• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.739-742
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• 2011

본 논문에서는 유비쿼터스 시대를 대비한 지능형 교육관리 시스템을 설계하고 이를 구현해보고자 했다. 이룰 위해 RFID와 웹캠을 활용한 학생관리, 지문인식을 통한 개인 보안인 증, 후킹 기술을 이용한 개인 컴퓨터의 키보드 잠금 설정, 크라우드 시스템을 이용한 개인자료 관리 기능, 패킷분석을 통한 사이트 접속차단 등의 시스템을 설계하고 구현했다. 시스템 구현 결과를 바탕으로 본 논문에서의 연구결과를 실제 실습수업 시스템에 적용하면서 좀 더 보완을 해 나갈 계획이다.

• 한국경영과학회지
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• 제32권1호
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.