• Communications of Mathematical Education
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• v.8
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• pp.137-150
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• 1999

본 연구의 목적은 아동들의 수학 교과에 대한 정의적 특성과 수학적 문제 해결력, 추론 능력간의 상호 관계를 구명하고, 이러한 관계들은 아동의 지역적인 환경에 따라 차이가 있는지를 분석하는 것이다. 본 연구를 통하여 얻은 결론은 다음과 같다. 정의적 특성의 하위 요인 중 수학적 문제 해결력과 귀납적 추론 능력에 대한 설명력이 가장 높은 요인은 수학교과에 대한 자아개념인 것으로 나타났으며, 연역적 추론 능력에 대한 설명력은 학습 습관이 가장 높은 것으로 나타났다. _그리고 귀납적 추론 능력이 연역적 추론 능력 보다 수학적 문제 해결력에 대한 설명력이 더 높은 것으로 나타났으며, 수학적 문제 해결력과 귀납적 추론 능력은 지역별로 유의한 차가 나타났으나 연역적 추론 능력은 지역간 유의한 차이가 나타나지 않았다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.157-160
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• 2001

본 논문에서는 강수량과 자동차 주행속도 등의 환경조건에 따라 와이퍼 속도를 일정하게 적용한 기존의 시스템을 개선하여 운전자의 개인 특성에 의해서도 속도 변경이 가능하게 함으로서 인간에게 조금 더 친밀감을 제공하는 시스템을 구현하였다. 초기 와이퍼 속도는 입력받은 강수량과 자동차 주행 속도로 추론하여 구하였다. 추론된 와이퍼 속도를 운전자의 개인 특성에 따라 변경하고자 할 경우, 해당 음성명령을 입력받아 재 추론하였다. 음성인식을 위해서는 고립단어 인식에 적절한 DTW방식을 사용하였고, 와이퍼 속도는 퍼지 추론을 적용하여 구하였다.

• Journal of Educational Research in Mathematics
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• v.19 no.1
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• pp.81-98
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• 2009

This study analyzed the types of quantitative reasoning and the characteristics of representation in order to figure out the characteristics of quantitative reasoning of the sixth graders. Three students who used quantitative reasoning in solving problems were interviewed in depth. Results showed that the three students used two types of quantitative reasoning, that is difference reasoning and multiplicative reasoning. They used qualitatively different quantitative reasoning, which had a great impact on their problem-solving strategy. Students used symbolic, linguistic and visual representations. Particularly, they used visual representations to represent quantities and relations between quantities included in the problem situation, and to deduce a new relation between quantities. This result implies that visual representation plays a prominent role in quantitative reasoning. This paper included several implications on quantitative reasoning and quantitative approach related to early algebra education.

• Education of Primary School Mathematics
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• v.19 no.2
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• pp.161-175
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• 2016

The purpose of this study is to measure the differences in affective characteristics and mathematical reasoning ability between gifted students and non-gifted students. This study compares and analyzes on the relations between the affective characteristics and mathematical reasoning ability. The study subjects are comprised of 97 gifted fifth grade students and 144 non-gifted fifth grade students. The criterion is based on the questionnaire of the affective characteristics and mathematical reasoning ability. To analyze the data, t-test and multiple regression analysis were adopted. The conclusions of the study are synthetically summarized as follows. First, the mathematically gifted students show a positive response to subelement of the affective characteristics, self-conception, attitude, interest, study habits. As a result of analysis of correlation between the affective characteristic and mathematical reasoning ability, the study found a positive correlation between self-conception, attitude, interest, study habits but a negative correlation with mathematical anxieties. Therefore the more an affective characteristics are positive, the higher the mathematical reasoning ability are built. These results show the mathematically gifted students should be educated to be positive and self-confident. Second, the mathematically gifted students was influenced with mathematical anxieties to mathematical reasoning ability. Therefore we seek for solution to reduce mathematical anxieties to improve to the mathematical reasoning ability. Third, the non-gifted students that are influenced of interest of the affective characteristics will improve mathematical reasoning ability, if we make the methods to be interested math curriculum.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.346-351
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• 2006

시맨틱 웹에 대한 관심이 높아짐과 더불어 관련 기술인 온톨로지와 이를 이용한 추론 기술 역시 이슈가 되고 있다. RacerPro, Pellet 등 지금까지의 전형적인 추론 시스템들은 주로 Tableaux Algorithm 기반의 추론 시스템으로 Tableaux Algorithm의 특성상 대용량 ABox 추론에서 문제점을 나타낸다. 이를 해결하기 위한 연구로는 Tableaux Algorithm 기반의 Instance Store와 Disjunctive Datalog Approach를 사용한 KAON2가 있다. 이러한 추론 기술에 대해서는 많은 연구가 진행되고 있지만 각 추론 시스템들에 대한 평가는 부족하다. 현재 추론 시스템들의 벤치마킹은 대부분 Tableaux Algorithm 기반의 TBox 추론에 대한 것으로 ABox 추론 및 최근 이슈인 대용량 ABox 추론에 대한 평가는 특히 부족하다. 이에 본 논문에서는 각 추론 시스템들의 이론적 배경을 근간으로 지금까지의 전형적 추론엔진들과 최근 이슈에 따른 대용량 ABox 추론을 위한 시스템들을 이론적 비교를 통해 살펴보며, 특히 대용량 ABox 추론를 위한 시스템인 Instance Store와 KAON2를 LUBM을 사용하여 평가함으로 대용량 ABox 추론에 있어 사용자의 요구에 따른 적절한 시스템을 제시한다.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.655-666
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• 2007

Reasoners using typical Tableaux algorithm such as RacerPro, Pellet have a problem in Tableaux algorithm large ABox reasoning. Researches to solve these Problems are dealt with Instance Store of University of Manchester which uses Tableaux algorithm based reasoner and DBMS and KAON2 of University of Karlsruhe using Disjunctive Datalog approach. An evaluation experiment for present reasoners is the experiment of TBox reasoning in most of Tableaux algorithm based one. The most of benchmarking tests in reasoning systems haven't done with ABox reasoning based Tableaux Algorithm but done with TBox reasoning based Tableaux Algorithm. Especially, rarely reported benchmarking tests in reasoners have been issued nowadays. Therefore, this thesis evaluates systems with theory of each reasoners for large ABox reasoning that becomes issues recently with typical reasoners. The large AoBx reasoning engine will be analyzed using Instance Store and KAON2 of Manchester University for large ABox processing. At the analysing method, LUBM(Lehigh University BenchMark), benchmarking test method, and it's test system will be introduced. In conclusion, I recommend appropriate reasoner in various environment with experiment result and characteristic of algorithm used for each reasoner.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.1
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• pp.137-146
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• 2002

The predictions of the loading characteristics was performed by the neural networks which use the results through structural analysis. The momentum backperpagtion which can be modified the teaming rate and momentum coefficient, was developed. Input patterns of the neural networks are the 9 strains which positioned at the side of the shell and output layers is the loading characteristics. Hidden layers were increased from 1 layers to 3 layers. Developed program which were trained by 9 strains predict the loading characteristics under 0.5%. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.65-67
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• 2010

위치기반서비스(LBS)는 사용자의 위치를 기반으로 다양한 정보제공 서비스를 하고 있다. 최근 연구에서는 단순한 정보제공이 아닌 사용자의 상황인식(Context-Aware)을 통하여 사용자에게 적합한 정보를 제공해주는 지능화된 서비스를 제공하고 있다. 하지만 현재 연구들은 사용자의 기본정보와 선호도정보를 이용한 단일기준 추론을 통하여 사용자에게 정보를 제공해주고 있으며, 이것은 사용자의 다양한 기준의 의사결정을 반영하지 못하는 한계점이 있다. 이러한 한계점을 극복하기 위하여 본 연구에서는 사용자의 정보, 선호도, 공간지리선호도 정보 Ontology를 구축하고, 의사 결정 기준에 가중치를 부여하는 Cost Value Ontology를 구축하여, 다 기준 의사추론을 통해 사용자에게 적절한 추천 결과가 도출되는 Ontology 추론시스템을 제안한다. 사용자들의 개인적인 특성 지식과 공간지리 선호도 지식을 구축할 수 있으며, 이러한 특성으로 구축된 지식 기반 하에 입력된 사용자 정보와 추론을 통하여 이 시스템을 통해 사용자의 선호도 Ontology를 구축할 수 있으며 이를 이용한 추론을 통하여 사용자의 현재상황에 적합한 결과를 도출함을 보였다.

• Composites Research
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• v.14 no.5
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• pp.26-37
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• 2001

After impact analysis of the composite cylindrical shells was performed. obtained outputs at 9 equally divided points of the shell were used as input patterns of the neural networks. Identification of impact loading characteristics was predicted simultaneously. Momentum backpropagation algorithm of neural networks which can modify the momentum coefficient and learning rate was developed and applied to identify the loading characteristics. Hidden layers of the backpropagation increased from 1 layer to 3 layers and trained the loading characteristics. Developed program with variable learning rate was converged close to real load characteristics under 1% error. Inverse engineering which identify the impact loading characteristics can be applicable to the composite laminated cylindrical shells with developed neural networks.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1189-1196
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• 2004

It is a major concern of e-shopping mall managers to satisfy a variety of customer's desire by recommending a proper product to the perspective purchaser. Customer information like customer's fondness, age, gender, etc. in shopping has not been used effectively for the customers or the suppliers. Conventionally, e-shopping mall managers have recommended specific items of products to their customers without considering thoroughly in a customer point of view. This study introduces the ways of a choosing and recommending of products using case-based reasoning and rule-based reasoning for customer themselves or others. A similarity measure between one member's idiosyncrasy and the other members' is developed based on the rule base and the case base. The case base is improved for the system intelligence by recognizing and learning the changes of customer's desire and shopping trend.