• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.399-402
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• 2010

본 연구의 목적은 RFID의 도입 성과에 영향을 미치는 요인과 조직의 혁신성의 조절효과를 분석하는 연구모형과 가설을 제시하는데 있다. 연구의 목적을 달성하기 위해 RFID에 관한 이론적 배경을 바탕으로 RFID 성과에 영향을 미치는 요인을 기술적 특성에는 표준화, 지각된 보안, 지각된 비용, 조직적 특성에는 최고경영층의 지원, 재정적 지원, 기술적 지원, 환경적 특성에는 모방적 압력, 강제적 압력, 규범적 압력으로 설정하였고, RFID는 BSC 성과를 이용하여 재무성과, 프로세스성과, 학습/성장성과, 고객성과로 설정하였다. 그리고 조직의 혁신성을 이용하여 혁신적인 조직은 RFID 도입요인과 도입 성과에 어떠한 영향을 주는지에 대한 연구모형을 설정하였다. 향후 연구에서는 이러한 연구모형과 가설에 대한 실증분석이 요구된다.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.147-153
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• 2014

The lifelong education institute in Korea has given the stable groundwork of lifelong education, according to the enforcement of the lifelong education policy for the embodiment of the lifelong education society by Lifelong Education Act newly revised and promulgated in 2007. After that, the lifelong education in Korea has developed, getting the system. Therefore, in this point, this study theoretically provided the history of the lifelong education institute, the content of Lifelong Education Act, and the current lifelong education institute. Above all, this study has significance that it discussed the lifelong education institute in Korea in the comprehensive level.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.7
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• pp.399-412
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• 2006

A clustering based wireless internet proxy server with cooperative caching has a problem of minimizing overall performance because some servers become overloaded if client request pattern is Hot-Spot or uneven. We propose a self-learning based adaptive clustering scheme to solve the poor performance problems of the existing clustering in case of Hot-Spot or uneven client request pattern. In the proposed scheme, requests are dynamically redistributed to the other servers if some servers supposed to handle the requests become overloaded. This is done by a self-learning based method based dynamic weight adjustment algorithm so that it can be applied to a situation with even various request pattern or a cluster of hosts with different performance. We performed experiments in a clustering environment with 16 PCs and a load balancer. Experimental results show the 54.62% performance improvement of the proposed schemes compared to the existing schemes.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.533-541
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• 2022

Metaverse have begun to attract attention because it facilitate interaction between learners and teachers in non face- to- face environment. In order to use the metaverse in the educational field such as online class, it is important that pre-service teachers intend to use it. The purpose of this study is to analyze the structural relationship between the pre-service teacher's educational competence and the intention to use the metaverse based on the technology acceptance model. The influence factors of flexibility for new technology, teacher efficacy, and TPACK were examined. It was conducted with 240 pre-service teachers, and the data of 183 pre-service teachers finally collected were used for the analysis. As a result of the study, among the metaverse educational competencies of elementary school pre-service teachers, flexibility and TPACK mediate perceived ease, and the pathways affecting metaverse use intention were significant. In this regard, theoretical and practical implications that can be helpful in the discussion and intention of using the metaverse of pre-service teachers were presented.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.705-710
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• 2003

Recently, most interchanges of information have been performed in the internet environments. So, the technuque, which is used as intrusion deleting tool for system protecting against attack, is very important. But, the skills of intrusion detection are newer and more delicate, we need preparations for defending from these attacks. Currently, lots of intrusion detection systemsmake the midel of intrusion detection rule using experienced data, based on this model they have the strategy of defence against attacks. This is not efficient for defense from new attack. In this paper, a new model of intrusion detection is proposed. This is hybrid statistical learning model using likelihood ratio test and statistical learning theory, then this model can detect a new attack as well as experienced attacks. This strategy performs intrusion detection according to make a model by finding abnomal attacks. Using KDD Cup-99 task data, we can know that the proposed model has a good result of intrusion detection.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.660-662
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• 1998

데이터 sphering은 신호처리 및 신경망 분야에서 널리 이용되는 기본적인 처리과정으로, 그 목적은 주어진 데이터간에 correlation을 제거하는 것이다. 본 논문에서는 회귀 신경망을 도입하여, 데이터 sphering을 위한 새로운 on-line 알고리듬을 제시한다. 정보 이론에 입각한 risk 함수와, 최근에 제시된 natural gradient을 이용하여 새로운 데이터 sphering 알고리듬을 유도한다. 새로 제시된 알고리듬의 성능을 기존에 널리 이용되어 온 anti-Hebbian 학습 알고리듬과 비교 분석한다.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.405-412
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• 2021

To prevent problems such as portrait rights, copyright, and cyber violence, an ethics education STEAM projects using deepfake apps using AI technology were developed and applied. The Deepfake apps were screened, and the contents of the elementary school curriculum were reconstructed. The STEAM project as creative experiential activities was mainly operated by the UCC activities, and applied the info-ethics awareness measurement test based on the planned behavior theory. The social STEAM project as money (financial) education was qualitatively analyzed. It was found that this STEAM classes using AI technology app significantly enhances the ethical awareness of information communication.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.149-156
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• 2014

NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.