• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.241-245
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• 1989

A knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. This paper presents an expert system to diagnose the various faults in power system. The developed expert system is represented considering two points; the possibility of solution and the fast processing speed. As uncertainties exist in the facts and rules which comprise the knowledge base of the expert system, Certainty Factor, which is based on the confirmation theory is used for the inexact reasoning. Also, as the diagnosis problem requires the inductive reasoning process in nature, the solution is imperfect and not unique in general. So the expert system is designed to generate all the possible hypothesis in order of the possibility and also it can explain the propagation procedure of the faults for each solution using the built in backtracking mechanism. In realization of the expert system, the processing speed is greatly dependent upon the problem representation, reasoning scheme and search strategy. So, in this paper the fault diagnosis problem itself is analysed from the view point of Artificial Intelligence and as a result, the expert system has the following basic features. 1) The certainty factor is adopted in the inference engine for inexact reasoning. 2) Problem apace is represented using the problem reduction technique. 3) Bidirectional reasoning scheme is used. 4) Best first search strategy is adopted for rapid processing. The expert system was developed us ing PROLOG language.

• International Journal of Knowledge Content Development & Technology
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• 제7권3호
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• pp.5-27
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• 2017

Retrieval of scholarly articles about a specific research issue is a routine job of researchers to cross-validate the evidence about the issue. Two articles that focus on a research issue should share similar terms in their core contents, including their goals, backgrounds, and conclusions. In this paper, we present a technique CCSE ($\underline{C}ore$ $\underline{C}ontent$ $\underline{S}imilarity$ $\underline{E}stimation$) that, given an article a, recommends those articles that share similar core content terms with a. CCSE works on titles and abstracts of articles, which are publicly available. It estimates and integrates three kinds of similarity: goal similarity, background similarity, and conclusion similarity. Empirical evaluation shows that CCSE performs significantly better than several state-of-the-art techniques in recommending those biomedical articles that are judged (by domain experts) to be the ones whose core contents focus on the same research issues. CCSE works for those articles that present research background followed by main results and discussion, and hence it may be used to support the identification of the closely related evidence already published in these articles, even when only titles and abstracts of the articles are available.

• 공학교육연구
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• 제25권1호
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• pp.33-45
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• 2022

This study aims to investigate effects of scaffolding on writing apprehension and media literacy in engineering freshmen's synchronous online writing course, and the relationships between the two variables. 'Scaffolding' is in-time support provided by a teacher/tutor or competent peer that enables students to meaningfully gain skills at problem solving process. Also, it is one of the most frequently mentioned concepts in education as well as one of the more necessary teaching strategies in an online writing course. In this study, provided treatments for the experiment were supportive scaffolding for domain-specific knowledge and reflective scaffolding for meta-cognitive knowledge. Participants were 102 engineering undergraduate students, who were assigned to two experimental groups by scaffolding types. A process-based writing course in online learning environment was conducted for 8 weeks. The writing tasks were given according to writing process. The findings were that, firstly, there were statistically significant writing apprehension's reduction and self-expression's improvement through the scaffolding provided in writing class. Secondly, writing apprehension's reduction and self-expression's improvement were significant in supportive scaffolding group. Thirdly, media literacy predicted writing apprehension. The practical implications of these findings are discussed herein, with particular attention on ways for writing apprehension's reduction as well as media literacy's enhancement.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.21-29
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• 2023

ChatGPT, a chatbot based on GPT large language models, has gained immense popularity among the general public as well as domain professionals. To assess its proficiency in specialized fields, ChatGPT was tested on mainstream exams like the bar exam and medical licensing tests. This study evaluated ChatGPT's ability to answer questions related to Building Information Modeling (BIM) by testing it on Korea's BIM expertise exam, focusing primarily on multiple-choice problems. Both GPT-3.5 and GPT-4 were tested by prompting them to provide the correct answers to three years' worth of exams, totaling 150 questions. The results showed that both versions passed the test with average scores of 68 and 85, respectively. GPT-4 performed particularly well in categories related to 'BIM software' and 'Smart Construction technology'. However, it did not fare well in 'BIM applications'. Both versions were more proficient with short-answer choices than with sentence-length answers. Additionally, GPT-4 struggled with questions related to BIM policies and regulations specific to the Korean industry. Such limitations might be addressed by using tools like LangChain, which allow for feeding domain-specific documents to customize ChatGPT's responses. These advancements are anticipated to enhance ChatGPT's utility as a virtual assistant for BIM education and modeling automation.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.702-705
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• 2006

정보의 폭발적인 증가로 인해 연구 개발을 위한 전 과정 중 연구동향 분석에 많은 시간이 소모되고 있다 최근 특정 분야의 지식이 연구개발이나 제품개발로 이루어지던 시대에서 융합지식을 통한 연구개발이나 제품생산으로 빠르게 진화하고 있다. 이러한 패러다임을 수용하기 위해 기존의 독립적이고 단편적인 정보로부터 융합정보를 제공할 수 있는 체계로의 전환이 필요하게 되었다. 또한 과학 기술 정책 및 산업 정책을 수립하기 위해 최근 과학, 기술, 산업의 지식 흐름에 대한 연구가 활발히 진행되고 있으나 정량적인 분석을 활용하기란 매우 어려운 문제이다. 왜냐하면 과학-기술간 지식흐름을 분석할 수 있는 정보자원이 존재하지 않기 때문이다. 이 연구는 연구개발이나 과학기술정책 및 산업정책에 활용할 수 있는 특허정보와 학술 문헌간 강 결합 연계 체제를 갖는 프레임웍을 개발하고자 한다.

• 한국자동차공학회논문집
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• 제13권5호
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• pp.163-170
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• 2005

Urban transit is a complex system that is combined electrically and mechanically, it is necessary to construct maintenance system for securing safety accompanying high-speed driving and maintaining promptly. Expert system is a computer program which uses numerical or non-numerical domain-specific knowledge to solve problems. In this research, we intend to develop the expert system which diagnose failure causes quickly and display measures. For the development of expert system, standardization of failure code classification system and creation of BOM(Bill Of Materials) have been first performed. Through the analysis of failure history and maintenance manuals, knowledge base has been constructed. Also, for retrieving the procedure of failure diagnosis and repair linking with the knowledge base, we have built RBR(Rule Based Reasoning) engine by pattern matching technique and CBR(Case Based Reasoning) engine by similarity search method. This system has been developed based on web to maximize the accessibility.

• 국제학술발표논문집
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• The 10th International Conference on Construction Engineering and Project Management
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• pp.1256-1263
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• 2024

In the realm of Architecture, Engineering, and Construction (AEC) education, various factors play a crucial role in shaping students' acceptance of the learning environments facilitated by visualization technologies, such as virtual reality (VR). Works on leveraging the heterogeneous educational information (i.e., pedagogical data, student performance data, and student survey data) to identify essential factors influencing students' learning experience and performance in virtual environments are still insufficient. This research proposed KnowLearn, an interactive learning assistant system, to integrate an educational knowledge graph (KG) and a locally deployed large language model (LLM) to generate real-time personalized learning recommendations. As the knowledge base of KnowLearn, the educational KG accommodated multi-faceted educational information from twelve perspectives, such as the teaching content, students' academic performance, and their perceived confidence in a specific course from the AEC discipline. A heterogeneous graph attention network (HAN) was utilized to infer the latent information in the KG and, thus, identified the perceived confidence, intention to use, and performance in a relevant quiz as the top three indicators that significantly influenced students' learning outcomes. Based on the information preserved in the KG and learned from the HAN model, the LLM enhanced the personalization of recommendations concerning adopting virtual learning environments while protecting students' privacy. The proposed KnowLearn system is expected to feasibly provide enhanced recommendations on the teaching module design for educators from the AEC domain.

• Genomics & Informatics
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• 제2권3호
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• pp.142-146
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• 2004

Biological data and analysis tools are accumulated in distributed databases and web servers. For this reason, biologists who want to find information from the web should be aware of the various kinds of resources where it is located and how it is retrieved. Integrating the data from heterogeneous biological resources will enable biologists to discover new knowledge across the specific domain boundaries from sequences to expression, structure, and pathway. And inevitably biological databases contain noisy data. Therefore, consensus among databases will confirm the reliability of its contents. We have developed WeSAT that integrates distributed and heterogeneous biological databases and analysis tools, providing through Web Services protocols. In WeSAT, biologists are retrieved specific entries in SWISS-PROT/EMBL, PDB, and KEGG, which have annotated information about sequence, structure, and pathway. And further analysis is carried by integrated services for example homology search and multiple alignments. WeSAT makes it possible to retrieve real time updated data and analysis from the scattered databases in a single platform through Web Services.

• Transactions on Control, Automation and Systems Engineering
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• 제4권1호
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• pp.32-37
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• 2002

Applications of scaled telemanipulation into micro or nano world that shows many different features from directly human interfaced tools have been increased continuously. Here, we have to consider many aspects of scaling such as force, position, and impedance. For instance, what will be the possible range of force and position scaling with a specific level of performance and stability\ulcorner This knowledge of feasible staling region can be critical to human operator safety. In this paper, we show the upper bound of the product of force and position scaling and simulation results of 1DOF scaled system by using the Llewellyn's unconditional stability in continuous and discrete domain showing the effect of sampling rate.

• 한국어정보학회:학술대회논문집
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• 한국어정보학회 2017년도 제29회 한글및한국어정보처리학술대회
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• pp.111-115
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• 2017

목적 지향적 대화 시스템(Goal-oriented dialogue system)은 텍스트나 음성을 통해 특정한 목적을 수행할 수 있는 시스템이다. 최근 RNN(recurrent neural networks)을 기반으로 대화 데이터를 end-to-end learning 방식으로 학습하여 대화 시스템을 구축하는데에 활용한 연구가 있다. End-to-end 방식의 학습은 도메인에 대한 지식 없이 학습 데이터 자체만으로 대화 시스템 구축을 위한 학습이 가능하다는 장점이 있지만 도메인 지식을 학습하기 위해서는 많은 양의 데이터가 필요하다는 단점이 존재한다. 이에 본 논문에서는 도메인 특정 지식을 결합하여 end-to-end learning 방식의 학습이 가능한 Hybrid Code Network 구조를 기반으로 한국어로 구성된 식당 예약에 관련한 대화 데이터셋을 이용하여 식당 예약을 목적으로하는 대화 시스템을 구축하는 방법을 제안한다. 실험 결과 본 시스템은 응답 별 정확도 95%와 대화 별 정확도 63%의 성능을 나타냈다.