• 패션비즈니스
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• 제23권3호
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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.

• 한국간호교육학회지
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• 제28권4호
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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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• 제15권3호
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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.

• 한국도서관정보학회지
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• 제51권2호
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• pp.1-21
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• 2020

본 연구는 언어 네트워크 분석을 통해 IFLA의 학교도서관 가이드라인의 언어적 의미를 파악하는데 목적이 있다. IFLA의 학교도서관 가이드라인은 2002년 초판과 2015년에 개정한 제2판이 있다. 본 연구는 학교도서관 가이드라인의 2002년판과 2015년판을 언어 네트워크의 관점에서 분석하고, 상호 비교하였다. 대상 테스트로부터 키워드들을 추출하고 동시출현관계를 바탕으로 언어 네트워크를 구성하였다. 동시출현 네트워크로부터 중심성(연결정도 중심성, 근접 중심성, 매개 중심성)을 분석하였다. 또한, 본 연구는 넷마이너4.0의 LDA 기능을 사용하여 토픽모델링 분석을 수행하였다. 본 연구의 주요 결과는 다음과 같다. 첫째, 중심성 차원에서 비교해 보면, 2015년판에서 'Program, Teaching, Reading, Inquiry, Literacy, Media' 등의 키워드가 2002년판에 비해 높게 나타나고 있다. 둘째, 2002년판의 중심성 상위 리스트에서 보이지 않던 'Inquiry'와 'Achievement' 키워드가 2015년판의 연결정도 중심성과 근접중심성에 새롭게 출현하고 있다. 셋째, 토픽 모델링의 분석 결과, 2002년판에 비해 2015년판은 학교도서관 서비스, 사서교사의 교수학습 활동, 미디어 및 정보활용교육, 교육과정 참여 등에 관한 토픽의 비중이 높아지고 있다.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2002년도 Language, Information, and Computation Proceedings of The 16th Pacific Asia Conference
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• pp.320-329
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• 2002

The issues in polysemy with respect to the verbs in WordNet will be discussed in this paper. The hypernymy/hyponymy structure of the multiple senses is observed when we try to build a bilingual network for Chinese and English. There are several types of polysemic patterns and a co-hypernym may have the same word form as its subordinates. Fellbaum (2000) dubbed autotroponymy that the verbs linked by mailer relation share the same verb form. However, her syntactic criteria seem not compatible to the hierarchies in WN. Either the criteria or the network should be reconducted. For most verbs in WN 1.7, polysemous relations are unlikely to extend over 3 levels of IS-A relation. Highly polysemous verbs are more complicated and may be involved in certain semantic structures. Semi-automatic sense grouping may be helpful for multimlinguital information retrieveal.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2003년도 춘계학술발표논문집 (중)
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• pp.919-922
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• 2003

1990년대 중반부터 최근까지 시맨틱 웹에 대한 많은 관심과 더불어 많은 연구가 진행중이다. 무한한 정보 자원을 가지고 있는 인터넷에서 자원에 대한 효율적 처리가 더욱더 강조된다. 그렇지만 시맨틱 웹에 대한 뚜렷한 결론을 내리기 힘들뿐만 아니라, 지금의 연구들에서는 시맨틱 웹에 대한 전체적 구상에 치중하고 있을 뿐, 세부적인 기술에 관한 연구는 미흡하다 최근까지의 연구의 초점은 주로 XML, XML Schema에서 RDF, RDF Schema 그리고 DAML+OIL에 이르기까지 다양한 마크업 언어의 개발 및 적용에 대한 연구이다. 이러한 연구의 결과 시맨틱 웹에서의 표현을 위한 마크업 언어에 대한 많은 성과를 가져왔지만, 시맨틱 웹의 핵심이 되는 정보의 의미적 표현은 더 많은 연구들이 요구된다. 본 논문은 시맨틱 웹의 핵심적인 부분을 차지하고 있는 온톨로지에 대한 연구이다. 최근 널리 사용되어지고 있는 온톨로지 중 하나인 WordNet을 시맨틱 웹의 온톨로지로 적용함에 있어, 발생하는 문제점을 해결하기 위한 방법을 제시한다. WordNet에 기반 한 도메인 온톨로지의 구축 및 적용에 대한 내용이 이 문제점을 해결하기 위한 본 논문의 요지이다.

• 한국정보과학회논문지:데이타베이스
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• 제36권6호
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• pp.466-475
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• 2009

다양한 온톨로지 개발로 온톨로지간에 정보공유와 재사용이 필요하게 되면서 온톨로지 매핑에 관련된 연구가 활발이 이루어지고 있다. 온톨로지 매핑 기법으로는 어휘 유사성, 구조 유사성, 인스턴스 유사성, 추론 유사성 검사 기법으로 나누어진다. 이 중 어휘 유사성 검사 기법은 대부분의 온톨로지 매핑 연구에서 사용하는 기법으로써 주로 워드넷에 정의되어 있는 동의어 집합만을 사용한다. 이에 본 연구에서는 워드넷에 정의되어 있는 동의어 집합 외에 상위어, 하위어, 전체어, 부분어 집합의 모든 단어들을 포함한 수퍼워드셋을 정의하고, 이것을 이용한 온톨로지 매핑 기법을 제안한다. 실험 결과에 의하면, 제안된 기법은 기존 온톨로지 매핑 기법보다 평균 12%까지 온톨로지 매칭율을 높인 것을 보여준다.

• 한국BIM학회 논문집
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• 제9권4호
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• pp.31-40
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• 2019

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• 대한원격탐사학회지
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• 제36권5_1호
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• pp.823-833
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• 2020

안정적인 작물 생산을 위하여 국내 농업지역에 대한 효과적인 작황 모니터링 기법의 요구가 증대되고 있다. 본 연구에서는 작물 재배지역 추출을 위하여 딥러닝 기법을 이용한 분류 모델을 개발하고, 이를 위성영상에 적용하고자 하였다. 이를 위하여, 식생분석에 유용한 blue, green, red, red-edge, NIR 밴드를 포함하고 있는 RapidEye 위성영상을 이용하여 작물 재배지역에 대한 훈련자료를 구축하고, 이를 활용하여 국내 양파 및 마늘 작물에 대한 재배면적을 추정하고자 하였다. 대기보정된 RapidEye 위성영상을 활용하여 훈련자료를 구축하였으며, 작물지역의 분류를 위하여 대표적인 의미론적 분할을 위한 딥러닝 모델인 FC-DenseNet을 이용하여 딥러닝 모델을 생성하였다. 최종적인 작물 재배지역은 지적도와의 결합을 통하여 객체 기반의 자료로 생성하였다. 실험결과, 대기보정된 훈련자료를 이용하여 학습된 FC-DenseNet 모델은 훈련에 사용되지 않은 타 지역의 작물 재배지역을 효과적으로 검출할 수 있음을 확인하였다.