• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.427-430
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• 1989

For on-line ARMA spectral estimation, the fast transversal filter algorithm of extended least squares method(ETS FTF) is presented. The projection operator, a key tool for geometric approach, is used in the derivation of the algorithm. ELS FTF is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each block, and thus it takes 10N+31 MADPR.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1998년도 학술대회
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• pp.33-36
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• 1998

Most image restoration problems are ill-posed and need to e regularized. A difficult task in image regularization is to avoid smoothing of image edges. In this paper, were proposed an edge-preserving image restoration algorithm using block-based edge classification. In order to exploit the local image characteristics, we classify image blocks into edge and no-edge blocks. We then apply an adaptive constrained least squares (CLS) algorithm to eliminate noise around the edges. Experimental results demonstrate that the proposed algorithm can preserve image edges during the regularization process.

• 전자공학회논문지A
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• 제28A권9호
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• pp.671-679
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• 1991

In adaptive antenna, several models are known according to a prior knowledge about jammer signal. Among those, Frost model with contraint is generally used however it has the problem that convergence speed is slow and that stability is not good. To improve such problems, this paper proposes constrained NLMS algorithm using LS method. In addition, the result obtained by applying this algorithm to Duvall antenna model is compared with that of Frost model.

• Structural Monitoring and Maintenance
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• 제5권3호
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• pp.379-390
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• 2018

In this paper, a novel and effective damage diagnosis algorithm is proposed to detect and estimate damage using two stages least squares support vector machine (LS-SVM) and limited number of attached sensors on structures. In the first stage, LS-SVM1 is used to predict the unmeasured mode shapes data based on limited measured modal data and in the second stage, LS-SVM2 is used to predicting the damage location and severity using the complete modal data from the first-stage LS-SVM1. The presented methods are applied to a three story irregular frame and cantilever plate. To investigate the noise effects and modeling errors, two uncertainty levels have been considered. Moreover, the performance of the proposed methods has been verified through using experimental modal data of a mass-stiffness system. The obtained damage identification results show the suitable performance of the proposed damage identification method for structures in spite of different uncertainty levels.

• International Journal of Control, Automation, and Systems
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• 제3권3호
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• pp.486-492
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• 2005

A practical recursive WLS (weighted least squares) algorithm is proposed to estimate relative range using LOS (line-of-sight) information for ASM (anti-ship missile) application. Apart from the previous approaches based on the EKF (extended Kalman filter), to ensure good convergence properties in long range engagement situations, the proposed scheme utilizes LOS rate measurements instead of conventionally used LOS angle measurements. The estimation error property for the proposed filter is investigated and a simple error compensator is devised to enhance its estimation error performances. Simulation results indicate that the proposed filter produces very accurate range estimates with extremely small computations.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.595-601
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• 2003

In this paper we propose an on line training method for classification based on least squares support vector machine. Proposed method enables the computation cost to be reduced and the training to be peformed incrementally, With the incremental formulation of an inverse matrix in optimization problem, current information and new input data can be used for building the new inverse matrix for the estimation of the optimal bias and Lagrange multipliers, so the large scale matrix inversion operation can be avoided. Numerical examples are included which indicate the performance of proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1653-1660
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• 2016

Smoothly clipped absolute deviation (SCAD) penalty is known to satisfy the desirable properties for penalty functions like as unbiasedness, sparsity and continuity. In this paper, we deal with the regression function estimation and variable selection based on SCAD penalized censored regression model. We use the local linear approximation and the iteratively reweighted least squares algorithm to solve SCAD penalized log likelihood function. The proposed method provides an efficient method for variable selection and regression function estimation. The generalized cross validation function is presented for the model selection. Applications of the proposed method are illustrated through the simulated and a real example.

• 대한기계학회논문집
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• 제18권8호
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• pp.1997-2004
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• 1994

A model analysis method has been developed in the paper. The method estimates the modal parameters of multiple input-output systems, assesses their quality, and seperates structural modes form computation ones. The modal parameter extraction algorithm is the least squares method with a finite difference model relating input and output time data. The quality of the estimated system model can be assessed in narrow frequency bands by comparing the measured and model predicted responses in time domain with the aid of digital filters. Structural modes can be effectively separated from computational ones using the convergence factor which represents the pole convergence rate. The modal analysis method has been applied to simulated and experimental vibration data to evaluate its utility and limitations.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.1-8
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Robin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method. A numerical example is also presented to illustrate the method.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.559-562
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• 2011

본 연구에서, 우리는 상호신호 간 간섭 및 부가적인 백색 가우시안 잡음이 존재하는 디지털 펄스폭 변조 시퀀스에 대하여 추정하는 적응 최우 추정 채널로써 회귀 최소 자승 알고리듬을 유도였으며 회귀 최소 자승 알고리듬은 기존의 최소자승 알고리듬보다 수렴특성이 더 좋음을 알 수 있었다.