• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.41-50
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• 2002

We provide local and semilocal theorems for the convergence of Newton-like methods to a locally unique solution of an equation in a Banach space. The analytic property of the operator involved replaces the usual domain condition for Newton-like methods. In the case of the local results we show that the radius of convergence can be enlarged. A numerical example is given to justify our claim . This observation is important and finds applications in steplength selection in predictor-corrector continuation procedures.

• Journal of the Korean Statistical Society
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• v.9 no.1
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• pp.61-77
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• 1980

In response surface experiments, a polynomial model is often used to fit the response surface by the method of least squares. However, if the vectors of predictor variables are multicollinear, least squares estimates of the regression parameters have a high probability of being unsatisfactory. Hoerland Kennard have demonstrated that these undesirable effects of multicollinearity can be reduced by using "ridge" estimates in place of the least squares estimates. Ridge regrssion theory in literature has been mainly concerned with selection of k for the first order polynomial regression model and the precision of $\hat{\beta}(k)$, the ridge estimator of regression parameters. The problem considered in this paper is that of selecting k of ridge regression for a given polynomial regression model with an arbitrary order. A criterion is proposed for selection of k in the context of integrated mean square error of fitted responses, and illustrated with an example. Also, a type of admissibility condition is established and proved for the propose criterion.criterion.

• Journal of the Korea Fashion and Costume Design Association
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• v.13 no.4
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• pp.139-151
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• 2011

Since personality lead to relatively consistent responses to one's own environment, consumers' distinct personality influences their buying behavior. In order to understand the relationship between consumer's personal characteristics and purchase behavior, the study investigated the effect of consumers' personality trait on the clothing selection criteria and preferred clothing image. Survey was utilized to collect the data and subjects were 333 high school students. Measures consisted of three main constructs: Consumer's extroversion and openness based on the Big-Five personality trait, clothing selection criteria, and preferred clothing image. The data were analyzed using PRELIS 2, LISREL 8.8, and SAS 9.2. The subjects was classified into three groups; Group 1 was a group of intermediate-level in openness and extroversion while Group 2 was a group of high-level in both personality traits. Group 3 was a group of high-level in openness but of low-level in extroversion. Clothing selection criteria were confirmed to have five constructs: other-directed, aesthetic, fashion & conformity-oriented, brand-oriented, and practical. In the buying situation, Group 1 prioritized fashion & conformity-oriented and brand-oriented criteria but regarded other-directed and aesthetic as less important than other groups did. Group 2 considered that all of the clothing selection criteria were important except practical. "The were six factors in the clothing image: elegance, simple, ethnic, masculine, active, and modem. The result showed a significant difference between groups in preferred clothing images. Group 2 liked most of the clothing images but group 3 did not. Group 3 preferred simple clothing image more than masculine or ethnic ones. Overall, the study concluded that the openness and extroversion of Big-Five personality traits could serve as a predictor of clothing selection criteria and preferred clothing image.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.1-7
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• 2017

Joint Collaborative Team on Video Coding (JCT-VC) of ISO/IEC MPEG and ITU-T developed the HEVC Screen Content Coding (HEVC SCC) standard as the HEVC extension for the screen content video coding. The Intra Block Copy (IBC) is the most effective tool adopted in HEVC SCC and predicts current block from already reconstructed neighboring blocks in the same picture. To reduce the amount of data in BV (Block Vector) to be transmitted, a BV predictor (BVP) is used to generate the BV differences in the IBC BV coding. In this paper, we analyze the current BV prediction process using HEVC reference software SCM-2.0 and SCM-4.0. Based on the analysis results, we propose an improved BVP candidate selection algorithm by adding a search process for adjacent BVs in addition to the existing spatial BVP candidates. Experimental results show that the BD-rate reduction of our proposed improvements ranges from 0.2% to 1%.

• Journal of Industrial Convergence
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• v.21 no.4
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• pp.91-100
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• 2023

This study was conducted to identify the effect of motive for major selection, major satisfaction on the nursing career commitment among senior nursing students. Data were collected from 215 nursing students and analyzed using t-test, ANOVA, Scheffé test, Pearson correlation analysis, and multiple regression analysis by SPSS ver. 29.0. As a result of this study, There were positive correlations between motive for major selection(r=.41, p<.001), major satisfaction(r=.35, p<.001), and nursing career commitment. The predictor on nursing career commitment was motive for major selection, explaining 39.4% (F=18.40, p<.001) of the variance. In conclusion, Through this study we found that motive for major selection has the greatest impact on nursing career commitment, it is necessary to seek educational new programs and curricula to develop nursing student's nursing career commitment.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• BMB Reports
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• v.46 no.1
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• pp.41-46
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• 2013

Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.6
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• pp.1482-1489
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• 1998

This paper aims to extract characteristics of the spectrum of fingerprint image and to apply them to image enhancement techniques in spatial frequency domain. Based on 1$\times$64 window as a processing unit and the different record lengths(32, 16, 8), the estimate of power spectrum density for each length was made. Each acquired spectrum characteristics was applied to the re-synthesis process of the fingerprint image, an improved gray scale image was obtained. In order to select an optimal predictor and the Huffman table for the fingerprint iamge, the lossless JPEG algorithm was used. Experiments were performed for extracting distribution characteristics for the each of 7 predictors from the fingerprint image and modeling processes, and the result was applied to the data compression algorithm and the selection of the optimal predictor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.6
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• pp.810-813
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• 1997

The objective of this research was to determine if leaf size (width and length) is correlated with seed size to the extent that leaf size can be used as a predictor of seed size in a population of soybean plants or lines. Twelve soybean strains, representing three distinct seed size groups, were analyzed. Data on seed size and leaf size of the 12 strains were obtained in 1994 and 1995 field experiments. Strain seed size was positively associated with leaf width (r=0.918) and leaf length (r=0.925). The results of our study indicate that there is a significant correlation between seed size and leaf size in soybean. It is possible that selection for greater seed size either leads to, or results from, greater leaf size.