• The Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.1
• /
• pp.45-52
• /
• 2016

This paper suggests a new method of finding regularization parameter for image restoration problems. Wiener filter requires priori information such that power spectrums of original image and noise. Constrained least squares restoration also requires knowledge of the noise level. If the prior information is not available, separate optimization functions for Tikhonov regularization parameter are suggested in the literature such as generalized cross validation and L-curve criterion. In this paper, self-regularization method that connects bias term of augmented linear system and smoothing term of Tikhonov regularization is introduced in the frequency domain and applied to the image restoration problems. Experimental results show the effectiveness of the proposed method.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.2
• /
• pp.97-101
• /
• 2010

FIR type system identification with noisy input and output data can be solved by a total least squares (TLS) estimation. However, the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose an iterative convex combination algorithm between TLS and least squares (LS). This combined algorithm shows robustness against the noise variance ratio. Consequently, the practical workability of the TLS method with noisy data has been significantly broadened.

• Journal of Korean Society of Transportation
• /
• v.18 no.6
• /
• pp.89-99
• /
• 2000

To cope with the limits of conventional O-D trip matrix collecting methods, several approaches have been developed. One of them is bilevel Programming method Proposed by Yang(1995), which uses Sensitivity Analysis Based(SAB) algorithm to solve Generalized Least Square(GLS) problem. However, the SAB a1gorithm has revealed two critical short-comings. The first is that when there exists a significant difference between target O-D matrix and true O-D matrix, SAB algorithm may not produce correct solution. This stems from the heavy dependance on the historical O-D information, in special when gravel Patterns are dramatically changed. The second is the assumption of iterative linear approximation to original Problem. Because of the approximation, SAB algorithm has difficulty in converging to Perfect Stackelberg game condition. So as to avoid the Problems. we need a more robust and stable solution method. The main purpose of this Paper is to show the problem of the dependency of Previous models and to Propose an alternative solution method to handle it. The Problem of O-D matrix estimation is intrinsically nonlinear and nonconvex. thus it has multiple solutions. Therefore it is necessary to require a method for searching globa1 solution. In this paper, we develop a solution algorithm combined with genetic algorithm(GA) , which is widely used as probabilistic global searching method To compare the efficiency of the algorithm, SAB algorithm suggested by Yang et al. (1992,1995) is used. From the results of numerical example, the Proposed algorithm is superior to SAB algorithm irrespective of travel patterns.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.4
• /
• pp.264-274
• /
• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.35T no.3
• /
• pp.87-95
• /
• 1998

This paper presents the method for the automatic tuning of a design weighting polynomial parameter of a generalized minimum-variance stochastic self tuning controller which adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design weighting polynomial parameters. The proposed self tuning method is simple and effective compared with other existing self tuning methods. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Journal of Korean Society of Transportation
• /
• v.18 no.4
• /
• pp.117-126
• /
• 2000

Conventional methods for collecting origin-destination trips have been mainly relied on the surveys of home or roadside interview. However, the methods tend to be costly, labor intensive and time disruptive to the trip makers, thus the methods are not considered suitable for Planning applications such as routing guidance, arterial management and information Provision, as the parts of deployments in Intelligent Transport Systems Motivated by the problems, more economic ways to estimate origin-destination trip tables have been studied since the late 1970s. Some of them, which have been estimating O-D table from link traffic counts are generally Entropy maximizing, Maximum likelihood, Generalized least squares(GLS), and Bayesian inference estimation etc. In the Paper, with user equilibrium constraint we formulate GLS problem for estimating O-D trips and develop a solution a1gorithm by using Genetic Algorithm, which has been known as a g1oba1 searching technique. For the purpose of evaluating the method, we apply it to Seoul inner ringroad and compare it with gradient method proposed by Spiess(1990). From the resu1ts we fond that the method developed in the Paper is superior to other.

• The Korean Journal of Financial Studies
• /
• v.4 no.1
• /
• pp.39-71
• /
• 1998

본 연구는 중소기업의 자금흐름특성과 기타 재무특성이 대기업의 그것과 어떤 차이를 갖는가를 살펴보고, 중소기업의 재무경쟁력을 규명하는데 그 목적을 두고 있다. 본 연구에서는 차입시장에서의 재무경쟁력은 실질유효이자율과 대출효율성계수를 기준으로 하였으며, 주식시장에서의 재무경쟁력은 자기자본비용과 주당 EVA를 기준으로 하였다. 그리고 본 연구에서는 연구목적을 달성하기 위해 MANOVA검정과 패널 자료를 이용한 일반화 최소자승법을 사용하였다. 중소기업의 단기 운전자금 사정은 대기업에 비해 열악하며, 재무경쟁력 역시 뒤떨어지는 것으로 나타났다. 다시 말해서 중소기업의 영업수익성은 대기업에 비해 오히려 좋으나, 차입시장 및 주식시장에서의 재무경쟁력은 상대적으로 낮은 것으로 밝혀졌다. 중소기업의 차입시장에서의 재무경쟁력을 결정하는 요인으로는 재무위험(-), 영업수익성(+), 유동성(+), 담보력(+) 등이 있었다. 그리고 중소기업의 주식시장에서의 재무경쟁력을 결정하는 요인으로는 재무위험(-), 영업위험(-), 영업수익성(+), 유동성(+), 성장성(+)등이 있었다.

• Journal of Korean Society of Transportation
• /
• v.18 no.2
• /
• pp.41-52
• /
• 2000

In the developing country, the transportation situation is changed very quickly and the transportation environment is not stable. So the transportation planning should be frequently made in considering the limited cost and time. And the traditional large-scale survey(household survey, roadside interview, etc.) has many Problem like the difficulty for doing it and getting mood results. Therefore the study about the method of evaluation on the traffic count based O/D matrix is Processing actively recently. Though the many study for the network in the realistic size are enacted, the study for comparing with the advantage and disadvantage of each method are few. Therefore this study mainly deals with the static method among the existing models of evaluation on the traffic count based O/D matrix(in terms of the transportation plan). Bi-level(GU) and gradient method are selected as main alternative model and analyzed their capability and validity. For testing the reliability of the models, Bi-level(GLS) and gradient method are adapted to toy network. Then we analyze the result of testing, and study the way for large network.

• Proceedings of the KOR-KST Conference
• /
• 2002.02a
• /
• pp.17-42
• /
• 2002

전통적인 OD조사에 의한 OD추정의 여러 문제점들로 인해 링크관측교통량과 기존OD를 결합해 OD를 추정하고자 하는 연구들이 제시되고 있다. Yang(1995)은 일반화최소자승법을 풀기 위한 IEA와 SAB 알고리즘을 제시하였다. 그러나 두 알고리즘의 문제점은 첫째 실제 OD를 알기가 어렵기 때문에 기존 OD를 중요한 추정기준으로 설정한다는 것으로, 이러한 추정의 종속성으로 인해, 기존 OD와 실제 OD의 차이가 큰 경우 정확한 해를 도출하지 못한다. 두 번째 문제는 통행패턴 추정시 선형근사화를 가정하기 때문에 게임이론적 측면에서 전제로 설정한 완전한 Stackelberg 상황을 구현하지 못한다는 것이다. 이러한 문제점을 피하기 위해서는 기존 OD나 관측교통량의 오차에 일관적인 해도출 기법이 필요하다. OD추정 문제는 본질적으로 비선형이고 비볼록하여 전역해 탐색기법이 필요하기 때문에 전역최적화가 가능한 유전알고리즘을 이용한 OD추정모형(GAM)을 개발하였다. 사례네트워크 분석결과, GAM은 기존 OD의 오차에 대해 크게 종속적이지 않으며 OD구조가 변하는 경우에도 추정이 가능하여, 일반적으로 실제 OD를 알 수 없는(기존OD의 오차가 어느 정도인지를 알 수 없는) 도시부 네트워크에서 신뢰성있는 추정력을 보였다. 또한 기존 OD 추정모형은 비교적 용이하게 차종별로 관측할 수 있는 링크교통량을 차종구분 없이 단일차종으로 이용함으로써, 정보의 손실을 초래하여 결과적으로 모형의 추정력을 저하시켰다. 그렇지만 다차종 링크관측교통량으로부터 다차종 OD 추정연구는 거의 없었으며, 그 결과가 단일차종에 대한 추정결과와 어떻게 다른지에 대한 연구도 전무하였다. 본 연구에서는 유전알고리즘을 이용한 OD 추정모형을 다수단 OD 추정모형(GAMUC)으로 확대하였다. 사례 분석 결과 단일차종 OD추정기법은 심각한 추정오류를 범할 수 있으며, 그 적용성도 낮다는 것을 보였다. 다차종 OD 추정기법이 단일차종 OD 추정기법보다 양호한 추정력을 보였으며, 다차종 기법 중에서는 GAMUC가 IEAMUC보다 우수한 추정력을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.44 no.6
• /
• pp.1-10
• /
• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.