• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권11호
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• 한국초등과학교육학회지:초등과학교육
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• 제37권1호
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• pp.39-53
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• 2018

The purpose of this study was to investigate the conceptual structure of subjects related to matter having pre-service elementary school teachers by applying semantic network analysis (SNA). The analyzed concepts in the subjects of matter were 6 words such as 'atom', 'molecule', 'ion', 'electron', 'matter' and 'particle'. The results of SNA of the concepts are as follows : 1. In the semantic network of 'atom', words having a high betweenness centrality were linked with the words based on both the scientific context and the everyday context. 2. The network of 'molecule' was analyzed to be more organized than the network of the 'atom'. 3. In the network of 'ion', the group of words of the scientific context was distinguished from the group of words of the everyday context. 4. The network of 'electron' was analyzed to be more oriented on electricity and magnetism in the field of physics. 5. In the network of 'matter', the words related to compounds were linked with knowledge of history of science. 6. The network of 'particle' was not structured with words based on particulate nature of matter.

• 한국의류학회지
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• 제42권3호
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• pp.428-437
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• 2018

This study derives evaluation terms by analyzing the semantic relationship between design elements and sentiment terms in regards to fashion design. As for research methods, a total of 38,225 texts from Daum and Naver Blogs from November 2015 to October 2016 were collected to analyze the parts, frequency, centrality and semantic networks of the terms. As a result, design elements were derived in the form of a noun while fashion image and user's emotional responses were derived in the form of adjectives. The study selected 15 noun terms and 52 adjective terms as evaluation terms for men's striped shirts. The results of semantic network analysis also showed that the main contents of the users of men's striped shirts were derived as characteristics of expression, daily wear, formation, and function. In addition, design elements such as pattern, color, coordination, style, and fit were classified with evaluation results such as wide, bright, trendy, casual, and slim.

• 영재교육연구
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• 제27권1호
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• pp.17-35
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• 2017

이 연구는 대학부설 과학영재교육원의 교육프로그램에 제시된 학습목표를 Bloom의 신교육목표분류체계와 언어네트워크분석 방법을 통해 분석하고 결과를 비교함으로써 학습목표를 분석할 때 언어네트워크분석 방법의 적용 가능성을 알아보기 위한 것이다. 이를 위하여 27개 대학부설과학영재교육원의 교육프로그램 중 과학 분야 169개 주제에 제시된 702개의 학습목표를 분석대상으로 선정하여 Bloom의 신교육목표 분류체계에 따라 분류하고 코딩한 후 각 학습목표 사이의 구조적 특성을 알아보기 위해 언어네트워크분석을 사용하였다. 분석 결과로 나타난 주요 특성은 다음과 같다. 첫째, 주제 별로 사용된 학습목표의 특성을 살펴본 결과 초등은 약 3개, 중등은 약 6개의 서로 다른 범주의 학습목표가 사용되고 있었다. 둘째, 연구방법과 학교 급에 관계없이 지식차원의 사실적 지식, 개념적 지식과 인지과정 차원의 '기억하다', '이해하다', '창안하다'의 비중이 높게 나타났다. 셋째, 단순 통계 분석 결과로는 확인할 수 없지만 언어네트워크분석 방법을 통한 가중치에 근거하여 살펴본 결과 초등 단계는 과학적 사실에 대한 학습을 통해 실제실험과정에 적용해 보는 활동을 강조한 반면, 중등 단계는 이보다는 과학적 사실, 개념 자체를 이해하는 것을 더욱 강조하고 있었다. 이와 같은 결과로 볼 때 기존 단순 통계적 연구를 통해 분석한 것에 비해 보다 다양한 학습목표의 특성을 해석할 수 있는 것으로 보아 언어네트워크분석방법이 학습목표를 분석하는데 적용 가능성이 높은 것으로 판단된다.

• 임상간호연구
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• 제25권3호
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• pp.209-220
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• 2019

Purpose: A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis. Methods: From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as "nurse", "Taeum", and "bullying" were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence. Results: This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, "nurse", "hospital", "patient", and "physician" were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters. Conclusion: This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.

• 국제물리치료학회지
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• 제10권4호
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• pp.1926-1933
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• 2019

Background: Physical therapy has been widely studied in various fields, however, the academic trends and characteristics has not been systematically analyzed. Semantic network analysis is used as an approach for this study. Objective: To explore academic trends and knowledge system in the physiotherapy research in the Journal of International Academy Physical Therapy (J of IAPTR) Study design : Literature review Method: Semantic network analysis was conducted using the titles of 272 articles published in the Journal of IAPTR from 2010 to 2019. Results: Frequency analysis revealed following most frequently used key words; Stroke (27 times), Balance (21 times), Elder (13 times), Forward head posture (FHP, 11 times), Muscle activity (9 times). The relationship between the presented keywords is divided into six subgroups (FHP and pain, walk and quality, elder and balance, stroke and apoptosis, muscle strength and function) according to their correlation and frequency to be used together. Conclusion: The study is considered to be of help to researchers who want to identify research trends in physiotherapy.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• 셀메드
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• 제13권2호
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• pp.3.1-3.21
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• 2023

In this study, the drinking culture of the Choseon dynasty is examined with the text frequency analysis technique on the entire 『Choseonwangjosilok (朝鮮王朝實錄)』. This study examined a total of 1,968 volumes and 948 books about 27 kings of Choseon , which spans a total of 518 years, through web crawling on the National Institute of Korean History website. Python 3.8 was used to extract sentences related to alcohol, Rhino 1.4.5 was used for morphological analysis to extract nouns, and Gephi 0.9.2 was used for semantic network analysis. According to 『Choseonwangjosilok (朝鮮王朝實錄)』 about alcohol culture, the results of the analysis are as follow: Alcoholic beverages were more often used in court or in ritual ceremonies rather than those based on specific ingredients or manufacturing methods commonly used by the general public. regarding the ruling class through semantic network analysis l in the 『Choseonwangjosilok (朝鮮王朝實錄)』, the Choseon dynasty was found to be highly associated with political issues related to maintaining the power relations within the Korean royal court system. At times, alcohol was used to maintain personal relationships, while at other times it was seen as an essential item in state ceremonies. It was also used as a highly political means to maintain and strengthen national power.

• 복식문화연구
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• 제30권5호
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• pp.766-778
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• 2022

Image-oriented information is becoming increasingly important on social networking services (SNS); the background of this trend is the popularity of selfies. Currently, camera applications using augmented reality (AR) and artificial intelligence (AI) technologies are gaining traction. An AR camera app is a smartphone application that converts selfies into various interesting forms using filters. In this study, we investigated the change of keywords according to the time flow of selfies in Goolgle News articles through semantic network analysis. Additionally, we examined the effects of using an AR camera app on appearance satisfaction and self-esteem when taking a selfie. Semantic network analysis revealed that in 2013, postings of specific people were the most prominent selfie-related keywords. In 2019, keywords appeared regarding the launch of a new smartphone with a rear-facing camera for selfies; in 2020, keywords related to communication through selfies appeared. As a result of examining the effect of the degree of use of the AR camera app on appearance satisfaction, it was found that the higher the degree of use, the higher the user's interest in appearance. As a result of examining the effect of the degree of use of the AR camera app on self-esteem, it was found that the higher the degree of use, the higher the user's negative self-esteem.

• 지능정보연구
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• 제10권2호
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• pp.1-13
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• 2004

상품검색은 고객들이 전자상거래의 접촉을 시작하는 인터페이스로서 매우 중요한 프로세스이다. 또한 전자상거래는 고객들에게 검색 시 쉽게 접근할 수 있는 프로세스를 제공하여야 한다. 특히 World Wide Web에서 상품정보는 광범위한 고객들이 신속하게 팽창하는 정보를 추적하기 위해서 통합과 표준화가 이뤄져야 한다. 상품 카탈로그(catalogue)에 대한 국제 표준화가 다양한 분야와 업종에서 구축되어져 왔는데, 요즈음은 UNSPSC((Universal Standard Products and Services Classification) 코드로의 수렴에 대한 논의가 활발해지고 있다. 이 표준을 채택하여 이 논문은 다국어상품검색 에이전트의 아키텍쳐(architecture)를 설계한다. 이 아키텍쳐는 중앙등록기 모델의 상품 카탈로그 관리를 기반으로 하여 분산처리의 update프로세스를 채택한다. 또한 이 아키텍쳐는 구매자 관점과 공급자 관점을 모두 고려한다. 상품정보의 일관성과 버전 관리는 UNSPSC코드 시스템에 의하여 제어된다. 고객이 사용하기 편리하도록 표준화에 포함되어져 있지 않은 상품명과 다국어 상품명은 Semantic Network, 시소러스(thesaurus)와 Semantic Web의 상품명 온톨로지 등을 활용하여 해결한다. 이를 위한 알고리즘들을 설계하고 또한 구현한다.