• Child Health Nursing Research
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• 제28권3호
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• pp.208-217
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• 2022

Purpose: This study investigated childhood cancer survivors' behavior related to a healthy lifestyle during their survival period by comparing reports between childhood cancer survivors and their parents. Methods: In this comparative descriptive study, a survey was conducted with a 33-item questionnaire and one open-ended question about areas for improvement. The participants comprised 69 childhood cancer survivors and 69 of their parents, for a total of 138. Results: The total mean healthy lifestyle score, on a 4-point Likert scale, reported by childhood cancer survivors was 2.97, while that reported by their parents was 3.03. No significant differences in children's healthy lifestyles were found between childhood cancer survivors' and their parents' reports (t=0.86, p=.390). For the open-ended question, the main keywords based on the results of degree and eigenvector centrality were "exercise", "unbalanced diet", and "food". These keywords were present in both the children's and parents' responses. Conclusion: Obtaining information on childhood cancer survivors' healthy lifestyles based on reports from themselves and their parents provides meaningful insights into the improvement of health care management. The results of this study may be used to develop and plan healthy lifestyle standards to meet childhood cancer survivors' needs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.894-907
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• 2022

The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers. The convolutional layer features will consume considerable time and memory if used directly to match an image. Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching. First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image. Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector. Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement. We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k. These experimental results indicate that the global feature extracted by the new method can better describe an image.

• 한국IT서비스학회지
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• 제22권1호
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• pp.57-74
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• 2023

It has been pointed out as a limitation that the rank of some efficient DMUs(decision making units) cannot be discriminated due to the relativity nature of efficiency measured by DEA(data envelopment analysis), comparing the production structure. Recently, to solve this problem, a DEA-SNA(social network analysis) model that combines SNA techniques with data envelopment analysis has been studied intensively. Several models have been proposed using techniques such as eigenvector centrality, pagerank centrality, and hypertext induced topic selection(HITS) algorithm, but DMUs that cannot be ranked still remain. Moreover, in the process of extracting latent information within the DMU group to build effective network, a problem that violates the basic assumptions of the DEA also arises. This study is meaningful in finding the cause of the limitations by comparing and analyzing the characteristics of the DEA-SNA model proposed so far, and based on this, suggesting the direction and possibility to develop more advanced model. Through the results of this study, it will be enable to further expand the field of research related to DEA.

• Child Health Nursing Research
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• 제29권3호
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• pp.182-194
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• 2023

Purpose: This study analyzed research trends in infant and toddler rearing behavior among family caregivers over a 10-year period (2010-2021). Methods: Text network analysis and topic modeling were employed on data collected from relevant papers, following the extraction and refinement of semantic morphemes. A semantic-centered network was constructed by extracting words from 2,613 English-language abstracts. Data analysis was performed using NetMiner 4.5.0. Results: Frequency analysis, degree centrality, and eigenvector centrality all revealed the terms ''scale," ''program," and ''education" among the top 10 keywords associated with infant and toddler rearing behaviors among family caregivers. The keywords extracted from the analysis were divided into two clusters through cohesion analysis. Additionally, they were classified into two topic groups using topic modeling: "program and evaluation" (64.37%) and "caregivers' role and competency in child development" (35.63%). Conclusion: The roles and competencies of family caregivers are essential for the development of infants and toddlers. Intervention programs and evaluations are necessary to improve rearing behaviors. Future research should determine the role of nurses in supporting family caregivers. Additionally, it should facilitate the development of nursing strategies and intervention programs to promote positive rearing practices.

• 무역학회지
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• 제47권6호
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• pp.189-209
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• 2022

This study aims to analyze Latin America and the Caribbean papers published in Korea during the past 2000-2020 years. Through this study, it is possible to understand the main subject and direction of research in Korea's Latin America and the Caribbean area. As the research mythologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. During 2011-2020 years, the most frequent keywords were changed from Neoliberalism and culture to policy education, and economy related words. The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, language, and Neoliberalism were the most important keywords during 2000-2010 years but economy, labor trade, industry, development became the most important keywords during 2011-2020 years in topics.

• 무역학회지
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• 제47권4호
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• pp.137-159
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• 2022

This study aims to analyze international trade papers published in Korea during the past 2002-2022 years. Through this study, it is possible to understand the main subject and direction of research in Korea's international trade field. As the research mythologies, this study uses the big data analysis such as the text mining and Social Network Analysis such as frequency analysis, several centrality analysis, and topic analysis. After analyzing the empirical results, the frequency of key word is very high in trade, export, tariff, market, industry, and the performance of firm. However, there has been a tendency to include logistics, e-business, value and chain, and innovation over the time. The degree and closeness centrality analyses also show that the higher frequency key words also have been higher in the degree and closeness centrality. In contrast, the order of eigenvector centrality seems to be different from those of the degree and closeness centrality. The ego network shows the density of business, sale, exchange, and integration appears to be high in order unlike the frequency analysis. The topic analysis shows that the export, trade, tariff, logstics, innovation, industry, value, and chain seem to have high the probabilities of included in several topics.

• 한국산업융합학회 논문집
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• 제27권2_2호
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• pp.343-356
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• 2024

Super app is an application that provides a variety of services in a unified interface within a single platform. With the acceleration of digital transformation, super apps are becoming more prevalent. This study aims to suggest service enhancement measures by analyzing the user review data before and after the transition to a super app. To this end, user review data from a payment-based super app(Shinhan Play) were collected and studied via topic modeling. Moreover, a matrix for assessing the importance and usefulness of topics is introduced, which relies on the eigenvector centrality of the inter-topic network obtained through topic modeling and the number of review recommendations. This allowed us to identify and categorize topics with high utility and impact. Prior to the transition, the factors contributing to user satisfaction included 'payment service,' 'additional service,' and 'improvement.' Following the transition, user satisfaction was associated with 'payment service' and 'integrated UX.' Conversely, dissatisfaction factors before the transition encompassed issues related to 'signup/installation,' 'payment error/response,' 'security authentication,' and 'security error.' Following the transition, user dissatisfaction arose from concerns regarding 'update/error response' and 'UX/UI.' The research results are expected to be used as a basis for establishing strategies to strengthen service competitiveness by making super app services more user-oriented.

• Asia pacific journal of information systems
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• 제29권3호
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• pp.448-467
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• 2019

In a new age of smart tourism, free independent travelers (FITs) choose their travel routes in a more diversified and less predictable way with the aid of smart services. This paper focuses on the movements of Chinese outbound FITs in the U.S. in the year of 2018. 110 places to visit (destinations) extracted from 122 travel routes recommendations on Qyer.com, a major online travel community in China, are analyzed with social network analysis (SNA). Based on the results of SNA, employing degree centrality, eigenvector centrality, betweenness centrality, network visualization, and cluster diagram methods, some preferred cities and natural attractions outside city centers (i.e., New York City (NYC), Los Angeles, San Francisco, Washington D.C., and Niagara Falls) are identified. Moreover, it is found that NYC in the East and Los Angeles in the West play a major role in the movements of Chinese FITs. This study contributes to the body of knowledge on tourist destination movements and provides valuable implications for smart service development in the tourism and hospitality industry.

• 지능정보연구
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• 제24권1호
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• pp.25-52
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• 2018

본 연구는 소비자들이 상권에 대하여 수행하는 웹 탐색 활동과 감성평가를 반영하는 데이터인 지역구 연관감성어휘를 기반으로 서울시 내 대형 상업 공간으로 정의할 수 있는 각 지역구 간의 연관 감성 네트워크에 대하여 소셜 네트워크 분석을 수행하였다. 나아가 도출한 소셜 네트워크 지표를 지역구 공공 데이터와 결합하여 보다 다각적 측면을 고려한 지역구 상권의 매출액에 영향을 미치는 요인들을 검증하였고 그 영향력의 변화 또한 확인해 보았다. 정적 데이터로 표현되는 공공 데이터만을 통해 구성된 모형으로도 높은 설명력을 가지는 것을 확인할 수 있었으나, 소셜 네트워크 분석 결과로 도출된 네트워크 지표와 결합된 모형에서는 그 설명력이 더욱 향상된 것이 확인되었다. 공공 데이터에 대한 회귀 분석 결과, 투입된 22개의 요인들 중 '골목 상권 수,' '1인당 거주면적,' '주거환경만족도,' '거래증감률,' '3년 이상 생존율'의 5개의 요인이 지역구 상권 매출액에 유의한 영향을 미치는 것이 확인되었다. 이후 공공 데이터와 네트워크 지표 결합 모형에서 투입된 지표들은 '에고 네트워크의 밀도,' '연결 중심성,' '근접 중심성,' '매개 중심성,' '아이겐벡터 중심성'이며, 이 중 '연결 중심성'과 '아이겐벡터 중심성'이 매출액에 유의한 영향을 미치며 모형 내에서 가장 높은 영향력을 보유한 것이 확인되었다. 본 연구는 각 상권이 소비자가 원하는 감성을 고려한 도시 전략 계획 수립과 이행의 실증적 근거로 활용될 수 있을 것이며, 상권에 진입하거나 재창업하는 자영업자나 잠재 창업자를 바탕으로 지역구 상권이 보유한 감성과 그 관계 구조를 고려한 상권 진입 방향성을 제공할 수 있을 것이다.

• 한국산학기술학회논문지
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• 제13권2호
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• pp.827-833
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• 2012

본 논문은 2D/3D 변환에서 깊이정보 생성을 위해 연산량을 감소시키는 레벨 간소화 기법을 적용하고 객체의 고유벡터를 이용하여 노이즈를 제거한 Optical flow를 이용하는 방법을 제안한다. Optical flow는 깊이정보를 생성하기 위한 방법 중 하나로 두 프레임간의 픽셀의 변화 벡터 값을 나타내어 움직임 정보를 나타내며 픽셀 단위로 처리하므로 정확도가 높다. 그러나 픽셀 단위 연산으로 긴 연산 시간이 소요되며 모든 픽셀을 연산하는 특성상 노이즈가 생길 수 있는 문제점이 있다. 본 논문에서는 이를 해결하기 위해 레벨 간소화 과정을 거쳐 연산 시간을 단축하였고 Optical flow를 영상에서 고유벡터를 갖는 영역에만 적용하여 노이즈를 제거한 뒤 배경 영역에 대한 깊이 정보를 에지 영상을 이용하여 생성하는 방법을 제안하였다. 제안한 방법으로 깊이정보를 생성한 뒤 DIBR(Depth Image Based Rendering)으로 2차원 영상을 3차원 입체 영상으로 변환하였고 SSIM(Structural SIMilarity index)으로 최종 생성된 영상의 오차율을 분석하였다.