• 한국지구과학회지
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• 제26권8호
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• pp.751-758
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• 2005

본 연구에서는 지질학 연구에 관한 과학철학적 논의를 기반으로 지구과학 탐구의 특징을 반영한 지구과학 탐구방법의 개념적 틀을 개발하고, 이 틀을 분석 도구로 하여 중학교 2학년 과학 교과서에 수록된 탐구 활동을 분석하였다. 분석틀에서는 지구과학의 탐구 방법을 논리적 추론 방법, 해석적 방법, 역사적 방법으로 구분하고, 각각에 부속하는 방법이나 방법론상의 특징들을 세분화하여 제시하였다. 분석을 위해서는 중학교 2학년 과학 교과서의 '지구의 역사와 지각 변동' 단원의 탐구 활동을 대상으로 하였다. 분석된 탐구 활동들에 논리적 추론 방법이 반영된 비율은 귀납적 방법이 23%, 연역적 방법이 22%, 귀추적 방법이 70로, 귀추적 방법이 가장 높은 반영 빈도를 보였다. 해석적 방법의 특징이 반영된 비율은 '이미 형성된 이해 구조의 역할'이 92%로 가장 높았으며, '순환적 추론'이 9%, '이해의 역사적 본성'이 17%였다. 역사적 방법은 그 반영된 비율이 높은 것으로부터 적절한 분류체계의 구성'(53%), '현대적 동일과정설의 원리 적용'(47%), '잔존물 해석'(41%), '단계이론화 과정에서 장소로써 시간을 대체'(3%), '독립된 연구 결과들의 수렴 정도 평가(3%) 순으로 나타났다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.199-202
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• 2001

An elevator system, which is essential equipment for vertical movement of an object, as a property of building, has been driven by various expenditures and purposes. Since developing electrical control technology, control system are highly developed. The elevator system has expanded widely, but a data accuracy acquisition technique and safety predict technique for securing system safety is still at a basic level. So, objective verification for elevator confidence condition requires an absolute accuracy measurement technique. Therefore, this study is executed in order to acquire a method of depending on sense of a manager with simple numeric measurement data, and to construct a logical, analytical foresight system for more efficient elevator management system. As an artificial intelligence for diagnosis, the fuzzy inference algorithm is used for foreseeing the system in this thesis, because the fuzzy algorithm is the most useful method for resolving subjective ideas and a vague judgment of humans. The fuzzy inference algorithm is developed for each sensor signal(i.e. vibration, velocity, current).

• 한국문헌정보학회지
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• 제14권
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• pp.3-18
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• 1987

The factors which can help the library users trace the specific subject that she or he needs are most important, when they are not sure of what they need exactly in front of a classified catalogue. This study is about what the factors are and how the factors affect the inference of users' reasoning structure. Since the classified catalogues are reflected by the classification structure, naturally the logic in the classification system becomes the focus of the study. This study concludes the classification system which enables the library users to use their reasoning capabilities, viz. the classification system which can help the users trace the specific subject even as they are not sure of the exact subject they need has following factors in the system. 1. It should have the validity based on the facts in the components of the classification system. 2. It should be logically arranged when the components of the classification system are placed in due sequence. 3. The notation of the system should be based on mnemonics. The reason is that the indispensable factors in the formation of inference of human reasoning structure are: 1. the premises which are based on the facts and 2. the logical relationship between the premises and conclusions which are induced from the premises.

• Spatial Information Research
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• 제7권2호
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• pp.191-207
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• 1999

공간과 관련된 의사결정문제 해결에 필요한 취득가능한 자료나 정보는 불완전하거나 부정확하며, 많은 부분 자연산어(natural language)로 기술되어 있다. 이 같은 정보들을 컴퓨터를 이용하여 처리하기 위해서는 결국 컴퓨터로 하여금 인간이 사용하는 자연어를 이해할 수 있도록 애매한 특성의 언어값(Linguistic value)을 정량적으로 기술할 필요가 있다. 이를 위해 퍼지집합(fuzzy set) 이론을 퍼지논리(fuzzy logic)가 대표적인 방법론으로 이용되고 있다. 본 논문에서는 부정확하거나 불명확한 자료 및 정보를 기반으로 의사결정문제를 지능적으로 처리하기위해 사용자가 가장 이해하기 쉬운 자연어로 『언어모델』을 구축하고, 평가사안이나 의사결정문제가 불명확하게 서술될 경우 컴퓨터를 이용한 구조화 및 추론을 통한 문제해결이 가능하도록 퍼지추론기관구축을 위한 일련의 논리적 개념과 구축과정을 연구하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.127-130
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• 2002

Human's brain action can divide by recognition and intelligence. recognition is sensing voice, image and smell and Intelligence is logical judgment, inference, decision. To this concept, Define function of cerebral cortex, and apply the result. Current expert system is lack, that reasoning by cerebral cortex and thalamus, hoppocampal and so on. In this paper, With human's brain action, wish to embody human's action artificially Embody brain mechanism using Modular Neural Network, Applied this result to snake robot.

• 한국과학교육학회지
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• 제14권2호
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• pp.123-132
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• 1994

The experimental evidence evaluation of the 11th grade students(N:91) was investigated. Specially, the influence of students' ideas about optical phenomena and presented evidence types on their evidence evaluation, and the influence of students' ideas on their causal inference modes were investigated. After eliciting the students' ideas about shadow phenomena and conformity of their idea, the experimental results with a binary outcome were presented as the evidence. Then the students were asked to evaluate the evidence. Again students' ideas were elicited. Most of students had causal ideas such that the shape of object(96%) and the inclination of screen(75%) were causes of shadow shape, not the shape(70%) and color(92%) of light source. In the case of the shape of object and the color of light source, most students(70%) believed strongly their ideas. Most responses(80%) in the evidence were evidence-based, and 12% of them were theory-based. There was no significant difference of reponses types between students with causal ideas(81%) and students with non-causal ideas(78%), between covariable and non-covariable evidence. But in the case of non-causal ideas, covariable evidence was more likely to yield evidence-based reponses than non-covariable evidence. If students had preconcepts inconsistent(84%) with the evidence, they were more likely to make evidence-based responses than the students with consistent ideas (75%) with the evidence. Especially in the case perceptually biased evidence, this tendency was marked. In the case of covariable evidence, many students made inclusion inferences(40%) rather than uncertainty inferences(32%). In the case of uncertainty inferences(94%), students more likely to make evidence-based reponses than inclusion inferences(83%) and exclusion infernces(88%). In the case of inclusion inferences and exclusion infernces, students tended to make idea-based responses and distort the evidences. In conclusion, when the students evaluate the experimental evidences, their ideas influence the causal inference modes. Especially, according to the conformity of the preconcepts and logical relation of evidences, the inference modes are more strongly depended upon the preconcepts rather than evidences.

• 한국경영과학회지
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• 제28권3호
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.

• 한국과학교육학회지
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• 제26권6호
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• pp.703-722
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• 2006

본 연구는 Peirce가 제시한 귀추법의 특성을 고찰하고, 이에 기초하여 그의 귀추법이 갖는 과학교육적 함의에 관해 논의하는 것을 목적으로 하였다. 귀추법은 가설 생성의 논리로서, 일반적 규칙과 관찰된 결과를 결합시키고, 유사성에 근거하여 관찰된 결과를 기존 법칙의 한 사례로 인식할 수 있도록 하는 논리적인 추리이다. 그리고 그 과정은 의심의 상태를 벗어나 믿음의 상태에 이르는 탐구의 과정이다. 이때 도달하게 되는 믿음은 습관의 확립을 수반하고, 습관은 어떤 대상이 갖는 의미 전부이므로, 그에게 있어서 믿음의 정착은 진리에 도달하는 길이다. 단, 그 믿음이 개인적인 경우에는 항상 오류가능하기 때문에, 진리에 도달하는 것은 무한히 탐구할 수 있는 탐구공동체가 궁극적 의견에 도달함으로써 가능하다. 이러한 특성의 귀추법은 과학교육에서 첫째, 실제적 효과를 고려하면서 의심에서 믿음으로 가게 하는 가설 생성에 주목할 것, 둘째, 유사성에 근거하여 관찰 결과를 기존 규칙의 한 사례로 귀속시키는 추리에 주목할 것, 셋째, 온건한 의미의 실재론적 관점이 견지되는 탐구공동체를 지향할 것 등을 제안하고 있다. 이러한 연구결과가 귀추법에 관한 추후 과학교육 연구들과 관련하여 비판적 논의의 한 축을 제공할 수 있기를 기대한다.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 추계학술발표대회(상)
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• pp.342-345
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• 2003

자동운전과 안전운전 구현을 위한 첨단 차량 및 도로 시스템(AVHS : Advanced Vehicle & Highway Systems)의 한 분야인 충돌 방지 시스템을 완성하기 위해서는 차량간의 상대 거리, 차량의 속도, 차선의 굴곡 정도, 경사도등을 사용해서 종합적으로 상황 판단을 내려야 한다. 본 논문에서는 이들 요소들중에서 차선의 굴곡도 판단을 근사 추론을 이용하여 실험하였다. 근사추론을 이용한 것은 차선의 굴곡도를 계산형으로 파악할 때의 단점인 계산 시간 오버헤드(overhead), 또 그에 따른 실시간 처리의 어려움, 고가의 장비필요성 등을 극복하기 위해서이며, 실험은 Fuzzy Logical Inference 기법을 사용하였다. 본 연구에서는 실제 도로상에서의 계산된 굴곡도와 실험된 시스템 결과와의 유사성과 그 시스템의 사용 가용성(feasibility)을 검정하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.