• 한국통신학회논문지
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• 제35권9C호
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• pp.735-742
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• 2010

본 논문에서는 다수의 송신 안테나와 수신 안테나를 갖는 RF 중계기를 위한 적응 간섭제거 알고리즘을 제안한다. 중계기에서 다수의 안테나를 사용하는 경우 송신 안테나와 수신 안테나간의 불완전한 격리로 인해 간섭이 발생하고, 궤환 간섭 경로는 MIMO (multi-input multi-output) 채널로 모델링된다. 이런 궤환 간섭을 제거하기 위해 적응 신호처리 기법에 기반하여 LMS (least mean square) 간섭제거 알고리즘과 RLS (recursive least squares) 간섭제거 알고리즘을 유도한다. 모의 실험을 통해 제안된 간섭 제거 알고리즘의 수렴 특성 및 정상 상태에서의 간섭 제거 성능을 비교한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(1)
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• pp.45-48
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• 2000

The use of multi-level modulation scheme in the wireless LAN(Local Area Networks) system requires an accurate channel estimation. In this paper, we present sequential least squares(LS) channel estimation scheme based on decision-directed channel tracking scheme. The proposed scheme improves the performance of the conventional LS estimator for wireless LAN. In addition, its structure is suitable for the high-rate wireless LAN. Simulation results show that the proposed scheme achieves about IdB Packet Error Rate(PER) gain compared to the LS scheme in a frequency selective channel.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.401-409
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• 2006

We consider a generalized partly-parametric additive risk model which generalizes the partly parametric additive risk model suggested by McKeague and Sasieni (1994). As an estimation method of this model, we propose to use the weighted least square estimation, suggested by Huffer and McKeague (1991), for Aalen's additive risk model by a piecewise constant risk. We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least squares method.

• 응용통계연구
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• 제6권1호
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• pp.79-93
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• 1993

회귀모형에 있어서의 Ruppert와 Carroll의 절사 회귀 추정법을 확장하여 회귀 분위수에 의 한 두 개의 두분으로 관측치를 분할하여 각 부분마다 가중치를 달리 부여하는 방법으로 적 합된 L-추정법을 제안하였다. 이 제안된 L-추정법은 특히 비대칭인 오차분포하에서 좋은 효율을 가지고 있었다.

• Journal of the Korean Data and Information Science Society
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• 제22권3호
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• 전자공학회논문지B
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• 제31B권10호
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• pp.32-43
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• 1994

This paper presents new QRD-based recursive least squares algorithms for bilinear lattice filter. Bilinear recursive least square lattice algorithms are derived by using the QR decomposition for minimization covariance matrix of predication error by applying Givens rotation to the bilinear recursive least squares lattics algorithms. The proposed algorithms are applied to the bilinear system identification to evaluate the performance of algoithms. Computer simulations show that the convergence properties of the proposed algorithms are superior to that of the algorithms proposed by Baik when signal includes the measurement noise.

• 한국시뮬레이션학회논문지
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• 제9권3호
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• pp.43-51
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• 2000

In regression model, we estimate the unknown parameters by using various methods. There are the least squares method which is the most general, the least absolute deviation method, the regression quantile method and the asymmetric least squares method. In this paper, we will compare each others with two cases: firstly the theoretical comparison in the asymptotic sense and then the practical comparison using Monte Carlo simulation for a small sample size.

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.571-575
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• 2003

In this paper, we describe a method for fuzzy polynomial regression analysis for fuzzy input--output data using shape preserving operations for least-squares fitting. Shape preserving operations simplifies the computation of fuzzy arithmetic operations. We derive the solution using mixed nonlinear program.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2000년도 추계학술대회 논문집
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• pp.6-10
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• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• 대한수학회논문집
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• 제18권4호
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• pp.703-712
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• 2003

This paper deals with the strong consistency of the asymmetric least squares for the nonlinear censored regression models which includes dependent variables cut off midway by any of external conditions, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. One example is given to illustrate the application of the main result.