• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.520-525
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• 2009

The conventional studies about an adaptive beamformer assumed that the interference signals are stationary, so they used time-average of signals or Least Mean Squares. However, these methods showed low performance of canceling the non-stationary interferences. In this paper, the MAFF-RLS algorithm is developed in order to cancel non-stationary interferences, and the GSC structure using this algorithm is proposed. Furthermore, the performance of the MAFF-RLS beamformer is verified by simulation using MATLAB. This simulation results show the performance of the proposed beamformer is better than that of the SMI and the conventional RLS beamformer.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.283-292
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• 1992

Updating algorithms are studied for the basic statistics (mean, variance). For a linear model, a recursive formulae for least squares estimators of regression coefficients, residual sum of squares and variance-covariance matrix are also studied. Hotelling's $T^2$ statistics can be calculated recursively using the recursive formulae of mean vector and variance-covariance matrix without computing the sample variance-covariance matrix at each stage.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.31-39
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• 2001

For estimating parameters of possibly nonlinear and/or non-stationary seasonal autoregressive(AR) processes, we introduce a new instrumental variable method which use the direction vector of the regressors in the same period as an instrument. On the basis of the new estimator, we propose new seasonal random walk tests whose limiting null distributions are standard normal regardless of the period of seasonality and types of mean adjustments. Monte-Carlo simulation shows that he powers of he proposed tests are better than those of the tests based on ordinary least squares estimator(OLSE).

• International Journal of Quality Innovation
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• v.1 no.1
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• pp.32-37
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• 2000

In an industrial process, the proper objective is to find the optimal operating conditions with minimum process variability around the target. Vining and Myers(1990) suggest to use the separate model for the mean response and the process varian linear predictor ${\tau}_i={\log}\;{\sigma}^2_i$ is unknown and should be estimated. Noting that the variance of $\hat{{\tau}_i}$ is heterogeneous, another appropriate D-optimality criterion $D_3$ based on the method of generalized least squares is proposed in this paper.

• Journal of Biomedical Engineering Research
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• v.13 no.2
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• pp.107-114
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• 1992

An adaptive filter for noise cancellation of ECG Is proposed. An adaptive noise canceller using the least mean squares algorithm Is used to reduce unwanted noise. An adaptive filter for nolse cancella lion minimizes the mean-square error between a primary input and a reference input. A primary input is the noisy ECG, and a reference input is a noise that Is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.447-456
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• 2007

On the basis of marginal likelihood of the residual vector which is free of nuisance mean parameters, we propose new Lagrange Multiplier seasonal unit root tests in seasonal autoregressive process. The limiting null distribution of the tests is the standardized ${\chi}^2-distribution$. A Monte-Carlo simulation shows the new tests are more powerful than the tests based on the ordinary least squares (OLS) estimator, especially for large number of seasons and short time spans.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.117-130
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• 2015

A least squares method for adjusting the horizontal network satisfies the conditions which is minimizing the sum of the squares of errors based on probability theory. This research compared accuracy of 3rd cadastral control points adjusted by traditional and least square method with respect to the result of Network-RTK. Test results showed the least square method more evenly distribute closure error than traditional method. Mean errors of least square and traditional adjusting method are 2.7cm, 2.2cm respectively. In addition, blunder in angle observations can be detected by comparing position errors which calculated by forward and backward initial coordinates. However, distance blunder cannot offer specific observation line occurred mistake because distance error propagates several observation lines which have similar directions.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.399-402
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• 2000

In this paper, we present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between- channel deterministic information is considered. For each channel a functional which combines the least mean squares (LMS), the least mean fourth(LMF), and a smoothing functional is proposed, We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters mentioned above are adjusted based on the partially restored image.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.11
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• pp.1555-1559
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• 2003

The objective of the present study was to explore associations between five microsatellites linked to $Fec^B$ and $FecX^I$ genes and litter size in Small Tail Han sheep. The polymorphisms of five microsatellite loci, OarAE101, BM1329, BMS2508, TGLA54 and TGLA68 were detected in 244 ewes of Small Tail Han sheep. Analysis of association between three microsatellite loci (BMS2508, BM1329 and OarAE101) located in the 10 cM region covering the $Fec^B$ gene (Booroola gene) and litter size in Small Tail Han sheep indicated that BMS2508 had significant effect on litter size in the second parity (p<0.05), but no significant effect on litter size in the first parity (p>0.05), while the other two microsatellite loci had no significant effect on litter size in both the first and the second parity in Small Tail Han sheep (p>0.05). At microsatellite locus BMS2508, least squares means in the second parity of genotypes 101/111 and 99/109 were significantly higher than those of genotypes 99/99, 99/101, 99/111 and 99/115 (p<0.05); least squares mean in the second parity of genotype 101/111 was significantly higher than that of genotypes 109/111 and 111/111 (p<0.05). Results of this study also indicated that two microsatellite loci (TGLA54 and TGLA68) that confined the 28.7 cM region covering the $FecX^I$ gene (Inverdale gene) did not affect litter size in both the first and the second parity in Small Tail Han sheep significantly (p>0.05). The information found in the present study is very important for improving the reproductive performance in sheep breeds by marker assisted selection.

• Korean Journal of Metals and Materials
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• v.56 no.11
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• pp.813-821
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• 2018

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.