• 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.

• Water for future
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• v.23 no.1
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• pp.79-87
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• 1990

In this study, a regression model, which relates annual flood peak flows collected at stramflow gaging stations in the Han river and Nakdong river basin to both basin characteristics and precipitation data, is developed by using the generalized least squares method which can provide reasonable and unbiased estimator of error variance by separating error variance of the regression model into that due to model error and due to sampling error. This model may be used as a mechanism for transferring hydrologic information from the gaged sites to ungaged sites.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.206-211
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• 2009

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPP$_{n,m}$(Current Price Position of the day n: a percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPP$_{n,m}$ are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

• The Journal of the Acoustical Society of Korea
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• v.29 no.6
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• pp.374-380
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• 2010

It is known that the problem of FIR filtering with noisy input and output data can be solved by a total least squares (TLS) estimation. It is also known that the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose a convex combination algorithm between the ordinary recursive LS based TLS (RTLS) and the ordinary recursive LS (RLS). This combined algorithm is robust to the noise variance ratio and has almost the same complexity as the RTLS. Simulation results show that the proposed algorithm performs near TLS in noise variance ratio ${\gamma}{\approx}1$ and that it outperforms TLS and LS in the rage of 2 < $\gamma$ < 20. Consequently, the practical workability of the TLS method applied to noisy data has been significantly broadened.

• Geotechnical Engineering
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• v.4 no.3
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• pp.19-26
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• 1988

A stochastic numerical model for predictions of differential settlement of foundation Eoils is developed in this Paper. The differential settlement is highly dependent on the spatial variability of elastic modulus of soil. The Kriging method is used to account for the spatial variability of the elastic modulus. This technique provides the best linear unbiased estimator of a parameter and its minimum variance from a limited number of measured data. The stochastic finite element method, employing the first-order second-moment analysis for computations of error Propagation, is used to obtain the means, ariances, and covariances of nodal displacements. Finally, a reliability model of differential settlement is proposed by using the results of the stochastic FEM analysis. It is found that maximum differential settlement occurs when the distance between two foundations is approximately same It with the scale of fluctuation in horizontal direction, and the probability that differential settlement exceeds the allot.able vague might be significant.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.129-135
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• 2008

This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.87-95
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• 1998

This paper presents the method for the automatic tuning of a design weighting polynomial parameter of a generalized minimum-variance stochastic self tuning controller which adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design weighting polynomial parameters. The proposed self tuning method is simple and effective compared with other existing self tuning methods. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Korean Journal of Optics and Photonics
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• v.7 no.4
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• pp.348-356
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• 1996

A mobile CARS spectrometer is constructed to measure the instantaneous temperature of gases, of which software include the quick fit methods and a least square fitting method to obtain temperatures from the spectra. Two quick-fit-methods give smaller variance of temperatures than the least square fitting method even though they consume much shorter time to yield temperatures. The precision and accuracy of CARS temperature is measured in the graphite tube blackbody furnace in reference to a radiation pyrometer. The accuracy of the CARS temperature is $\pm$2% from 1000K to 2400K and the precision is $\pm$35K at 1600K with the most accurate quick-fit-method. As a demonstration of the instantaneous measurement, the spectrometer is applied for measurement of the turbulent combustion at a certain condition. eograms(HS) are made using a relatively small number of synthesized 2D images. The influence of aliasing artifacts caused by insufficient or improper sampling is presented, and a new sampling theory is proposed, which is used to making holographic stereograms. Also, the optical system for extension of viewing distance and viewing zone is proposed. Results of this analysis can be applied to design normal holographic stereograms and computer based holographic stereograms.

• Korean Journal of Weed Science
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• v.17 no.3
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• pp.303-309
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• 1997

Variation of morphological and physiological traits of 50 Bidens tripartita accessions were studied and the accessions were grouped through cluster analysis based on four major characters; plant type, leaf partition, achene length, days to flowering. Bidens tripartite accessions have shown significant variations in plant type, stem length, days to flowering, leaf shape, leaf partition, chlorophyll content, leaf color, stem color, achene color, achene length and achene shape. Most of Bidens tripartite accessions appeared to have strong dormancy and also photodormancy with some exceptions. Plants could be classified into 5 types from straight(I) to triangle(V), and intermediate diamond type(III) was prevalent. The plant type score has negative correlation with the stem length. None, three, and five part leaved plants were observed and most of them were three or five parted. Leaf partition had negative correlation with achene length and chlorophyll content. Average days to flowering was 108 days in the range of 94~141 days. It had positive correlation with achene length and leaf shape and negative correlation with achene color. Average achene length was 10.0mm and it had positive correlation with achene shape, stem length, days to flowering and leaf shape. It also had negative correlation with leaf color, stem color, achene color, leaf partition. Bidens tripartite accessions could be divided into identifiable six groups from the cluster analysis at the distance 0.06 using Ward's minimum-variance method.