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기상자료 보간을 통한 송전선로 갤러핑 사고 발생확률 예측
이정훈(Junghoon Lee),정호연(Hoyeon Jung),정형조(Hyung-Jo Jung) 한국소음진동공학회 2016 한국소음진동공학회 학술대회논문집 Vol.2016 No.4
Galloping phenomenon is one of the most serious vibration problems on transmission line. Even though transmission power lines are designed to prevent galloping, they can be extensively damaged due to aerodynamic instabilities, caused by icing and snow accretion. In this study, occurrence conditions of galloping phenomenon on the transmission line were analyzed by using a logistic regression model. In order to construct a regression model, climate information were obtained from Automatic Weather Station (AWS) provided by Korea Meteorological Administration (KMA). Because locations of AWS and transmission line are different, it was necessary to obtain exact weather condition on the transmission line by using estimation of the atmospheric condition. In order to estimate the weather condition, a Kriging process which was one of the interpolation techniques was used. Finally, the logistic regression model was constructed and showed good performance through cross validation.
이진환(JinHwan Lee),이정훈(JungHoon Lee),정호연(HoYeon Jung),정형조(Hyung-Jo Jung) 한국소음진동공학회 2015 한국소음진동공학회 학술대회논문집 Vol.2015 No.4
겨울철 송전선로에서 발생하는 Galloping은 여러 가지 기후 및 지형요인에 의해 발생한다. 그 중 기후요인과 갤러핑 발생확률과의 상관관계를 파악하기 위하여 AWS(자동기상관측장비) 데이터를 활용, 분석하였다. 본연구를 통하여 풍속, 풍향각, 풍향 표준편차 및 평균기온 등의 기후요 인들과 갤러핑 발생과의 연관성을 파악하고자 한다. Galloping is caused by various climatic and geological factors. AWS(Automated Weather Station) data is analyzed to find the correlation between climatic factors and probability of galloping occurrence. Through analyzing the relationship between climactic factors such as average and standard deviation of Wind speed, direction, and temperature and the occurence of galloping we want to know correlation between climate factor and galloping.
강민정(Minjeong Kang),이수연(Suyeon Lee),정보람(Boram Jung),정호연(Hoyeon Jeong) 한국디자인학회 2024 디자인학연구 Vol.37 No.4
Background : This paper aims to improve the user experience of search filters when browsing products on online grocery shopping platforms to propose guiding principles for optimizing search filter designs based on food culture trends, product variations, and user preferences. Methods : This study was conducted through a literature review, case studies, and quantitative/qualitative user surveys. Literature review was carried out to establish the direction of a filter design guide. Through the analysis of search filter cases in domestic and international food shopping malls, essential filters and specialized filters based on products and user types were identified. Based on them, a survey was conducted to extract key filters for each product. Contextual interviews were then conducted to derive insights and user types. Combining these findings, a food filter design guide and ideas were proposed. Results : The guide for key filters by food category in online food shopping malls is as follows. Firstly, for processed foods, prioritize providing a [Brand] filter. Secondly, for refrigerated/frozen foods, prioritize offering a [Taste/Type] filter. Lastly, for fresh foods, prioritize providing an [Origin (Production Place)] filter, while agricultural and livestock products can share filters. Additionally, users’ behavior types in utilizing food product search tools are categorized into three: ‘Shopping Enthusiast’ prioritizes price and delivery date, ‘Stability Seeker’ considers quantity/price and expiration date, and ‘Homemaker’ focuses on taste preferences and reviews. Conclusions : In this study, we identified the priorities of major food category search filters in online food shopping malls and proposed a food product search filter design guide that reflects user search behaviors. The research findings are expected to serve as crucial reference material for future design of food search AI algorithms.