• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.41-49
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• 2009

It's going to be ubiquitous environment which is able to use web pages independent of time and a place by recent development of mobile techniques. On this, we improved the leveled reading supporting system according to U-learning environment and make student to read on both Off-line and On-line through connecting to E-book service. So we developed the reading supporting system which can improve the interests in reading and reading skill and proved the effects. U-learning based leveled reading education support system could be helped develop the reading ability by raising the interest in the activities reading and after reading.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.991-1000
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• 1994

To control nonlinear systems adaptively, we improve learning speed of neural networks and present a novel control algorithm characterized by compensation of control inputs. In an error-backpropagation algorithm for tranining multilayer neural networks(MLNN's) the effect of the slope of activation functions on learning performance is investigated and the learning speed of neural networks is improved by auto-adjusting the slope of activation functions. The control system is composed of two MLNN's, one for control and the other for identification, with the weights initialized by off-line training. The control algoritm is modified by a control strategy which compensates the control error induced by the indentification error. Computer simulations show that the proposed control algorithm is efficient in controlling a nonlinear system with abruptly changing parameters.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.2
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• pp.214-225
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• 2001

This research deals with an off-line learning method targeted for systematically constructing negotiation strategies in automated electronic commerce. Single-issue negotiation is assumed. Variants of competitive learning and hierarchical clustering method are devised and applied to extracting negotiation strategies, given historical negotiation data set and tactics. Our research is motivated by the following fact: evidence from both theoretical analysis and observations of human interaction shows that if decision makers have prior knowledge on the behaviors of opponents from negotiation, the overall payoff would increase. Simulation-based experiments convinced us that the proposed method is more effective than human negotiation in terms of the ratio of negotiation settlement and resulting payoff.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.499-501
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• 2004

교육의 새로운 방향은 가상 학교나 WBI와 같은 교육적 활용분야로 확대되고 있으나, e-Learning 상에서 이뤄지는 평가의 응시자에 대한 신뢰 확보는 어려운 상황이다. 기존의 인증이란 사용자가 E와 Password를 타인에게 공개하지 않는 한 본인임을 인증하였다. 하지만 온라인상에서 시험 응시자는 본인의 ID와 Password를 타인에게 알려주어 대리시험이 가능하게 함은 물론 시험문제의 공유 또는 다수의 응시자가 한 곳에 모여 문제를 풀어 감으로서 평가에 대한 신뢰도에 의문을 갖지 않을 수 없게 되었다. 이에 인터넷으로 원격조정이 가능한 PC카메라와 얼굴인식 프로그램 그리고 원격제어프로그램을 이용하여 응시자를 인증함으로써 부정행위를 원천적으로 봉쇄하고, 감독자가 언제 어디서나 웹을 통하여 쉽게 감독할 수 있도록 LMS 기능의 보완이 요구된다. 본 논문을 통해서는 채팅기능을 통한 상호 대화가 가능하고 응시 장면을 동영상으로 압축 저장하여 사후 감독이 가능토록 함으로서 e-Learning상에서의 평가 및 학사관리의 공정성 및 신뢰도를 높일 수 있는 방안을 제시하였다.

• Journal for History of Mathematics
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• v.30 no.2
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• pp.101-119
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• 2017

This paper is a follow up study of [2]. In this paper we analyse the contents of middle school mathematics textbooks published in the 1st National Curriculum Period centered on the concept 'straight line' and discuss how they are different from contemporary mathematics textbooks in view of connectedness of contents, mathematical terms, textbook as a learning material vs. teaching material, relationship between contents of national curriculum and textbooks, and some topics related to direct proportion, function, method of equivalence as a method for solving simultaneous linear equations and so on. The results of our analysis and discussion suggest implications for reforming mathematics curriculum and developing mathematics textbooks.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.1
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• pp.79-83
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• 2000

Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system table prior to learning. Therefore, we introduced LQR(Linear Quadratic Regulator) technique to stabilize the system. It is a state feedback control to move unstable poles of a linear system to stable ones. But, if the system is nonlinear or complicated to get a liner model, we cannot expect good results with only LQR. In this paper, we propose that the LQR law is derived from a roughly approximated linear model, and next the fuzzy controller is tuned by the adaptive on-line learning with the real nonlinear plant. This hybrid controller of LQR and fuzzy learning was superior to the LQR of a linearized model in unstable nonlinear systems.

• Journal of Digital Contents Society
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• v.1 no.1
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• pp.1-11
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• 2000

A variety of educational digital contents are provided for both distance and on-line learning on the Internet recently. Especially, as edutainment fields are activated, fun-centered educational contents are improved so much. But it is still lack of high quality, which could lead a learner to study by himself without losing enjoyment for an appropriate duration (about $1{\sim}2$ years). A system, which enforces learning motivation for a learners positive learning, is demanded. This paper shows the planning and the implementation of learning Reward System (LRS) which is providing rewards a learner for achievement of the teaming object which is suggested by him (her) and his (her) mentors. LRS is aiming at enhancement of educational effects by providing both amusements and rewards employing edutainment contents.

• IE interfaces
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• v.18 no.1
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• pp.10-21
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• 2005

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. In this research we compare the performance of Naive Bayesian learning and Centroid-Based Classification using the different data set of an on-line shopping mall and a credit card company. We analyze which method performs better under which conditions. We compared classification accuracy of them which depends on structure and size of train set and increasing numbers of class. The experimental results indicate that Naive Bayesian learning performs better, while Centroid-Based Classification is more robust in terms of classification accuracy.

• Journal of Engineering Education Research
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• v.17 no.6
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• pp.62-68
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• 2014

The purpose of this work is to provide systematic lecture materials for instructers who search for the effective teaching of applied mechanics of materials course with respect to lecture contents, teaching methods, and itemized course evaluations according to each class learning objective. For this. the evolution of teaching contents since 2010 until 2014 are briefly depicted and then most recent course learning objectives, lecture contents, and evaluation schemes are presented in detail. The results of this study may be used as base line data for the lecturers who teach similar courses and for the evaluation of program outcomes in ABEEK scheme through course-embedded assessment.

• Journal of Information Technology Services
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• v.2 no.2
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• pp.87-95
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• 2003

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. in this research we develop e-mail classifiers for e-mail Response Management Systems (ERMS) using naive bayesian learning and centroid-based classification. We analyze which method performs better under which conditions, comparing classification accuracies which may depend on the structure, the size of training data set and number of classes, using the different data set of an on-line shopping mall and a credit card company. The developed e-mail classifiers have been successfully implemented in practice. The experimental results show that naive bayesian learning performs better, while centroid-based classification is more robust in terms of classification accuracy.