• 생물정신의학
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• 제27권1호
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• pp.9-17
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• 2020

Objectives The purpose of this study is to investigate the interest level, perception and attitude of suicide high-risk group toward the media's suicide report, and also to provide the fundamental data in order to strengthen both the standard and the recommendation of media coverage in Korea. Methods We analyzed the data from the 2013 The Korea National Suicide Survey. The study included 1500 participants aged between 19 and 75 years. The participants were selected through the regional multi-layer stratification method using the sampling frame of the 2010 Census. One-on-one face-to-face interviews investigated the interest level, opinions, and attitudes toward suicide reports and collected data on past suicide history and demographic data. The collected data were evaluated using multiple logistic regression analysis. A probability level of p < 0.05 was considered statistically significant. Results The suicide high risk groups with histories of suicidal ideations or attempts showed more interest in suicidal reports [suicidal thought odds ratio (OR) = 1.81, 95% confidence interval (CI) = 1.40-2.34 ; suicidal attempt OR = 3.21, 95% Cl = 1.52-6.78]. However, there was no difference among the groups in thought that suicide reports incite suicide (suicidal thought OR = 1.26, 95% Cl = 0.92-1.73 ; suicidal attempt OR = 0.96, 95% Cl = 0.44-2.09). The suicide high risk groups showed a positive attitude toward suicide reports, but it was not statistically significant (suicidal thought OR = 1.10, 95% Cl = 0.41-2.92 ; suicidal attempt OR = 1.10, 95% Cl = 0.76-1.59). Conclusions We suggest the need of careful attitude of media suicidal reports not to bring about copycat suicides. and also we offer suicide prevention campaign based on media leverage.

• 디지털산업정보학회논문지
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• 제14권4호
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• 지능정보연구
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• 제20권2호
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• pp.73-92
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• 2014

기존 협업 필터링 기법은 사용자들의 아이템에 대한 선호도를 기반으로 유사 아이템 집합 또는 유사 사용자 집합을 구성하고, 이를 이용해 예측된 사용자의 특정 아이템에 대한 선호도를 기반으로 추천을 수행한다. 이로 인해, 사용자 선호도 정보가 부족하게 되면, 유사 아이템 사용자 집합의 신뢰도가 낮아지고, 추천 서비스의 신뢰도 또한 따라서 낮아진다. 또한, 서비스의 규모가 커질수록, 유사 아이템, 사용자 집합의 생성에 걸리는 시간은 기하급수적으로 증가하고 추천서비스의 응답시간 또한 그에 따라 증가하게 된다. 위와 같은 문제점을 해결하기 위해 본 논문에서는 적응형 군집화 기법을 제안하고 이를 적용한 협업 필터링 기법을 제안하고 있다. 이 기법은 크게 네 가지 방법으로 이루어진다. 첫째, 사용자와 아이템의 특성 벡터를 기반으로 사용자와 아이템 각각을 군집화 하여, 기존 협업 필터링 기법에서 유사 아이템, 사용자 집합을 생성하는데 소요되는 시간을 절약하며, 사용자 선호도 정보만을 이용한 부분 집합 생성보다 추천의 신뢰도를 높이고, 초기 평가 문제와 초기 이용자 문제를 일부 해소한다. 둘째, 미리 구성된 사용자와 아이템의 군집을 기반으로 군집간의 선호도를 이용해 추천을 수행한다. 사용자가 속한 군집의 선호도가 높은 순서대로 아이템 군집을 조회하여 사용자에게 제공할 아이템 목록을 구성하여, 추천 시스템의 부하 대부분을 모델 생성 단계에서 부담하고 실제 수행 시 부하를 최소화한다. 셋째, 누락된 사용자 선호도 정보를 사용자와 아이템 군집을 이용하여 예측함으로써 협업 필터링 추천 기법의 사용자 선호도 정보 희박성으로 인한 문제를 해소한다. 넷째, 사용자와 아이템의 특성 벡터를 사용자의 피드백에 따라 학습시켜 아이템과 사용자의 정성적 특성 정량화의 어려움을 해결한다. 본 연구의 검증은 기존에 제안되었던 하이브리드 필터링 기법들과의 성능 비교를 통해 이루어졌으며, 평가 방법으로는 평균 절대 오차와 응답 시간을 이용하였다.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• 문화기술의 융합
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• 제9권5호
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• pp.591-596
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• 2023

본 연구는 10대 1인 미디어 제작자를 중점으로 하여 쉽고 편리하게 영상을 제작할 수 있는 프로그램을 개발하였다. 사용자 조사를 통해 나타난 10대 제작자들의 요구사항과 문제점을 파악하여 사용자가 원하는 캐릭터 커스터마이징 기능과 GPT를 활용한 감정 및 행동 추천 시스템을 구현하였다. 렌더링 과정에서는 OpenCV와 FFmpeg를 사용하여 오디오와 영상을 결합하여 최종 영상을 생성하였다. 영상 제작에 전문성이 없는 10대 사용자들은 간단한 인터페이스를 통해 웹드라마 캐릭터를 커스터마이징하고, GPT의 도움을 받아 감정과 행동을 추천받을 수 있다. 본 연구의 프로그램은 편집 및 연출에 전문성이 없는 10대 사용자들을 대상으로 양질의 영상 제작을 돕고, 영상 제작에 대한 진입장벽을 낮춰주며, 1인 미디어 산업의 발전에 기여할 수 있는 도구가 될 것으로 기대된다. 향후 모바일이나 세로해상도 버전의 영상을 고려한 영상 제작 환경을 제공할 수 있도록 할 예정이다.

• 산업융합연구
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• 제21권12호
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• pp.25-35
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• 2023

본 연구는 고등학생의 성 미디어 리터러시 인식과 성 태도가 성인지 감수성에 미치는 영향을 파악하기 위한 서술적 조사연구이다. 본 연구대상자는 D시에 소재하는 고등학생 120명으로, 연구에 자발적으로 동의 후 참여하였다. 자료는 2022년 11월 28일부터 12월 15일까지 수집되었으며, SPSS 22.0 프로그램을 사용하여 descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient 및 hierarchical regression analysis 방법으로 분석되었다. 연구결과, 성인지 감수성에 영향을 미치는 요인은 성 태도(β=-.317, p<.001), 아버지의 교육수준(β=.305, p<.001), 성별(β=.262, p=.001), 성 지식 출처(β=-.252, p=.001), 성 미디어 리터러시 인식(β=-.143, p=.045) 순으로 영향을 미쳤으며, 설명력은 41.6%로 나타났다(F=13.395, p<.001). 본 연구는 다양성이 강조되는 미래 사회에서 필수적인 역량으로 간주되는 성인지 감수성을 높이기 위해 학문적·실무적·정책적 제언을 하였다는 점에서 의의를 갖는다.

• Nuclear Engineering and Technology
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• 제50권5호
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• pp.639-647
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• 2018

In this study, the effects of selecting water/silver nanofluid as both a coolant and a reactivity controller during the first operating cycle of a light water nuclear reactor are investigated. To achieve this, coupled neutronic-thermo-hydraulic analysis is employed to simulate the reactor core. A detailed VVER1000/446 reactor core is modeled in monte carlo code (MCNP), and the model is verified using the porous media approach. Results show that the maximum required level of silver nanoparticles is 1.3 Vol.% at the beginning of the cycle; this value drops to zero at the end of cycle. Due to substitution of water/boric acid with water/Ag nanofluid, reactor operation time at maximum power extends to 357.3 days, and the energy generation increases by about 27.3%. The higher negative coolant temperature coefficient of reactivity in the presence of nanofluid in comparison with the water/boric acid indicates that the reactor is inherently safer. Considering the safety margins in the presence of the nanofluid, minimum departure from nucleate boiling ratio is calculated to be 2.16 (recommendation is 1.75).

• 한국컴퓨터정보학회논문지
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• 제20권1호
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• pp.127-135
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• 2015

본 논문에서는 Space Syntax 이론을 적용한 여성의 안전한 여가활동 경로를 추천하는 애플리케이션을 제안하고 구축한다. 이 애플리케이션은 일반적인 네비게이션이 우선순위로 가지는 최단시간, 최단거리 탐색과는 다르게, 안전을 최우선으로 한 경로를 탐색하게 된다. 접근성, 지역 간 상호관계 등을 바탕으로 추천경로를 생성하고 여성의 여가활동에 있어 다양한 위험요소를 염두에 두게 된다. 결과적으로 Space Syntax 알고리즘을 통해 여성 사용자들의 주관적 선택사항이 적용된 자기맞춤형 경로를 선택 가능하게 한다.

• 지능정보연구
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• 제21권3호
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• pp.101-116
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• 2015

많은 기관들이 데이터에 기반을 둔 의사결정을 수행해 왔으며, 특히 수치자료를 비롯한 정형 데이터가 이러한 목적으로 널리 활용되어 왔다. 하지만 최근에는 스마트기기와 소셜미디어의 발달로 인해 다양한 형태를 가진 방대한 양의 정보가 생성, 공유, 저장되면서, 전통적인 정형 데이터 기반 의사결정으로부터 비정형 빅데이터 기반 의사결정으로 관심의 전환이 이루어지고 있다. 데이터 기반 의사결정의 대표적 분야인 추천시스템 분야에서도 성능 향상을 위해 비정형 데이터를 활용해야 한다는 필요성이 최근 꾸준히 제기되고 있다. 특히 사용자의 성향이나 선호도는 고객의 니즈와 직결되기 때문에, 비정형 데이터 분석을 통해 사용자의 성향을 파악하고 이를 통해 상품 추천 및 구매 예측의 정확도를 향상시키기 위한 노력이 매우 시급하게 이루어질 필요가 있다. 따라서 본 연구에서는 사용자의 성향을 측정하여 재구매 예측 정확도, 특히 카테고리별 재구매 예측 정확도를 높임으로써, 궁극적으로 추천시스템의 성능을 향상시킬 수 있는 방안을 제시한다. 구체적으로는 사용자의 일상적인 인터넷 사용 기록을 분석하여 고객이 조회하는 뉴스 기사의 이슈를 식별하고 다양한 이슈에 대한 고객의 관심을 계량화한 후, 이를 활용하여 고객의 카테고리별 재구매 여부를 예측하는 모델을 제안하고자 한다. 실제 웹 트랜잭션으로부터 도출된 인터넷 뉴스 조회 기록 및 쇼핑몰 구매 기록을 대상으로 실험을 수행한 결과, 고객의 과거 구매이력만을 활용한 카테고리 재구매 예측 모형에 비해 본 연구에서 제안한 모형, 즉 고객의 과거 구매이력과 관심 이슈를 모두 활용한 예측 모형의 정확도가 다소 우수한 것으로 나타났다.

• 한국실내디자인학회논문집
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• 제26권1호
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• pp.33-42
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• 2017

Interior design especially the design developments for commercial spaces should ensure the economy of spaces. This is the marketing for space. It is necessary to understand the design elements of commercial space for the efficient using of digital media of space marketing. In this research decided to design application schemes for shopping mall by searching fashion shopping behaviors of users with persona-base scenario approach. Collected the information of users behavior base on case analysis of advance research to make personas. And develop scenario base personas for knowing the needs of users. And then made 4 schemes as the solution. Scheme 1. Customers can get the information and communication with the staff through with media screen in one room of shopping mall. The data of customers can be recorded for providing information or product recommendation personality. Forecast to preferred by group trip or customers that don't like walking around in shopping mall. Scheme 2. Install a media screen at entrance of a store in shopping mall and show special effect to raise customers' attention who walk through from the front of the store. Through with this customers will stay for long time and raise curiosity to get in the store. And the media screen also provide information of store and products. Scheme 3. Customers can get the information of products with using smart phone to scan QR cord in labels of products and record. Customers can finish whole shopping behaviors without the help of store staff. And can make buying decision even have left shopping mall as a online mode. Scheme 4. Store managers can record the products and the environment of store with 360 camera and update to website. Then customers can browse the virtual space with VR glasses. That make customers can have the real shopping experience without being in the store. In this study, have presented schemes of digital media in commercial space. But there are various of commercial space. In this study was presented schemes for shopping mall by searching fashion shopping behaviors of users. And look forward the researches about specific space setting and other type of commercial space for space development.