• 공학교육연구
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• 제25권5호
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• pp.85-93
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• 2022

Capstone Design, a project-based learning technique, is the most important curriculum that clarifying major knowledge and cultivating the ability to apply through the process of solving problems in the industrial field centered on the student project team. Accordingly, various and extensive studies are being conducted for the successful implementation of capstone design courses. Unlike previous studies, this study aimed to quantitatively analyze the opinions that recorded the experiences and feelings of students who performed capstone design, and used text mining methodologies such as frequency analysis, correlation analysis, topic modeling, and sentiment analysis. As a result of examining the overall opinions of the latter period through frequency analysis and correlation analysis, there was a difference between the languages used by the students in the opinions according to gender and project results. Through topic modeling analysis, 'topic selection' and 'the relationship between team members' showed an increase in occupancy or high occupancy, and topics such as 'presentation', 'leadership', and 'feeling what they felt' showed a tendency to decreasing occupancy. Lastly, sentiment analysis has found that female students showed more neutral emotions than male students, and the passed group showed more negative emotions than the non-passed group and less neutral emotions. Based on these findings, students' practical recognition of the curriculum was considered and implications for the improvement of capstone design were presented.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제3권2호
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• 한국문헌정보학회지
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• 제56권1호
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• pp.151-173
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• 2022

공유경제의 개념은 기존의 경제 패러다임을 자원의 소유에서 자원에 대한 접근으로 전환하고 있으며, 이는 공공도서관의 근본적인 역할과 그 맥을 같이 하고 있다. 이에 본 연구에서는 공공도서관 이용과 공유경제의 상호 관계를 실증적으로 분석하였다. 분석 결과, 공공도서관 프로그램 참여는 공유경제에 대한 만족도와 신뢰 형성에 대한 기제로 작용하는 반면, 공공도서관 시설 및 장비의 이용은 공유경제에 대한 태도 및 참여와 상관관계가 있는 것으로 나타났다. 도서관 장서의 활용은 지식, 정보, 개인적 경험과 같은 무형 자원의 공유에 긍정적인 영향을 미치고 있다. 반면 도서관 소장 자원의 대출 이용은 공유를 통한 경제적 이득에 대한 인식에 부정적인 영향을 미치는 것으로 나타났다. 이러한 결과를 바탕으로, 공공도서관은 정보 네트워크뿐만 아니라 사회적 관계를 통한 휴먼 네트워크를 기반으로 공유경제와 상호보완적인 역할을 수행하는 방향을 고려할 필요가 있다.

• 한국컴퓨터정보학회논문지
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• 제27권9호
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• pp.139-147
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• 2022

본 연구에서는 증강현실 기술을 이용하여, 화학에 입문하는 사람이 화학 학습에 필요한 지식을 쉽게 습득할 수 있도록 모바일 애플리케이션을 개발하는 것을 목표로 한다. 본 연구에서는 2차원 형태의 그림을 인식해 화학 구조를 3차원의 개체로 증강 시켜 사용자의 화면에 보여주고, 이와 관련된 다분야의 정보를 동시에 제공하는 서비스를 활용해 새로운 화학 학습 경험을 제공하는 점이 특징이다. 이를 위해 별도의 시스템과 콘텐츠를 구성하였고, 안전하고 실시간적인 데이터 관리를 위해 로그인 API와 실시간 데이터베이스 기술을 사용하였으며, 이미지 인식 및 3차원 개체 증강 서비스를 위해 이미지 트래킹 기술을 사용하였다. 본 연구를 통한 결과는 실험을 통해 유의미한 결과를 도출하였다. 향후 연구에서는 화학 구조 데이터 라이브러리를 사용하여 효율적으로 데이터를 불러오고 출력할 수 있도록 한다.

• 대한한의학원전학회지
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• 제35권4호
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• pp.97-114
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• 2022

Objectives : Background research on the history of Huangbo's taste being written as 'pungent' was undertaken, after which its clinical meaning was examined from the medical perspective that was behind the medicinal's taste designation. Furthermore, through various understandings on the 'pungent' taste within the process of clinical application, the meaning of 'pungent' in Korean medicinal research was re-evaluated. Methods : Description of Huangbo's taste as 'pungent' as written in medical texts were chronologically examined to determine its origin. The clinical meaning of the pungent taste of Huangbo was examined within the broad medical perspective of doctors who were behind these descriptions. Results & Conclusions : The pungent taste of Huangbo was first described by Zhang Yuansu, followed by doctors of the Yishui School such as Li Dongyuan, Wang Haogu, etc., during which such knowledge was established and contributed to recognition of Huangbo's effect as tonifying Kidney deficiency and treatment of fire within water, after reaching the Kidney. Li Dongyuan understood the meaning of Huangbo's pungent taste as eliminating Yin fire and restoring the upward direction, ultimately restoring the general 'Rising-Falling-Floating-Sinking' mechanism within the context of his inner damage treatment. On the other hand, Zhu Danxi interpreted the pungentness of Huangbo based on his understanding of the nature of fire and action towards it. It seems as Huangbo's effects were understood within a relatively narrow frame, application of its pungent taste became vague, which gave rise to criticism by later period doctors, ultimately leading to an ambiguous understanding of the pungent taste of Huangbo.

• 산업융합연구
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• 제21권2호
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• pp.61-71
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• 2023

본 연구는 국내 MZ세대 대학생들의 디지털 성범죄 인식과 관련된 요인을 탐색하기 위한 서술적 연구이다. 본 연구를 위해 국내 대학생 150명을 대상으로 설문지를 배포하여 자료를 수집하였으며, 참여자들의 평균 연령은 21.17세이었다. 분석 결과, MZ세대 대학생의 디지털 성범죄 인식과 가장 관련성이 높은 변수는 성적 인식이었으며(β=-0.390, p<.001), 성별(β=0.207, p=.018)과 성적 태도(β=0.157, p=.045)가 더해져 설명력은 25.2%였다(F=17.588, p<.001). 본 연구는 MZ세대 대학생들의 올바른 디지털 성범죄 인식 형성 및 개선을 위해 대학생의 디지털 성범죄 인식과 관련된 요인을 고려한 맞춤형 교육의 필요성을 제안한다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• 디지털융복합연구
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• 제20권5호
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• pp.857-866
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• 2022

본 연구는 국내 요양병원 종사자를 대상으로 한 감염관리 수행도 관련요인을 파악하고, 각 요인별 효과크기를 산출하기 위해 실시한 메타분석 연구이다. 대상 문헌은 KMBASE, Research Information Sharing Service (RISS), Korean studies Information Service System(KISS), DataBase Periodical Information Academic(DBpia), National Library of Korea, Pubmed, EMBASE가 이용되었고, R3.5.1을 이용하여 분석하였다. 총 22개의 관련요인 중 사례수가 3개 이상 관련요인을 메타분석 한 결과, 그 효과크기가 성별(.16), 연령(.30), 학력(.32), 병상수(.28), 감염관리 교육필요성(.44), 감염관리 교육경험(.25), 감염관리 인지(지식)(.70)으로 나타났다. 이러한 결과를 바탕으로 요양병원 종사자의 감염관리 수행을 향상시키기 위한 프로그램 개발 및 적용에 근거자료로 활용될 것이다.

• Smart Structures and Systems
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• 제31권5호
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• 무역학회지
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• 제41권5호
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• pp.213-233
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• 2016

FTA 원산지 규정은 품목의 특성에 맞게 산업별 특성 및 양 당사국간의 상황을 고려하여 양 당사국간의 협상에 의해 정해지고 협정문을 통해 규정하고 있으며, 수산물의 원산지결정기준은 크게 완전생산기준을 적용하는 협정과 2단위 세번변경기준을 적용하는 협정으로 나눌 수 있다. 수산물은 HS code Chapter 3에 속하며, 일반적으로 양식 또는 어획에 의한 획득을 통해서 생산된다. 본 연구에서는 이러한 관점에서 각 협정을 비교 하였다. 어획된 수산물의 경우 공해어업과 관련한 배경 지식이 없는 상황에서 업무상 판단 오류가 발생 할 수 있다. 원산지 판정을 위한 선박의 인정 요건과 관련하여 국제 협정에 의해 선박은 등록국의 국기를 게양하고 항행 할 수 있으므로, FTA 원산지규정에 적용할 수 있도록 하는 기국주의 등에 대한 연구를 진행하였다.