• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.821-826
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• 2003

In this paper, constrained Remez exchange algorithm is proposed to reduce the critical path of an IIR filter. The proposed algorithm is based on Remez exchange algorithm and least squares method. By IIR filter design examples, it is shown that the proposed method can maximally increase speed by 20%.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1456-1467
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• 1990

The time domain adaptive noise canceller (time domain ANC) with the adaptive compensator and its algorithm, so called compensated least mean squares(CLMS) algorithm, had been introduced to improve the performance of ANC[1]. In this paper the time domain ANC with the adaptive compensator is transformed into the frequency domain ANC with the adaptive ocmpensator. An compensated frequency-domain least mean squares(CFLMS) algorithm that can adapt the proposed frequency domain ANC is presented.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.501-506
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• 2007

A generalized finite difference method for solving solid mechanics problems such as elasticity and crack problems is presented. The method is constructed in framework of Taylor polynomial based on the Moving Least Squares method and collocation scheme based on the diffuse derivative approximation. The governing equations are discretized into the difference equations and the nodal solutions are obtained by solving the system of equations. Numerical examples successfully demonstrate the robustness and efficiency of the proposed method.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.125-133
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• 1993

A statistical method is described for estimation of the unknown constants in a theory using both of the computer simulation data and the real experimental data, The best linear unbiased predictor based on a spatial linear model is fitted from the computer simulation data alone. Then nonlinear least squares estimation method is applied to the real experimental data using the fitted prediction model as if it were the true simulation model. An application to the computational nuclear fusion devices is presented, where the nonlinear least squares estimates of four transport coefficients of the theoretical nuclear fusion model are obtained.

• East Asian mathematical journal
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• v.14 no.2
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• pp.399-410
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• 1998

In [3], Franca and Stenberg developed several Galerkin least squares methods for the solution of the problem of linear elasticity. That work concerned itself only with the error estimates of the method. It did not address the related problem of finding effective methods for the solution of the associated-linear systems. In this work, we present computational experiments of W-cycle multigrid method. Computational experiments show that the convergence is uniform as the parameter, $\nu$, goes to 1/2.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.207-214
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• 1999

Optimal estimating functions for a class of autoregressive models with GARCH(1,1) error are discussed. The asymptotic properties of the estimator as the solution of the optimal estimating equation are investigated for the models. We have also some simulation results which suggest that the proposed optimal estimators have smaller sample variances than those of the Conditional least-squares estimators under the heavy-tailed error distributions.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.193-198
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• 1999

The estimation of parameters in regression models with multiplicative errors is usually based on the gamma or log-normal likelihoods. Under reciprocal misspecification, we compare the small sample efficiencies of two sets of estimators via a Monte Carlo study. We further consider the case where the errors are a random sample from a Weibull distribution. We compute the asymptotic relative efficiency of quasi-likelihood estimators on the original scale to least squares estimators on the log-transformed scale and perform a Monte Carlo study to compare the small sample performances of quasi-likelihood and least squares estimators.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.307-316
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• 2004

Principal Component Regression(PCR) and Partial Least Squares Regression(PLSR) are the two most popular regression techniques in chemometrics. In the field of chemometrics usually the number of regressor variables greatly exceeds the number of observation. So we have to reduce the number of regressors to avoid the identifiability problem. In this paper we compare PCR and PLSR techniques combined with various robust regression methods including regression depth estimation. We compare the efficiency, goodness-of-fit and robustness of each estimators under several contamination schemes.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.61-64
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• 2002

In this paper, various equalizers are considered to improve the performance of Home Phoneline Networking Alliance (HomePNA) 2.0 system under dispersive channel with intersymbol interference. We evaluate and compare the performances of Recursive Least Squares (RLS) and Least Mean Squares (LMS) adaptation algorithms. Computer simulations show that the equalizers utilizing tile RLS algorithm outperforms the LMS algorithm, especially for the system of high symbol rate and complex constellation.