• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.245-252
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• 2003

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.367-369
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• 2004

In this paper we propose an effective and reliable hand detection method using neural network with ICA(Independent Component Analysis) Features. Many algorithms of hand detection have been proposed yet. Among them, ICA is the one of the interesting topics in image processing. ICA can not only separate mixed signals but also efficiently extract low-dimensional features in signals. ICA features are able to represent the characteristic of the images well. The object of this paper is to use effectively ICA that has above advantage. That is, by the proper number of Independent component the arithmetic speed is faster and by normalization of ICA feature the performance of detection is more reliable. For this, we adopt the algorithm, the Proportion of variance, which select the ICA feature by comparing the ratio of variance of ICA feature. By this method, we can extract the feature that is good at classifying hand and non-hand. Our experimental results show that by using ICA features, we obtained a better performance in hand detection than by only training NN on the image. And we can use hand detection system effectively and reliably by our proposal.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1299-1305
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• 2013

In this paper, we investigate a method of reducing the computational cost in speech recognition by dimensionality reduction of MFCC feature vectors. Eigendecomposition of the feature vectors renders linear transformation of the vectors in such a way that puts the vector components in order of variances. The first component has the largest variance and hence serves as the most important one in relevant pattern classification. Therefore, we might consider a method of reducing the computational cost and achieving no degradation of the recognition performance at the same time by dimensionality reduction through exclusion of the least-variance components. Experimental results show that the MFCC components might be reduced by about half without significant adverse effect on the recognition error rate.

• Journal of Korean Society for Quality Management
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• v.10 no.1
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• pp.7-12
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• 1982

Arvesen (1969) has shown a procedure which produces an approximate confidence interval for a variance component in unbalanced one-way classification model. In this paper, his work is extended to the case when the model of interest is unbalanced two-way classification. Following the procedure described in this paper, approximate confidence intervals are computed by a Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.921-929
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• 2014

A bivariate exponential distribution with a location parameter is proposed as a model for a two-component shared load system with a guarantee time. Some statistical properties of the proposed model are investigated. The maximum likelihood estimators and uniformly minimum variance unbiased estimators of the parameters, mean time to failure, and the reliability function of system are obtained with unknown guarantee time. Simulation studies are given to illustrate the results.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.349-356
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• 2010

The ordinary least squares based estimator of the disturbance variance in a regression model for spatial panel data is shown to be asymptotically unbiased and weakly consistent in the context of SAR(1), SMA(1) and SARMA(1,1)-disturbances when there is measurement error in the regressor matrix.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.61-74
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• 2020

Interval-valued data, one type of symbolic data, are observed in the form of intervals rather than single values. Each interval-valued observation has an internal variation. Principal component analysis reduces the dimension of data by maximizing the variance of data. Therefore, the principal component analysis of the interval-valued data should account for the variance between observations as well as the variation within the observed intervals. In this paper, three principal component analysis methods for interval-valued data are summarized. In addition, a new method using a truncated normal distribution has been proposed instead of a uniform distribution in the conventional quantile method, because we believe think there is more information near the center point of the interval. Each method is compared using simulations and the relevant data set from the OECD. In the case of the quantile method, we draw a scatter plot of the principal component, and then identify the position and distribution of the quantiles by the arrow line representation method.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of Ginseng Research
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• v.35 no.1
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• pp.31-38
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• 2011

In order to distinguish the cultivation area of Panax ginseng, principal component analysis (PCA) using quantitative and qualitative data acquired from HPLC was carried out. A new HPLC method coupled with evaporative light scattering detection (HPLC-ELSD) was developed for the simultaneous quantification of ten major ginsenosides, namely $Rh_1$, $Rg_2$, $Rg_3$, $Rg_1$, Rf, Re, Rd, $Rb_2$, Rc, and $Rb_1$ in the root of P. ginseng C. A. Meyer. Simultaneous separations of these ten ginsenosides were achieved on a carbohydrate analytical column. The mobile phase consisted of acetonitrile-water-isopropanol, and acetonitrile-water-isopropanol using a gradient elution. Distinct differences in qualitative and quantitative characteristics for ginsenosides were found between the ginseng roots produced in two different Korean cultivation areas, Ganghwa and Punggi. The ginsenoside profiles obtained via HPLC analysis were subjected to PCA. PCA score plots using two principal components (PCs) showed good separation for the ginseng roots cultivated in Ganghwa and Punggi. PC1 influenced the separation, capturing 43.6% of the variance, while PC2 affected differentiation, explaining 18.0% of the variance. The highest contribution components were ginsenoside $Rg_3$ for PC1 and ginsenoside Rf for PC2. Particularly, the PCA score plot for the small ginseng roots of six-year old, each of which was light than 147 g fresh weight, showed more distinct discrimination. PC1 influenced the separation between different sample sets, capturing 51.8% of the variance, while PC2 affected differentiation, also explaining 28.0% of the variance. The highest contribution component was ginsenoside Rf for PC1 and ginsenoside $Rg_2$ for PC2. In conclusion, the HPLC-ELSD method using a carbohydrate column allowed for the simultaneous quantification of ten major ginsenosides, and PCA analysis of the ginsenoside peaks shown on the HPLC chromatogram would be a very acceptable strategy for discrimination of the cultivation area of ginseng roots.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.604-608
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• 1997

Ten selected soybean genotypes, consisting of nine from a pedigree breeding programme and one recommended variety, were evaluated in nine different locations and over two years for stability of yield performance. Variance component analysis revealed that soybean regional yield trials should be performed at more locations rather than in more years. Five stability parameters, which were coefficient of variability, regression coefficient, deviation parameter, variance component for genotype$\times$environment interaction, and ecovalence, were employed in the evaluation. Significant genotype$\times$environment interaction was present with respect to soybean yield. The highest average yield over nine locations and two years was shown in Suwon 145, which was considered to be stable in all stability statistics. In rank correlation among stability parameters, there were highly significant correlations among stability parameters derived from three Eberhart and Russell's, Plaisted's, and Wricke's methods. Due to the different ranking of genotypes by different stability parameters, a comprehensive method should be employed to identify the promising genotype as well as to characterize the relationship between genotype and environment.