• Journal of Magnetics
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• 제22권1호
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• 한국수소및신에너지학회논문집
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• 제33권4호
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• pp.318-329
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• 2022

This study analyzed keyword networks and Author's Affiliation networks of hydrogen-related papers published in Korea Citation Index (KCI) journals from 2016 to 2020. The study investigated co-occurrence patterns of institutions over time to examine collaboration trends of hydrogen scholars. The study also conducted frequency analysis of keyword networks to identify key topics and visualized keyword networks to explore topic trends. The result showed Collaborative research between institutions has not yet been extensively expanded. However, collaboration trends were much more pronounced with local universities. Keyword network analysis exhibited continuing diversification of topics in hydrogen research of Korea. In addition centrality analysis found hydrogen research mostly deals with multi-disciplinary and complex aspects like hydrogen production, transportation, and public policy.

• 웰빙융합연구
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• 제5권4호
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• pp.33-37
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• 2022

Purpose: This study aims to understand the trend and central concept of domestic researches on laughter therapy. For the analysis, this study used total 72 theses verified by inputting the keyword 'laughter therapy' from 2007 to 2021. Research design, data and methodology: This study performed the development and analysis of keyword co-occurrence network, analyzed the types of researches through topic modeling, and verified the visualized word cloud and sociogram. The keyword data that was cleaned through preprocessing, was analyzed in the method of centrality analysis and topic modeling through the 1-mode matrix conversion process by using the NetMiner (version 4.4) Program. Results: The keywords that most appeared for last 14 years were laughter therapy, depression, the elderly, and stress. The five topics analyzed in thesis data from 2007 to 2021 were therapy, cognitive behavior, quality of life, stress, and the elderly. Conclusions: This study understood the flow and trend of research topics of domestic laughter therapy for last 14 years, and there should be continuous researches on laughter therapy, which reflects the flow of time in the future.

• 한국IT서비스학회지
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• 제14권1호
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• pp.237-249
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• 2015

The volume of unstructured text data generated by various social media has been increasing rapidly; therefore, use of text mining to support decision making has also been increasing. Especially, issue Clustering-determining a new relation with various issues through clustering-has gained attention from many researchers. However, traditional issue clustering methods can only be performed based on the co-occurrence frequency of issue keywords in many documents. Therefore, an association between issues that have a low co-occurrence frequency cannot be discovered using traditional issue clustering methods, even if those issues are strongly related in other perspectives. Therefore, issue clustering that fits each of criteria needs to be performed by the perspective of analysis and the purpose of use. In this study, a multi-dimensional issue clustering is proposed to overcome the limitation of traditional issue clustering. We assert, specifically in this study, that issue clustering should be performed for a particular purpose. We analyze the results of applying our methodology to two specific perspectives on issue clustering, (i) consumers' interests, and (ii) related R&D terms.

• 한국비블리아학회지
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• 제22권2호
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• pp.235-248
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• 2011

본 연구에서는 Web of Science DB를 대상으로 주제어(topic)가 web archiving에 해당하는 288건의 논문을 대상으로 동시출현단어 네트워크 분석을 수행하였다. 분석 결과 웹 아카이빙 주제 영역에서는 의학영역 정보기술 및 시스템과 관련된 이미지 아카이빙 관련연구들이 가장 중점적으로 수행되어 왔다. 문헌정보학 및 기록관리학 영역에서의 웹 아카이빙 연구는 크게 웹 아카이빙 및 디지털 보존 프로젝트 주제와 웹 아카이빙툴과 방법론 주제를 중심으로 수행되어왔으며, 향후 웹 아카이빙 소프트웨어 및 툴 관련 연구가 활성화될 수 있을 것으로 예측된다.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• ETRI Journal
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• 제40권6호
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• pp.745-758
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• 2018

A wide range of studies in various disciplines has focused on the Internet of Things (IoT) and cyber-physical systems (CPS). However, it is necessary to summarize the current status and to establish future directions because each study has its own individual goals independent of the completion of all IoT applications. The absence of a comprehensive understanding of IoT and CPS has disrupted an efficient resource allocation. To assess changes in the knowledge structure and emerging technologies, this study explores the dynamic research trends in IoT by analyzing bibliographic data. We retrieved 54,237 keywords in 12,600 IoT studies from the Scopus database, and conducted keyword frequency, co-occurrence, and growth-rate analyses. The analysis results reveal how IoT technologies have been developed and how they are connected to each other. We also show that such technologies have diverged and converged simultaneously, and that the emerging keywords of trust, smart home, cloud, authentication, context-aware, and big data have been extracted. We also unveil that the CPS is directly involved in network, security, management, cloud, big data, system, industry, architecture, and the Internet.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.784-791
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• 2022

Tack coat is a thin layer of asphalt between the existing pavement and asphalt overlay. During construction, insufficient tack coat layering can later cause surface defects such as slippage, shoving, and rutting. This paper proposed a method for tack coat inspection improvement using an unmanned aerial vehicle (UAV) and deep learning neural network for automatic non-uniform assessment of the applied tack coat area. In this method, the drone-captured images are exploited for assessment using a combination of Mask R-CNN and Grey Level Co-occurrence Matrix (GLCM). Mask R-CNN is utilized to detect the tack coat region and segment the region of interest from the surroundings. GLCM is used to analyze the texture of the segmented region and measure the uniformity and non-uniformity of the tack coat on the existing pavements. The results of the field experiment showed both the intersection over union of Mask R-CNN and the non-uniformity measured by GLCM were promising with respect to their accuracy. The proposed method is automatic and cost-efficient, which would be of value to state Departments of Transportation for better management of their work in pavement construction and rehabilitation.

• 한국산업융합학회 논문집
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• 제26권6_3호
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• pp.1183-1195
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• 2023

In this study, we collected and analyzed domestic and international studies related to watersheds in the forest sector. Keyword co-occurrence analysis was conducted using the VOSviewer program to identify the research areas of domestic and international studies and the network structure to compare research trends. As a result, the number of research articles in international watershed-related studies showed an overall increasing trend, and the research areas were diverse and located close to each other, indicating that many convergence studies were conducted. On the other hand, the number of papers in domestic watershed-related studies seems to have stagnated overall from the past to the present, and the research areas are mainly focused on forest disasters and hydrology, with limited interdisciplinary convergence studies. In addition, in both domestic and international studies, watersheds are currently mentioned as research sites rather than management or analysis units in the forest sector. It is important to actively promote interdisciplinary research in Korea to provide a scientific and balanced basis for watershed-level forest management planning.

• 한국문헌정보학회지
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• 제57권3호
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• pp.351-379
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• 2023

본 연구는 국내외 메타버스 관련 연구의 동향을 파악하기 위해 한국학술지인용색인(KCI)과 Web of Science(WoS), 그리고 Web of Science - CPCI(Conference Proceeding Citation Index)에서 메타버스를 검색어로 입력하여 KCI에서 913편, WoS에서 232편, WoS-CPCI에서 277편의 논문을 수집하였고, 각각 2,644개, 885개, 787개의 저자 키워드를 추출하여 동시출현단어 분석을 수행하였다. 정량분석을 통해 메타버스 관련 연구가 최근 들어 양적으로 폭증하였고, 국내에서 는 학제간연구, 컴퓨터학, 교육학 등의 주제분야에서 주로 연구되고 있으며, WoS에서는 경영·경제 분야에서, WoS-CPCI 에서는 컴퓨터공학 분야에서 주로 연구되고 있음을 알 수 있었다. 키워드 네트워크 분석에서는 모든 데이터베이스에서 Virtual Reality, Augmented Reality 등 메타버스의 기술적 측면과 관련된 용어들의 전역중심성이 공통적으로 높게 나타났으며 군집분석을 통해 국내에서는 교육 관련 연구와 메타버스 플랫폼에 관한 키워드의 군집이 포함되고, WoS에서는 계량서지학적 분석과 관련된 키워드 군집이 생성되었으며, WoS-CPCI는 주로 메타버스의 기술적 측면에 대한 키워드 군집이 주로 나타났다.