• Journal of The Korean Association of Information Education
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• v.15 no.2
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• pp.307-317
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• 2011

Identifying gifted students in the valid method is one of the most important issues in the filed of gifted education. The Identification processes should be based on the theoretically sound definition of the gifted. However, the concept of giftedness varies according to the culture and philosophy of each society. Furthemore, we do not have any direct measure for evaluating students' giftedness. So far. there is no satisfactory single tool which is universally accepted as an absolute identification method. Recently, programs for gifted students are regarded as the channel for entering prestigious high schools as well as competitive colleges by many parents in Korea. As the result, education business for preparing young students to be accepted to gifted programs are growing rapidly. This study was performed in order to (1)analyze the problems in the current identification methods, (2)establish the strategies for identifying gifted students whom the future society needs, and (3)suggest the procedures and tools which can be adapted for the proposed identification methods.

• Korean Journal of Food Science and Technology
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• v.49 no.5
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• pp.469-473
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• 2017

The objective of this study was to develop methods for the discrimination of new and stale rice by using an electronic eye. To develop the discriminant, 107 rice samples produced in the years 2015 and 2016 were investigated. After the rice was treated with guaiacol, oxydol, and p-phenylenediamine reagents, an electronic eye was applied to discriminate between newly harvested rice and rice stored for 1 year. Out of the 4,096 color codes of the electronic eye, 31 color codes were identified for the discrimination of newly harvested rice and rice stored for 1 year. The classification ratio of newly harvested rice and rice stored for 1 year was 100% and the discrimination accuracy for unknown samples was 100%. In a total of 150 mixtures of new rice and stale rice, the discrimination accuracy was between 16.7 and 95.6%, depending on the mixing ratio. This capability of the electronic eye will be useful as a tool for discriminating the production year of rice.

• Journal of Gifted/Talented Education
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• v.15 no.1
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• pp.67-84
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• 2005

In this study we investigated the adequacy of tools for distinction gifted students through the comparison these mutual relation on the basis of data, like paper test, the depths interview score, and the rest data((TTCT: Torrance Tests of Creative Thinking, IQ test, FASP: Find A Shape Puzzle, V.T: Visualization Tests and Exp: experimental ability test), and analysis data of EEG test for examining the adequacy of tools for identification gifted students. So, we developed Brain Wave gifted Index(G-Index) for finding another distinction ability as using brain waves data. The standard of index development use gifted brain characteristic in closed-eyes rest state which is judged like that characteristic of distinction between gifted and normal students is the most clear and consistence. That is, the degree of unified pattern between each object and gifted PCA pattern was defined by Pearson method which added spatial mutual index to weight concept. This refer to mean number of spatial PCA pattern. Searching for the possibility of distinction gifted gave distinction effect in 76%. The result of regression analysis on the basis of mutual relation between the rest data is . The probability formula for distinct gifted group is as follow. $$P=\frac 1{1+e^{-[-0.018(TTCT)+0.057(IQ)+1.916(FASP)+0.682(V.T)+0.088(Exp.)+0.034(G-Index)-57.510]}}$$ The result of this calculation showed that probability for distinct in gifted group was very good(95.0%). On the basis of upper result, tools for identification gifted students should be estimated as using many-sided estimation data whatever possible. And following study about development, and operation of tools for distinction suitable to gifted student in science should be progressed.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.867-876
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• 2017

Datasets with small n and large p are often found in various fields and the analysis of the datasets is still a challenge in statistics. Discriminant analysis models for such datasets were recently developed in classification problems. One approach of those models tries to detect dimensions that distinguish between groups well and the number of the detected dimensions is typically smaller than p. In such models, the number of dimensions is important because the prediction and visualization of data and can be usually determined by the K-fold cross-validation (CV). However, in sparse data scenarios, the CV is not reliable for determining the optimal number of dimensions since there can be only a few observations for each fold. Thus, we propose a method to determine the number of dimensions using a measure based on the standardized distance between the mean values of each group in the reduced dimensions. The proposed method is verified through simulations.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.89-93
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• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.120-125
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• 2009

본 논문에서는 통계적 분류방법을 이용하여 문화재 자료의 분석을 수행하였다. 분류방법으로는 선형판별분석, 로지스틱회귀분석, 의사결정나무분석, 신경망분석, SVM분석을 사용하였다. 각각의 분류방법에 대한 개념 및 이론에 대해 간략히 소개하고, 실제자료 분석에서는 "지역별 문화재 통계분석 및 모형개발 연구 1차(2008)"에 사용된 자료 중 익산시 자료를 근거로 매장문화재에 대한 분류방법별 적합모형을 구축하였다. 구축된 모형과 모의실험의 결과를 통해 각각의 적합모형에 대한 비교를 수행하여 모형의 성능을 비교하였다. 분석에 사용된 도구로는 최근 가장 관심을 갖는 R-project를 사용하였다.

• Journal of Gifted/Talented Education
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• v.9 no.2
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• pp.131-152
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• 1999

일반적으로 영재교육을 실시함에 있어서 가장 먼저 선행되어야 할 것은 영재아 판별기준을 정해 학생선발과정을 운영하는 것과 실제 가르칠 교육내용을 구체적으로 정의하여 교재를 개발하는 것이다. 특히, 최근에 각광받고 있는 정보과학 분야의 영재교육에 대한 연구는 이제 시작단계에 불과하여 서로 다른 이론적 근거와 주관적인 기준에 따라 전국의 과학연재교육센터들을 중심으로 다양한 논의가 활발하게 이루어지고 있다. 본 논문에서는 아주대학교 과학영재교육센터에서 초등학교 4학년부터 중학교 3학년까지의 학생들을 대상으로 운영되고 있는 정보과학 영재교육 프로그램을 자세히 소개함으로써, 앞으로 정보과학 분야의 영재교육을 위한 구체적인 영재판별기준과 학생선발절차는 물론 체계적인 교육과정 및 교과목의 개발을 위한 하나의 실례로써 소개하고자 한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.06a
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• pp.204-207
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• 2001

뇌 MR 영상에서 질환을 자동적으로 진단하고 판별하는 작업은 정상인의 뇌 영상과의 비교를 통해서 가능하다. 정상인과의 뇌 영상 비교를 통하여 보다 정확하게 질병에 대한 근거를 제시할 수가 있기 때문에 이러한 접근 방법들이 여러 의료영상 연구 분야에서 시도되고 있다. 정상인의 뇌 영상과의 비교를 위해서는 우선적으로 해결되어야 하는 것이 현재의 대상 영상이 정상인 뇌의 어느 위치의 영상과 일치하는 지를 판별하는 문제이다. 따라서 본 연구는 이러한 뇌 매핑에 사용될 수 있는 특징들을 추출하기 위한 것으로, 뇌 매핑에 사용되는 특징들을 추출하기 위해서 뇌 MR 영상으로부터 대리영역, 뇌영역, 뇌척수액영역 그리고 눈영역을 분할한 후 이들의 윤곽선, 최소사각형과 각 영역들의 픽셀 정보들을 찾아낸다. 이는 추후 연구할 뇌 매핑을 위한 대분류에 사용될 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.471-480
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• 2010

Cluster analysis is one of the useful methods to find out number of groups and member’s belongings. With the rapid development of computer application in statistics, variety of new methods in clustering analysis were studied such as EM algorism and Self organization maps. The goals of cluster analysis is finding the number of groupings that are meaningful to me. If data are analyzed perfectly with cluster analysis, we can get the same results from discernment analysis.