• The Journal of the Acoustical Society of Korea
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• v.16 no.3
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• pp.38-44
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• 1997

In this study, wer derived LMF-Sato, LMSF-Sato complex blind equalization algorithms for complex data. And then, the convergence rates, the convergence characteristics at the steady state and the stability of the proposed LMF and LMSF blind equalization algorithms are compared with those of LMS-Sato blind equalization algorithm. In simulations with 16-QAM data, LMF-Sato and LMSF-Sato algorithms showed better performance comparing with LMS-Sato algorithm generally. When the initial estimation errors of the weights of the equalizer are large, LMF-Sato algorithm showed ill characteristic in stability. However, LMSF-Sato algorithm has good covergence characteristics and preserves robustness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.1
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• pp.1-9
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• 1987

This paper is mainly concerned with the analysis of post-adjustment techniques for the outliers identification and reliabilities in geodetic networks. The proposed strategy which easily attached to a least squares adjustment program, is successfully used in a test network. It shows that their application can considerably improve the quality of the results, but the main advantage appears when their reliability is considered.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.3
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• pp.237-244
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• 2001

On this study, a 50-years-old earth dam was measured by the static method of GPS for deformation monitoring. The reference network was measured by the vector between points in twice times and the monitored points were observed in four times at test field, i.e. an embankment which was restored by mortar, In addition, gross errors in the measurement were estimated and eliminated by data snooping method and random errors were adjusted by least square method. Finally, the amount of displacement was estimated from variance-covariance matrix. Also, precision of points were showed by the confidence ellipse(95%), and the amount of displacement was figured.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.511-520
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• 2013

Detection outliers and robust estimators are crucial in regression models with outliers. In such studies the focus is on detecting outliers and estimating the coefficients using leave-one-out. Our study introduces Rice estimator which is an error variance estimator without estimating the coefficients. In particular, we study a comparison of the statistical properties for Rice estimator with and without outliers in simple regression models.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.505-506
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• 2007

일반적으로 저가 장비를 이용한 수신 신호 강도(RSSI)의 측정은 전파의 특성상 다소 부정확한 정보를 제공하고, 이는 최소평균제곱오차(MMSE)를 이용한 위치 추정 방법에 있어 큰 오차 요인으로 작용한다. 따라서 이 논문에서는 수신 신호 강도를 이용한 기존의 위치 추정 방법을 개선하기 위해 센서 네트워크상의 유효 노드선정 알고리즘을 제시한다. 그리고 개선된 방법을 이용하여 센서 네트워크 기반의 실내 위치 추정 시스템을 구현 한다. 끝으로, 개선된 방법의 성능 검증을 위한 실험 결과를 제시 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.621-628
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• 2018

More than four visible GPS (Global Positioning System) satellites are required to obtain absolute positioning. However, it is not easy to satisfy this condition when a rover is in such unfavorable condition as an urban area. As a consequence, clock-aided positioning has been used as an alternative method especially when the number of visible satellites is three providing that receive clock error information is available. In this study, LSC (Least-Squares Collocation) method is proposed to interpolate clock errors for clock-aided positioning after analyzing the characteristics of receiver clock errors. Numerical tests are performed by using GPS data collected at one of Korean CORS (Continuously Operating Reference Station) and a nearby GPS station. The receiver clock errors are obtained through the DGPS (Differential GPS) positioning technique and segmentation procedures are applied for efficient interpolation. Then, LSC is applied to predicted clock error at epoch which clock information is not available. The numerical test results are analyzed by examining the differences between the original and interpolated clock errors. The mean and standard deviation of the residuals are 0.24m and 0.49m, respectively. Therefore, it can be concluded that sufficient accuracy can be obtained by using the proposed method in this study.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.737-752
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• 2008

About 1800, mathematicians combined analysis of error and probability theory into error theory. After developed by Gauss and Laplace, error theory was widely used in branches of natural science. Motivated by the successful applications of error theory in natural sciences, scientists like Adolph Quetelet tried to incorporate social statistics with error theory. But there were not a few differences between social science and natural science. In this paper we discussed topics raised then. The problems considered are as follows: the interpretation of individual man in society; the arguments against statistical methods; history of the measures for diversity. From the successes and failures of the $19^{th}$ century social statisticians, we can see how statistics became a science that is essential to both natural and social sciences. And we can see that those problems, which were not easy to solve for the $19^{th}$ century social statisticians, matter today too.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.10B
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• pp.1210-1215
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• 2011

In this paper, a supervised adaptive equalization algorithm based on probability density function (PDF) matching method is introduced and its decision-feedback version is proposed for underwater communication channels with strong impulsive noise and severe multipath characteristics. The conventional least mean square (LMS) algorithm based on mean squared error (MSE) criterion has shown to be incapable of coping with impulsive noise and multipath effects commonly shown in underwater communications. The linear PDF matching algorithm, which shows immunity to impulsive noise, however, has revealed to yield unsatisfying performance under severe multipath environments with impulsive noise. On the other hand, the proposed nonlinear PDF matching algorithm with decision feedback proves in the simulation to possess superior robustness against impulsive noise and multipath characteristics of underwater communication channels.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.471-485
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• 2016

Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.967-975
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• 2010

Since the least squares estimation is not appropriate when multicollinearity exists among the regressors of the linear regression model, the principal components regression is used to deal with the multicollinearity problem. This article suggests a new procedure for the selection of suitable principal components. The procedure is based on the condition index instead of the eigenvalue. The principal components corresponding to the indices are removed from the model if any condition indices are larger than the upper limit of the cutoff value. On the other hand, the corresponding principal components are included if any condition indices are smaller than the lower limit. The forward inclusion method is employed to select proper principal components if any condition indices are between the upper limit and the lower limit. The limits are obtained from the linear model which is constructed on the basis of the conjoint analysis. The procedure is evaluated by Monte Carlo simulation in terms of the mean square error of estimator. The simulation results indicate that the proposed procedure is superior to the existing methods.