• Journal of the Korean earth science society
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• v.42 no.1
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• pp.118-131
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• 2021

The purpose of this study was to investigate the characteristics of evidence-based reasoning practiced by middle school gifted students. Data were collected through an online task in which middle school students in gifted education institutes of a university located in the metropolitan area, Korea, performed inquiry about the shape of a planet's orbit. The students were given data of Mercury's greatest elongations and asked to draw the planet's orbit with the data. Each of the students was also asked to provide his or her hypothesis of Mercury's orbit before the drawing and to reason about the orbit again using his or her own drawing as evidence. The content analysis of the students' reports revealed 5 different types of judgement about the shape of Mercury's orbit, 4 types of reasoning about the hypothesis and evidence, and the characteristics of evidence-based reasoning within the judgement types. Based upon the analysis results, the importance of proper interpretations of evidence in evidence-based reasoning, the core role of the theory-evidence coordination, and the usefulness of working with multiple hypotheses were discussed. In addition, implications for earth science education were suggested.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.581-587
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• 2011

A wireless sensor network needs to consist of multi-sensors in order to infer a high level of information on circumstances. Data fusion, in turn, is required to utilize the data collected from multi-sensors for the inference of information on circumstances. The current paper, based on Dempster-Shafter's evidence theory, proposes data fusion in a wireless sensor network with different weights assigned to different sensors. The frequency of events per sensor is the crucial element in calculating different weights of the data of circumstances that each sensor collects. Data fusion utilizing these different weights turns out to show remarkable difference in reliability, which makes it much easier to infer information on circumstances.

• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.635-647
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• 2018

Motivated reasoning refers to biased reasoning that is affected by motivation to achieve a particular result or goal. In this study, we attempted a theoretical study on motivated reasoning that hinders the development of scientific thinking and empirical study on actual context of motivated reasoning in the research experiences of next-generation Korean researchers in the field of science and technology. To be specific, literature reviews were conducted to explore the psychological meaning of motivated reasoning and its negative impact on scientific thinking and science research. To understand the substantial meaning and context of motivated reasoning in the field of real science and technology research, we conducted in-depth interviews with eight graduate students and one young science and technology researcher. As a result of the literature reviews, we found out that motivated reasoning can interfere with the proper theory and data coordination, which is the core process of scientific thinking at the individual level. At the socio-cultural level, it can lead to cessation of constructing scientific knowledge and it can act as a mechanism in the process of using science for specific socio-cultural beliefs or purposes, thereby hindering the development of science and technology based on rationale and objective scientific thinking. Quantitative analysis with in-depth interview data showed that graduate students and the young researcher's experienced motivated reasoning results in trying to protect prior beliefs, make hasty conclusions, protecting socio-cultural belief or rationalizing decisions made by their community. Their motivated reasoning could become an obstacle in constructing valid science and technology knowledge through appropriate theory and evidence coordination. Based on these findings we discussed science education for improving scientific thinking.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.510-512
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• 2012

상황인식 서비스 분야에서 불확실한 데이터를 추론하는 것은 매우 어렵고 복잡하다. 이러한 상황정보들에서 얻어지는 데이터는 불확실성을 내포하고 있어서 불확실한 추론 결과를 초래할 수 있다. 비록 불확실성 문제들을 해결하기 위해 퍼지 이론, 뉴런 네트워크, 동적 베이지안 네트워크, 은닉 마르코프 모델과 같은 여러 종류의 방법들이 제시되었지만 이러한 방법들은 가설들을 하나의 숫자에 의해 신뢰의 정도를 표시하기 때문에 많은 어려움이 있다. 본 논문에서는 사용자들이 제공받는 서비스들에 대하여 만족도를 평가한 후 수집된 데이터를 활용하여 사용자들의 상관 관계를 분석한다. 그리고 Dempster-Shafer 이론을 사용하여 사용자들로부터 측정된 믿음 값을 융합한다. 이는 불확실성 값을 낮추어 추론결과의 정확성을 높이고 증거구간을 재설정하여 사용자들에게 신뢰성 있는 적응형 서비스를 제공하게 한다.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.30-43
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• 2022

In this study, elementary students' science communication competence was investigated based on the grammatical features expressed in their language-use in classroom discourse and science writings. The classes were designed to integrate the evidence-based reasoning framework and traditional learning cycle and were conducted on fifth graders in an elementary school. Eight elementary students' discourse data and writings were analyzed using lexico-grammatical resource analysis, which examined the discourse text's content and logical relations. The results revealed that the student language used in analyzing data, interpreting evidence, or constructing explanations did not precisely conform to the grammatical features in science language use. However, they provided examples of grammatical metaphors by nominalizing observed events in the classroom discourses and those of causal relations in their writings. Thus, elementary students can use science language grammatically from science language-use experiences through listening to a teacher's instructional discourses or recognizing the grammatical structures of science texts in workbooks. The opportunities in which elementary students experience the language-use model in science learning need to be offered to understand the appropriate language use in the epistemic context of evidence-based reasoning and learn literacy skills in science.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.31-40
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• 2000

A robust scheme of locating and counting the number of vehicles m urban traffic scenes, a core component of vision-based traffic monitoring systems, is presented The method is based on the evidential reasoning, where vehicle evidences m the background subtraction Image are obtained by a new locally optimum thresholding, and the evidences are merged by three heuristic rules using the geometric constraints The locally optimum thresholding guarantees the separation of bright and dark evidences of vehicles even when the vehicles are overlapped or when the vehicles have similar color to the background Experimental results on diverse traffic scenes show that the detection performance is very robust to the operating conditions such as the camera location and the weather The method may be applied even when vehicle movement is not observed since a static Image IS processed without the use of frame difference.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.63-73
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• 2006

대부분의 CEO들은 대규모 투자가 선행되는 정보시스템의 화폐적 가치에 확신을 가지고 싶어 한다. 지금까지 MIS 연구자들은 정보시스템의 조직적 성과에 관한 여러 가지 간접적인 증거를 보여주었으나 경영자들이 요구하는 정보시스템에 대한 화폐적 확신을 주는데 실패하였다. 본 연구는 최근 각 기업들이 도입하고 있는 BSC의 성과지표 중 정보시스템 관련 지표를 활용하여 기업의 계량적 및 비계량적 성과측정을 활용함으로써 조직의 정보시스템 성과를 화폐가치로 환산할 수 있는 방법을 제시하고자 한다. 이때 사례기반추론 시스템을 활용하면 사례베이스로부터 유사사례를 도출하고 이를 통하여 정보시스템 도입에 필요한 주요 정보를 추론할 수 있게 되어 조직에서 도입할 정보시스템의 잠재적 화폐가치를 어느 정도 가늠할 수 있다. 본 연구는 정보시스템의 화폐적 가치분석을 위하여 실물옵션 가격결정모형을 활용하였고 객관적 화폐가치 추론을 위한 웹 사이트 구축을 목표로 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.310-313
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• 2017

범죄수사 과정에서 많은 양의 데이터를 시간 내에 분석하는 것은 성공적인 포렌식의 필수 요소이다. 컴퓨터와 사람 모두에게 있어, 시간과 자원의 제한은 수사 결과에 부정적인 영향을 가져온다. 그러므로 현재 사용되고 있는 다양한 포렌식 도구에는 시간과 자원의 효율적인 사용이 필요하다. 사례기반추론 및 멀티에이전트 시스템과 같은 인공지능 기반의 도구를 통해 디지털 포렌식 수사를 효과적으로 도울 수 있다. 본 논문에서는 인공지능을 활용한 지능형 포렌식 도구 및 프레임워크를 분석하고, 오늘날의 프레임워크의 한계점과 미래에 관해 논의한다. 인공지능 기반 시스템의 목적은 수사에서의 증거를 포함한 데이터를 분석하고 연관성을 밝힘으로서 포렌식 전문가에게 중요한 단서를 제공하고 직접 분석해야하는 데이터의 양을 줄이는 것에 있다. 이러한 인공지능의 활용은 많은 양의 데이터를 수사할 때 사람이 간과할 수 있는 증거들을 연결시켜주는 데에 큰 도움이 된다.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.