• The Journal of the Acoustical Society of Korea
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• 제15권3E호
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• pp.27-30
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• 1996

A recursive estimation for the enhancement of white noise contaminated speech is proposed. This method is based on the Kalman filter with time-varying parametric model for the clean speech signal. Then, hidden filter model are used to model the clean speech signal. An approximation improvement of 4-5 dB in SNR is achieved at 5 and 10 dB input SNR, respectively.

• Journal of applied mathematics & informatics
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• 제16권1_2호
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• pp.19-35
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• 2004

We develop in this paper some preconditioners for sparse non-symmetric M-matrices, which combine a recursive two-level block I LU factorization with multigrid method, we compare these preconditioners on matrices arising from discretized convection-diffusion equations using up-wind finite difference schemes and multigrid orderings, some comparison theorems and experiment results are demonstrated.

• 대한전기학회논문지
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• 제32권5호
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• pp.183-188
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• 1983

This paper deals with short-term load forecasting. The load model is represented by the state variable form to exploit the Kalman filter technique. The load model is derived from Taylor series expansion and remainder term is considered as noise term. In order to solve recursive filter form, among various algorithm of solving Kalman filter, this paper uses exponential data weighting technique. This paper also deals with the asymptotic stability of filter. Case studies are carried out for the hourly power demand forecasting of the Korea electrical system.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.279-288
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• 1998

We propose a multiprocess dynamic Poisson model for the analysis of Poisson process with the covariates. The algorithm for the recursive estimation of the parameter vector modeling time-varying effects of covariates is suggested. Also the algorithm for forecasting of numbers of events at the next time point based on the information gathered until the current time is suggested.

• 대한전기학회논문지:전력기술부문A
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• 제48권7호
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• pp.898-902
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• 1999

In this paper, we proposed an algorithm for estimating parameters of nonlinear continuous-discrete state-space system. This algorithm uses the conventional extended Kalman filter(EKF) for estimating state variables, and modifies the recursive prediction error method for parameter estimation of the nonlinear system. Simulation results for both linear and nonlinear measurements under the environment of process and measurement noises show a convincing performance of the proposed algorithm.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1991년도 학술발표회 논문집
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• pp.23-27
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• 1991

RLS(Recursive Least Squares)나 LMS(Least mean square)등은 알고리듬 고유의 성질상 잡음이 섞인 시스템에 있어서는 올바른 역 모델링을 할 수 없다. 따라서, 잡음의 영향을 받지않는 견실한(robust) 모델 추정 알고리듬이 필요하다. 본 논문에서는 잡음환경하에 있는 시스템을역 모델링하는데 있어서, 잡음의 영향을 줄이기위해 완전최소자승법을 도입하고 기존의 최소자승법과 비교 실험하였다. 그리고, 이 방법의 적응 알고리듬을 제안하였으며, RLS(Recursive least squares)와 그 성능을 비교하여 타당성을 검토하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.937-940
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• 1995

The purpose of this paper is to present a nonlinear system identification method, where an adaptive nonlinear recursive state-spare(ANRSS) filter is employed as its filter structure, and a variable step (VS) algorithm is applied as its adaptation law. To demonstrate the validity of the proposed method, some simulation results are included.

• 산업경영시스템학회지
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• 제17권30호
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• pp.107-116
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• 1994

This paper formulates a dynamic monitoring and control model with a machine state by quality variations in a single lot production system. A monitoring model is based on estimate of machine state obtained using control theory. The model studied in this paper has a great advance from a point of view the combination between quality control (Sampling, Control Chart) and automatic control theory, and can be extended in a several ways.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 추계학술발표논문집(상)
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• pp.489-492
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• 2004

미지의 패턴을 분류하기 위해서 사용되는 메모리 기반 학습 기법은 만족할만한 분류 성능을 보여주고 있다. 하지만 메모리 기반 학습기법은 단순히 패턴과 메모리에 저장된 예제들 간의 거리를 기준으로 분류하므로, 패턴을 분류하는 처리과정을 설명할 수 없다는 문제점을 가지고 있다. 본 논문에서는 RPA(Recursive Partition Averaging) 기법을 이용하여 패턴을 분류하는 과정을 설명할 수 있는 규칙 추출 알고리즘과 또한 일반화 성능을 향상시키기 위하여 규칙의 조건을 확장하는 알고리즘을 제안한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.817-826
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• 1987

An offset-free self-tuning control with pole placement (STCPP) and a recursive parameter estimation with multiple and variable forgetting factors (REWF), together with their application to a real plant, are described. There are two different types of offset-free STCPP; their features are analysed and discussed. REMVF employs as many forgetting factors as parameter estimates. It is suitable when parameters to be estimated are changing at different rates. The offset-free STCPP and REMVF have been successfully applied to a real plant, giving excellent results.