• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1329-1341
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• 2022

Destroying the marine environment and marine ecosystem and causing marine accidents, marine debris is generated every year, and among them, submerged marine debris is difficult to identify and collect because it is on the seabed. Therefore, deep-learning-based semantic segmentation was experimented on waste fish nets and waste ropes using underwater images to identify efficient collection and distribution. For segmentation, a high-resolution network (HRNet), a state-of-the-art deep learning technique, was used, and the performance of each optimizer was compared. In the segmentation result fish net, F1 score=(86.46%, 86.20%, 85.29%), IoU=(76.15%, 75.74%, 74.36%), For the rope F1 score=(80.49%, 80.48%, 77.86%), IoU=(67.35%, 67.33%, 63.75%) in the order of adaptive moment estimation (Adam), Momentum, and stochastic gradient descent (SGD). Adam's results were the highest in both fish net and rope. Through the research results, the evaluation of segmentation performance for each optimizer and the possibility of segmentation of marine debris in the latest deep learning technique were confirmed. Accordingly, it is judged that by applying the latest deep learning technique to the identification of submerged marine debris through underwater images, it will be helpful in estimating the distribution of marine sedimentation debris through more accurate and efficient identification than identification through the naked eye.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.45-67
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• 2015

The purpose of this study is to analyse the semantic relations of terms in STNet, a structured terminology dictionary based on author keywords of humanities, social sciences, and art and sport in the Korea Citation Index (KCI) and to describe the procedure for optimizing the relation types and specifying the name of relationships. The results indicate that four logical criteria, such as creating new names for relationships or limitation of typing the relationship by the appearance frequency of same type, consideration of direction of relationship, reflection to accept the existing name of relationships, are required for the optimization of the typing and naming the relationships. We applied these criteria to the relationships in the class "real person" of STNet and the result shows that 1,135 out of 1,743 uncertain relationships such as RT, RT_X or RT_Y are specified and clarified. This rate of optimization with ca. 65% represents the usefulness of the criteria applicable to the cases of database construction and retrieval.

• Annual Conference on Human and Language Technology
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• 2001.10d
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• pp.538-545
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• 2001

본 연구에서는 오픈 도메인에서 동작할 수 있는 질의 응답 시스템(Open-domain Question Answer ing System)을 구현하고 영어권 TREC에 참가한 결과를 기술하였다. 정답 유형을 18개의 상위 노드를 갖는 계층구조로 분류하였고, 질문 처리에서는 LSP(Lexico-Semantic Pattern)으로 표현된 문법을 사용하여 질문의 정답 유형을 결정하고, lemma 형태와 WordNet 의미, stem 형태의 3가지 유형의 키워드로 구성된 질의를 생성한다. 이 질의를 바탕으로, 패시지 선택에서는 문서검색 엔진에 의해 검색된 문서들을 문장단위로 나눠 정수를 계산하고, 어휘체인(Lexical Chain)을 고려하여 인접한 문장을 결합하여 패시지를 구성하고 순위를 결정한다. 상위 랭크의 패시지를 대상으로, 정답 처리에서는 질문의 정답 유형에 따라 품사와 어휘, 의미 정보로 기술된 LSP 매칭과 AAO (Abbreviation-Appositive-Definition) 처리를 통해 정답을 추출하고 정수를 계산하여 순위를 결정한다. 구현된 시스템의 성능을 평가하기 위해 TREC10 QA Track의 main task의 질문들 중, 200개의 질문에 대해 TRIC 방식으로 자체 평가를 한 결과, MRR(Mean Reciprocal Rank)은 0.341로 TREC9의 상위 시스템들과 견줄 만한 성능을 보였다.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.107-112
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• 2016

The Sejong Electronic(machine-readable) Dictionary, developed by the 21st century Sejong Plan, contains systematically organized information on Korean words. It helps to solve problems encountered in the electronic formatting of the still-commonly-used hard-copy dictionary. The Sejong Electronic Dictionary, however has a limitation relate to sentence structure and selection-restricted nouns. This paper discuses the limitations of word-sense disambiguation(WSD) that uses subcategorization information suggested by the Sejong Electronic Dictionary and generalized selection-restricted nouns from the Korean Lexico-semantic network. An alternative method that utilized semi-supervised learning, the chi-square test and some other means to make WSD decisions is presented herein.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.563-573
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• 2006

Cyber-infrastructures for scientific and engineering applications require integrating heterogeneous legacy data in different formats and from various domains. Such data integration raises challenging issues: (1) Support for multiple independently-managed schemas, (2) Ease of schema evolution, and (3) Simple schema mappings. In order to address these issues, we propose a novel approach to semantic integration of scientific data which uses XML schemas and RDF-based schema mappings. In this approach, XML schema al-lows scientists to manage data models intuitively and to use commodity XML DBMS tools. A simple RDF-based ontological representation scheme is used for only structural relations among independently-managed XML schemas from different institutes or domains We present the design and implementation of a prototype system developed for the national cyber-environments for civil engi-neering research activities in Korea (similar to the NEES project in USA) which is called KOCEDgrid (http://www.koced.net).

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.500-505
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• 2015

The Sejong Electronic(machine readable) Dictionary, which was developed by the 21 century Sejong Plan, contains a systematic of immanence information of Korean words. It helps in solving the problem of electronical presentation of a general text dictionary commonly used. Word sense disambiguation problems can also be solved using the specific information available in the Sejong Electronic Dictionary. However, the Sejong Electronic Dictionary has a limitation of suggesting structure of sentences and selection-restricted nouns. In this paper, we discuss limitations of word sense disambiguation by using subcategorization information as suggested by the Sejong Electronic Dictionary and generalize selection-restricted noun of argument using Korean Lexico-semantic network.

• Fashion & Textile Research Journal
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• v.21 no.3
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• pp.277-287
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• 2019

This study analyzes the online issues of design innovation by a fashion creative director. The study selected fashion house Gucci as the main subject and analyzed social media posts. As for study methods, a social matrix program Textom 2.0 collected 13,014 nouns and adjectives using 'Gucci Collection' as a search keyword from Naver Blogs from March to August 2014 and from March to August 2016. Design related issues were derived through semantic network analysis using Ucinet6 and the NetDraw program. The results of the keyword frequency analysis showed that social media user interest for the Gucci collection increased based on the rapid increase in the number of posts from 1,064 to 2,126 after changing the fashion creative director. The results of visualization of semantic network analysis and content analysis also showed that the main issues related to the Gucci collection design changed after the replacement of the fashion creative director. The study found that issues formed around the product information worn by celebrities for promotion purposes during the 2014 period; however, during the 2016 period, issues were formed around 'vintage' and 'retro' runway concepts with design styles related to Alessandro Michele, the new creative director.

• Journal of Fashion Business
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• v.23 no.3
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• The Journal of Korean Academic Society of Nursing Education
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• v.28 no.4
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• pp.400-410
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• 2022

Purpose: This study aimed to understand the keywords and major topics of the educational goals and objectives of nursing educational institutions in South Korea. Methods: From May 10 to May 20, 2022, the educational goals and objectives of all 201 nursing educational institutions in South Korea were collected. Using the NetMiner program, degree and degree centrality, semantic structure, and topic modeling were analyzed. Results: The top keywords and semantic structures of educational goals included 'respect for human (life)-spirit-science-based on, global-competency-professional nurse-nursing personnel-training, professional-science-knowledge-skills, and patients-therapeutic care-relationship.' The educational goals' major topics were clients well-being based on science and respect for human life, a practicing nurse with capabilities and spirit, fostering a nursing personnel with creativity and professionalism, and training of global nurses. The top keywords and semantic structures of the educational objectives included 'holistic care-nursing-research-action-capability, critical thinking-health-problem solving-capability, and efficiency-communication-collaboration-capability.' The educational objectives' major topics were 'nursing professionalism, communication and problem-solving capability; a change of healthcare environments and a progress of nursing practices; fostering professional nurses with creativity and global capability; and clients' health and nursing practice.' Conclusion: Educational goals in nursing presented specific nursing values and concepts, such as respect for human life, therapeutic care relationships, and the promotion of well-being. Educational objectives in nursing presented the competencies of nurses as defined by the Korean Accreditation Board of Nursing Education (KABONE). Recently, the KABONE announced new program outcomes and competencies, which will require the revision of educational goals. To achieve those educational objectives, it is suggested that the expected level of competencies be clearly defined for nursing graduates.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.211-218
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• 2023

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.