• Journal of the Korean Statistical Society
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• v.15 no.2
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• pp.118-126
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• 1986

The relationships between combined estimators and generalized least squares estimators in block designs are reviewed. Here combined estimators mean the best linear combination of intrablock and interblock estimaters. It is well known that only for balanced incomplete block designs the combined estimators of Yates and of the generalized least squares estimators give the same result. In this paper, a general form of the combined estimators for treatment effects is derived and it can be seen that such estimators are equivalent to the generalized least squares estimators.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.655-658
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• 1999

In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.105-117
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• 2004

In this paper, a new method is developed for estimating the mean of a population which has a linear trend. This method involves drawing a sample by the modified systematic sampling, and then estimating the population mean with an adjusted estimator, not with the sample mean itself. We use the method of least squares in determining the adjusted estimator. The proposed method is shown to be more and more efficient as the linear trend becomes stronger. It turns out to be relatively efficient as compared with the conventional methods if $\sigma$$^2$the variance of the random error term in the infinite superpopulation model, is not very large.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1173-1177
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• 2015

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.3
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• pp.312-319
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• 2011

In this paper, a new linearization algorithm for DPD(Digital PreDistorter) is suggested. This new algorithm uses DFP(Davidon-Fletcher-Powell) method. This algorithm is more accurate than that of the existing algorithms, and this method renew the best-fit value in every routine with out setting the initial value of step-size. In modeling power amplifier, the memory polynomial model which can model the memory effect of the power amplifier is used. And the overall structure of linearizer is based on an indirect learning architecture. In order to verify for performance of proposed algorithm, we compared with LMS(Least Mean-Squares), RLS(Recursive Least squares) algorithm.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.167-170
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• 1998

본 논문에서는 주파수 영역에서 구현되는 안테나 배열을 이용한 적응형 등화기의 구조를 제안하였다. 제안된 방식은 주파수 영역에서 블록 LMS (least mean squares) 방식을 사용하기 때문에 수렴 속도가 빠르고 계산량이 적다. 또한 안테나 배열을 사용함으로서 다중경로 환경에서 한 개의 안테나를 사용한 경우에 비해 특히 우수한 성능을 지닌다. 다중 경로를 통한 신호들을 분리하기 위해 학습신호를 사용하여 반복적으로 출력신호와의 오차를 최소화하는 방법을 사용하였다. 시뮬레이션을 통하여 안테나의 개수, 신호의 입사각, 세기 등에 따른 성능을 분석하고, 제안된 방식이 다이버시티 시스템에 사용되는 경우에 대하여도 성능을 분석하였다.

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.903-908
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• 2009

The shrink parameter in ridge regression may be contaminated by outlying points. We propose robust cross validation scores in ridge regression instead of classical cross validation. We use robust location estimators such as median, least trimmed squares, absolute mean for robust cross validation scores. The robust scores have global robustness. Simulations are performed to show the effectiveness of the proposed estimators.

• 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.

• Near Infrared Analysis
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• v.1 no.2
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• pp.1-8
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• 2000

Using the net analyte signal, hybrid linear analysis was proposed to predict chemical concentration. In this paper, we select a sample from training set and apply orthogonal signal correction to obtain an improved pseudo unit spectrum for hybrid least analysis. using the mean spectrum of a calibration training set, we first show the calibration by hybrid least analysis is effective to the prediction of not only chemical concentrations but also physical property variables. Then, a pseudo unit spectrum from a training set is also tested with and without orthogonal signal correction. We use two data sets, one including five chemical concentrations and the other including ten physical property variables, to compare the performance of partial least squares and modified hybrid least analysis calibration methods. The results show that the hybrid least analysis with a selected training spectrum instead of well-measured pure spectrum still gives good performances, which is a little better than partial least squares.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.523-530
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• 2016

Most classical control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To address this issue, a variety of statistical approaches has been employed to estimate the serial structure of the process. In this paper, we propose a multioutput least squares support vector regression and apply it to construct a residual multivariate cumulative sum control chart for detecting changes in the process mean vector. Numerical studies demonstrate that the proposed multioutput least squares support vector regression based control chart provides more satisfying results in detecting small shifts in the process mean vector.