• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.219-224
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• 2020

Binary classification has been broadly investigated in machine learning. In addition, binary classification can be easily extended to multi class problems. To successfully utilize machine learning methods for classification tasks, preprocessing and feature extraction steps are essential. These are important steps to improve their classification performances. In this paper, we propose a new learning method based on weighted least squares. In the weighted least squares, designing weights has a significant role. Due to this necessity, we also propose a new technique to obtain weights that can achieve feature transformation. Based on this weighting technique, we also propose a method to combine the learning and feature extraction processes together to perform both processes simultaneously in one step. The proposed method shows the promising performance on five UCI machine learning data sets.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.53-58
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• 2012

In this paper, we evaluate and analyze representative estimation methods for the sparse channel. In order to evaluate error performance of matching pursuit(MP) and minimum mean square error(MMSE) algorithm, lower bound of MMSE is determined by Cramer-Rao bound and compared with upper bound of MP. Based on analysis of those bounds, mean square error of MP which is effective in the estimation of sparse channel can be larger than that of MMSE according to the number of estimated tap and signal-to-noise ratio. Simulation results show that the performances of both algorithm are reversed on the sparse channel with Rayleigh fading according to signal-to-noise ratio.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.4
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• pp.283-289
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• 2014

This study was conducted to develop a non-destructive detection method for adulterated powder products using Raman spectroscopy and partial least squares regression(PLSR). Garlic and ginger powder, which are used as natural seasoning and in health supplement foods, were selected for this experiment. Samples were adulterated with corn starch in concentrations of 5-35%. PLSR models for adulterated garlic and ginger powders were developed and their performances evaluated using cross validation. The $R^2_c$ and SEC of an optimal PLSR model were 0.99 and 2.16 for the garlic powder samples, and 0.99 and 0.84 for the ginger samples, respectively. The variable importance in projection (VIP) score is a useful and simple tool for the evaluation of the importance of each variable in a PLSR model. After the VIP scores were taken pre-selection, the Raman spectrum data was reduced by one third. New PLSR models, based on a reduced number of wavelengths selected by the VIP scores technique, gave good predictions for the adulterated garlic and ginger powder samples.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.687-693
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• 2015

Using 378,086 lactation records on dairy cattle, environmental factors influencing acetone and ${\beta}$-hydroxybutyrate acid contents in raw milk which are used as ketosis diagnosis indicator traits were analyzed in this experiment. Significance testing was conducted on farm, lactation stage, parity, milking time and month of age by traits. The results of this experiment indicated that there was a highly significant (p < 0.01) difference in all factors and lactation stage was the most significant factor. Linear regression coefficients of month of age on daily milk yields and acetone and ${\beta}$-hydroxybutyrate acid contents were all positive, while their quadratic linear regression coefficients were negative. Least square means for milk yield at second lactation stage (36~65 days) was 19.06kg which was higher than that of late lactation stage by 6.51kg. Least square means for acetone and ${\beta}$-hydroxybutyrate acid contents at the first lactation stage (5~35 days) were highest (0.1929mM/L and 0.0742mM/L, respectively), and there was a trend that they decreased as the milking progressed, but increased slightly at the late stage of milking. However, least square means for acetone and ${\beta}$-hydroxybutyrate acid contents at the first parity were 0.1414mM/L and 0.0522mM/L, respectively, which were higher than the average milk yield after the second parity. Least square means for acetone and ${\beta}$-hydroxybutyrate acid contents of PM milk yield (0.1372mM/L and 0.0534mM/L, respectively) were higher than those of AM milk yield collectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.10
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• pp.2303-2309
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• 1997

This paper presents a convergence analysis of the stocastic gradient adaptive algorithm based on the least mean absolute third (LMAT) error criteriohn. Under the assumption that the signals involved are zero-mean, wide-sense sateionaryand gaussian, a set of nonlinear difference equations that characterizes the mean and mean-squared behavior of the algorithm is derived. Computer simulation resutls show fairly good agreements between the theoetical and empirical behaviors of the algorithm.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.1-6
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• 2003

This paper analyses the performance of either the minimum variance distortionless response (MVDR) or the recursive least square (RLS) beamformer structured on the systolic array. Provided that the snapshot vector including the desired user's signal and the interferences with the noise is received at the array antenna. In order to improve the quality of received signal, MVDR or RLS algorithm can be utilized to update the beamformer weights recursively. Furthermore to increase the channel capacity, by the usage of the above schemes, the effect of the spatial filtering can be obtained which constructively combining multipath components corresponding to the desired user whereas the multiple access interferences (MAI) is nulled out on spatial domain. This paper introduces the MVDR and RLS beamformer structured on systolic array conducting the spatial filtering, and its performance under the multipath fading channel in the presence of multiple access interferences will be analyzed. To show the superior spatial filtering performances of the proposed scheme employing the systolic way structured beamformer, the computer simulations are carried out. And the validity of practical deployment of the proposed scheme will be confirmed throughout showing the BER behaviors and the beampatterns.

• Sleep Medicine and Psychophysiology
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• v.6 no.1
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• pp.52-60
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• 1999

Objectives: With the object of finding the appropriate conditions and algorithms for dimensional analysis of human EEG, we calculated correlation dimensions in the various condition of sampling rate and data aquisition time and improved the computation algorithm by taking advantage of bit operation instead of log operation. Methods: EEG signals from 13 scalp lead of a man were digitized with A-D converter under the condition of 12 bit resolution and 1000 Hertz of sampling rate during 32 seconds. From the original data, we made 15 time series data which have different sampling rate of 62.5, 125, 250, 500, 1000 hertz and data acqusition time of 10, 20, 30 second, respectively. New algorithm to shorten the calculation time using bit operation and the Least Trimmed Squares(LTS) estimator to get the optimal slope was applied to these data. Results: The values of the correlation dimension showed the increasing pattern as the data acquisition time becomes longer. The data with sampling rate of 62.5 Hz showed the highest value of correlation dimension regardless of sampling time but the correlation dimension at other sampling rates revealed similar values. The computation with bit operation instead of log operation had a statistically significant effect of shortening of calculation time and LTS method estimated more stably the slope of correlation dimension than the Least Squares estimator. Conclusion: The bit operation and LTS methods were successfully utilized to time-saving and efficient calculation of correlation dimension. In addition, time series of 20-sec length with sampling rate of 125 Hz was adequate to estimate the dimensional complexity of human EEG.

• Journal of KSNVE
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• v.4 no.1
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• pp.59-69
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• 1994

A new LMS algorithm titled constrained LMS' is proposed for problems with constrained structure. The conventional LMS algorithm can not be used because it destroys the constrained structures of the weights or parameters. Proposed method uses error-back propagation, which is popular in training neural networks, for error minimization. The illustrative examplesare shown to demonstrate the applicability of the proposed algorithm.

• 한국항공운항학회:학술대회논문집
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• 2016.05a
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• pp.26-29
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• 2016

본 논문에서는 멀티로터 시스템의 모델리을 위한 방법으로 파라미터 추정법을 제시하였으며 이를 위해 실제 비행데이터를 이용한다. 파라미터 추정법으로는 예측오차 기법과 순화최소자승법이 사용되었고 그 결과를 나타내었다.