solution of partial differential equations are the finite element method and the finite difference method. They both lead to solving large sparse linear systems. For many applications, it is not unusal that the order of matrix is greater than 10, 000. For this kind of problem, a direct method such as Gaussian elimination can not be used because of the prohibitive cost. To this end, many iterative methods with modest cost have been studied and proposed by numerical analysts.(omitted)

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.666-668
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• 1988

A new tridiagonal Toeplitz linear system (TTLS) solver is proposed. The solver decomposes a strictly diagonally dominant TTLS equation into a number of subsystems using a limit convergent of an analytic solution of a continued fraction. Subsystem equations can be solved employing a modified Gaussian elimination method. The solver fully exploits parallelism. Optimized operational environment for the algorithm is discussed.

• Journal of Korean Ophthalmic Optics Society
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• v.1 no.1
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• pp.29-39
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• 1996

We studied the method which would determine the appropriate additive damping factor for the damped least sequres(DLS) optimization. We calculated eigenvalues of the product of the Jacobian matrix of error function by using the singular value decomposition(SVD) method. While suitable damping factor was appiled to the additive DLS by using SVD and Gaussian elimination method, the convergence and stability of the optimization process were examined in a triplet-type camera lens-system where the condition number is well conditioned. We compared the convergence and stability of merit function when median, maximum and minimum of eigenvalues were used as a damping factor in the optimization process. When damping factor is median of eigenvalue, the convergence and stability of merit function are more excellent than in the case of two other eigenvalues. Thus, we adopt the median of eigenvalues as an appropriate damping factor. Next, by using SVD and Gaussian elimination method, we compound the convergence and stability of optimization process for triplet-type camera lens-system design. In these two method; triplet-type camera lens-system in which condition number is well conditioned, has little improvement with the combination of DLS and SVD.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.949-956
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• 2014

Recently, with the development of hardware and software technology related with image information delivery, the demand for various multimedia service has increased. But, the process of treating, sending, and storing image signals generates image degradation by various external causes. The main cause of image degradation is noise. image is mostly damaged by AWGN (additive white Gaussian noise). Therefore, there have been active researches on noise elimination. This paper, to reduce the effects of AWGN added to the image, suggests a noise-eliminating algorithm which is excellent in low-frequency and high-frequency characteristics in space. And, this paper, through simulation techniques, compared the result of the suggested algorithm with those of the existing methods. And, to evaluate the performance of it, PSNR (peak signal to noise ratio) was used.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.113-116
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• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• Journal of Digital Contents Society
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• v.14 no.1
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• pp.117-124
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• 2013

This paper suggests the robust method of extraction for moving objects in illumination variation by using image sequence from an immovable camera. The most difficult part of the implication is the effect by illumination and noise. The object area is hardly estimated when the dusky area occurs in illumination variation by time change. This thesis describes the extraction of moving objects employed by Gaussian mixture model which is noise robust measure. Also, the report suggests the elimination method of illumination part in input image by the representative illumination image which is defined to minimize the illumination influence.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.337-339
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• 2004

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

• ETRI Journal
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• v.34 no.5
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• pp.719-726
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• 2012

Messages are generally selected with the same probability in the encoding scheme of rateless codes for equal error protection. In addition, a belief propagation (BP) decoding scheme is generally used because of the low computational complexity. However, the probability of recovering a new message by BP decoding is reduced if both the recovered and unrecovered messages are selected uniformly. Thus, more codeword symbols than expected are required for the perfect recovery of message symbols. Therefore, a new encoding scheme with a nonuniform selection of messages is proposed in this paper. In addition, a BP-Gaussian elimination hybrid decoding scheme that complements the drawback of the BP decoding scheme is proposed. The performances of the proposed schemes are analyzed and compared with those of the conventional schemes.

• Korean Management Science Review
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• v.13 no.3
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• pp.105-113
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• 1996

Constructing an initial basis is an important process in the simplex method. An initial basis greatly affects the number of iterations of iterations and the execution time in the simplex method. The purpose of this paper is to construct a good initial basis. First, to avoid linear dependency among the chosen columns, an enhanced Gaussian elimination method and a method using non-duplicated nonzero elements are developed. Second, for an order to choose variables, the sparsity of the column is used. Experimenal results show that the proposed method can reduce the number of iterations and the execution time compared with Bixby's method by 12%.