• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.186-189
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• 2005

본 논문에서는 LVQ를 퍼지화한 새로운 퍼지 학습 법칙들을 제안하였다. 퍼지 LVQ 학습법칙 1은 기존의 학습률 대신에 퍼지 학습률을 사용하였는데 이는 조건 확률의 퍼지화에 기반을 두고 있다. 퍼지 LVQ 학습법칙 2는 클래스들 사이에 존재하는 입력벡터가 결정 경계선에 대한 정보를 더 가지고 있는 것을 반영한 것이다. 이 새로운 퍼지 학습 법칙들을 improved IAFC(Integrted Adaptive Fuzzy Clustering)신경회로망에 적용하였다. improved IAFC신경회로망은 ART-1 (Adaptive Resonance Theory)신경회로망과 Kohonen의 Self-Organizing Feature Map의 장점을 취합한 퍼지 신경회로망이다. 제안한 supervised IAFC 신경회로망 1과 supervised IAFC neural 신경회로망 2의 성능을 오류 역전파 신경회로망의 성능과 비교하기 위하여 iris 데이터를 사용하였는데 Supervised IAFC neural network 2가 오류 역전파 신경회로망보다 성능이 우수함을 보여주었다.

• Annual Conference of KIPS
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• 2014.04a
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• pp.757-760
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• 2014

프로그래밍 능력은 21세기 정보화 사회를 살아가는데 있어 인간이 컴퓨터를 활용하여 다양한 문제를 해결할 수 있도록 도움을 준다. 효과적인 프로그래밍 교육이 이루어지기 위해서는 학습자들에게 순차적 수행, 조건적 수행, 반복적 수행과 같은 기본적인 프로그래밍 개념을 습득하도록 할 필요가 있다. 따라서 본 연구는 스도쿠 인공지능 프로그래밍 교육에서 프로그래밍의 기본 개념을 바탕으로 알고리즘을 설계하는 방법을 학습시키는 방안을 모색하기 위한 목적이 있다. 연구의 목적을 달성하기 위해 중학생 10명을 대상으로 실험 연구를 진행하였다. 연구 결과, 학습자는 연구자가 제안한 활동지가 알고리즘 설계 학습에 도움이 된 것으로 인식한 것을 확인할 수 있었다. 본 연구는 프로그래밍 교육에서 초보 학습자가 이해하기 어려워하는 프로그래밍 개념을 학습하는데 도움이 되는 학습 방법을 제시하였다는데 의의가 있다.

• Journal of The Korean Association For Science Education
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• v.21 no.1
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• pp.13-21
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• 2001

This study examined the influences of project activities based on multiple intelligences to science achievement of elementary school children. The proportions of variance of science achievement explained by General Intelligence(GI) and Multiple Intelligences(MI) were analyzed, then the influences of project activities, which used various aspects of MI were investigated. Two classes of grade 5 at Pusan in Korea were selected for the study. On the basis of science achievement of prior term, the subjects were classified into upper-, average-, and lower-achievement groups. GI and MI were measured for each child, and the relationships of these measures with prior science achievement were analyzed using multiple regression analyses. In order to investigate the effects of the project activities on science achievement, the classes were divided into the control and experimental groups, which the former group learned science topics using the traditional teaching and learning method and the latter group performed the projects about the same topics using their own multiple intelligences. Then, their achievements were analyzed by ANOVA. Results showed that the proportion of variance explained by MI was higher about two times than that of explained by GI. Project activities contributed to the improvement of science achievement of average and upper achievers, however, in the case of under achievers, this effect was not statistically significant.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.383-389
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• 2004

This paper proposes a method to provide intelligence for characters in fighting action games by using a neural network. Each action takes several time units in general fighting action games. Thus the results of a character's action are not exposed immediately but some time units later. To design a suitable neural network for such characters, it is very important to decide when the neural network is taught and which values are used to teach the neural network. The fitness of a character's action is determined according to the scores. For learning, the decision causing the score is identified, and then the neural network is taught by using the score change, the previous input and output values which were applied when the decision was fixed. To evaluate the performance of the proposed algorithm, many experiments are executed on a simple action game (but very similar to the actual fighting action games) environment. The results show that the intelligent character trained by the proposed algorithm outperforms random characters by 3.6 times at most. Thus we can conclude that the intelligent character properly reacts against the action of the opponent. The proposed method can be applied to various games in which characters confront each other, e.g. massively multiple online games.

• The KIPS Transactions:PartA
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• v.16A no.5
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• pp.403-410
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• 2009

In this paper, we present an on-line quiz authoring software that helps teachers create an intelligent on-line quiz. It is designed to give each student appropriate diagnostic report using Bayesian inference networks that represent the relationships among knowledge-items. Once the authors design and edit quizzes in quiz authoring page, the authoring tool automatically produces a knowledge-model based on Bayesian inference network, on-line quizzes, and student report pages. It turns out that the on-line quizzes generated by this tool help students identify their weak parts of subject, make learning strategies for the next learning steps and carry out supplementary learning for their weak knowledge-items.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.125-134
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• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.27-30
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• 2000

본 논문에서는 선형 이진 신경회로망(Linear Binary Neural Network)을 이용하여 이진 영상으로부터 골격(skeleton)을 추출하는 병렬 구조를 제안하였다. 기존의 골격 추출 알고리즘으로부터 이진함수를 추출하고 이를 MSP Term Grouping Algorithm을 이용하여 학습시켰다. 결과에서는 기존의 역전파(Back-propagation) 학습알고리즘을 사용한 신경회로망보다 더 쉽게 하드웨어로 구현할 수 있음을 보여준다.

• Journal of the Korean School Mathematics Society
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• v.23 no.2
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• pp.223-237
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• 2020

Artificial intelligence technology, which represents the era of the 4th Industrial Revolution, is now deeply involved in our lives, and future education places great emphasis on building students' capabilities for the principles and uses of artificial intelligence. Therefore, the purpose of this study is to develop the contents of AI related education in mathematics, which the relationship is closely connected to each other. To this end, I propose establishing two novel AI-related contents in mathematics education. One subject is related to learning the principle of machine learning based on mathematics foundation. In addition, I draw the core math contents dealt in following subject called 'Basic Mathematics for AI and Data Science.'

• Review of KIISC
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• v.27 no.3
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• pp.6-11
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• 2017

최근의 인공지능과 기계학습 기술이 과학기술 전반에 걸쳐서 적용되고 있다. 정보보안 분야에서도 인공지능 기술이 다양하게 적용되어 여러 가지 우수한 성능의 제품과 기술들이 나오고 있다. 이러한 시점에 인공지능과 기계학습의 원천 이론 중 하나인 베이지안 추론 (Bayesian Inference)기술에 대한 소개를 하고자 한다. 특히, 정보보호를 연구하는 연구자들에게 베이지안 기술의 기초부터 활용에 이르는 영역을 선보이며 악성코드 분석과 함께 카드사기탐지 기술과 관련하여 베이지안 추론 기술의 적용가능성을 소개한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.275-279
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• 1997

최근 인공지능연구에서는 기호즈의와 커넥션니즘이 독립적으로 연구되어 왔으나 차츰 융합의 필요성이 절실히 요구되고 있다. 본 연구에서는 먼저 기호주의의 일부분인 고전논리를 확장한 다치논리와 커넥션니즘의 기본부분인 신경회로망을 융합한 다치신경망을 구성하고, BP에 기반을 둔 학습 MVL 네트워크를 이용하여 해석한다. 본 논문에서는 이러한 구성 및 해석방법을 확장하여 비고전적인 다치신경회로망을 구성하는 방법을 제안한다.