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원주시 숯가마 및 산업단지 인근에서 측정한 PM2.5 이온 및 탄소 성분 특성
홍세인,이승묵,한영지 한국대기환경학회 2023 한국대기환경학회지 Vol.39 No.2
In this study, PM2.5 samples were collected at two sampling sites near the Munmak industrial complex and a charcoal kiln, and their ionic and carbonaceous constituents were measured. Average PM2.5 concentrations were 21.8±13.1 μg/m3 and 39.7±24.0 μg/m3 at the sites near the industrial complex and the charcoal klin, respectively. At the charcoal kiln site, organic carbon (OC) contributed 50.6% to PM2.5 mass and there was a significant correlation between PM2.5 and OC, indicating that PM2.5 concentration was greatly influenced by OC. The influence of charchol kiln on EC was much lower than on OC; therefore, a very high OC/EC ratio was observed. On the other hand, the contribution of ionic constituents (including NO3 -, SO4 2-, and NH4 +) for PM2.5 was much higher at the sampling site near the industrial complex than at the charcol kiln site. With the southwesterly winds blown from the industrial complex, the concentrations of PM2.5 and ionic components significantly increased. Correlation of OC concentrations between two sampling sites was not significant, but the correlation coefficients of ionic components between two sampling sites were very high. These results suggest that OC was influenced by local sources (charcoal kiln or industrial complex) but the ionic components were considered to be more influenced by medium- or long range transport than by local emission source.
유튜브 ‘투병 브이로그’ 콘텐츠 시청행태에 대한 연구: 라포(Rapport)를 중심으로
홍세인,정윤혁 강원대학교 사회과학연구원 2023 사회과학연구 Vol.62 No.3
죽음이 병원에서 관리되면서 일상생활에서 죽음을 접할 기회가 줄어듦에 따라, 사람들은 미디어를 통해 간접적으로 죽음을 경험하게 되었다. 최근에는 투병을 다루는 브이로그, 즉 투병 브이로그가 죽음에 대한 다양한 이야기를 전달하고 있다. 투병 브이로그는 한 사람의 투병 과정과 죽음에 이르는 과정을 세밀하게 보여주며, 질병과 죽음에 대한 이해를 높인다. 투병 브이로그에 대한 관심이 높아짐에 따라 생산자와 콘텐츠 효과에 관한 연구가 수행되었지만, 시청자 측면에서의 논의는 상대적으로 부족하다. 특히 이전 연구들은 주요 시청자를 투병자로 가정하였고, 이는 일반 사람들이 시청행태를 설명하지 못한다. 본 연구는 18명의 투병 브이로그 시청자에 대한 인터뷰를 통해 투병 브이로그의 시청행태를 탐색하였다. 브이로그의 업로드 방식이나 화면 구성 등의 인터페이스 요소뿐만 아니라, 투병자의 태도와 외모 변화도 제작자와 시청자 사이의 라포를 형성에 영향을 미쳤다. 이러한 라포는 시청자들이 지속해서 투병 브이로그를 시청하게 만들고, 댓글 작성, 좋아요 누르기 등의 시청자 참여를 촉진하였으며, 투병 브이로거와 시청자 간, 그리고 시청자들끼리의 연대감을 형성하게 하였다. 결과적으로, 강화된 라포는 시청자들이 브이로거의 죽음에 직면했을 때, 그 죽음을 ‘타인’의 죽음이 아니라 ‘나와 가까운 사람’의 것으로 인식하게 하였다.
확장된 사용자 유사도를 이용한 CF-기반 건강기능식품 추천 시스템
홍세인,정의주,김재경 한국지능정보시스템학회 2023 지능정보연구 Vol.29 No.3
With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user’s preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon’s “Health and Personal Care”. The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.