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최일영(Choi, IlYoung) 동양한문학회(구 부산한문학회) 2019 동양한문학연구 Vol.52 No.-
본 논문은 恕菴 申靖夏(1681~1716)가 蘇軾(1037~1101)을 얼마나 어떻게 애호하였는지 확인하고, 그러한 소식 애호 및 동일화 양상이 신정하의 산문에 어떻게 스며들었으며 또 어떻게 반영되었는지 究明하는 것을 목적으로 하였다. 신정하는 스스로 소식을 매우 좋아한다고 밝혔고, 주변 인물들 역시 신정하의 소식애호를 잘 알고 있었다. 신정하의 문집에 남아 있는 詩文에 대한 비평은 소식을 대상으로 한 것이 제일 많았고 비평의 내용 또한 호평일색이었다. 신정하의 다른 작 품에서는 소식과 자신을 동일시하는 경향을 엿볼 수 있었으며, 신정하의 주변 인물들도 신정하의 글을 보고는 소식을 떠올리기도 하였다. 본론에서는 신정하가 소식을 닮고 싶어 하였고 또 비슷한 글을 지었다는 것에 기반하여, 신정하의 산문을 대상으로 이른바 ‘소식스러움’을 확인하고자 하였다. 신정하는 소식의 단편 산문에서 보이는 서정적인 느낌과 골계적인 수사를 소식의 特長으 로 주목하였고, 이 특장을 본받아서 글을 써야 한다고 주장하였다. 신정하의 언급을 토대로 尺牘類와 題跋類 등의 단편 산문에서 ‘詩的 抒情性’과 ‘滑稽美’를 확인할 수 있었는데, 이러한 점이 바로 주변 인물들로 하여금 신정하의 글을 읽고 소식을 떠 올리게 하는 요인이었을 것이다. 물론 신정하 이외의 다른 문인들의 산문에도 서정성 및 골계미 등을 찾을 수 있다. 하지만 신정하는 소식을 애호하다 못해 동일화 경향을 보였고, 소식 산문의 여러 장점 중에서도 서정성 및 골계미 등을 주목한 인물이다. 그렇기 때문에 신정 하 산문에서 보이는 이러한 ‘소식스러움’으로 그 문학적 영향 관계를 이해할 수 있을 것이다. This paper aims to confirm how much Shin Jeong-ha liked Sūshì and to study the literary influence of each other. Shin Jeong-ha himself said he liked Sūshì very much, and people around him knew that Shin liked Sūshì very much. Shin Jeong-ha s literary criticism was the most about Sūshì, and the content of the criticism was all good. Shin Jeong-ha s literature shows a tendency to regard himself as the same as Sūshì, and Shin Jung-ha s acquaintances felt that his literature was similar to that of Sūshì. Based on the fact that Shin Jeong-ha wanted to resemble Sūshì and wrote similar prose, I wanted to confirm the Sūshì-esque of Shin Jung-ha s prose. Shin Jeong-ha noted the lyrical feelings and humorous expressions in Sūshì s short prose as his strong points, and insisted that he should learn from them and write. Based on Shin Jung-ha s comments, I was able to find Lyricism and Humor in short prose such as Shin Jung-ha s letters and some sort of memo. This may have been the reason why people read Shin Jung-ha s writing and felt it was similar to Sūshì s. Of course, you can find Lyricism and Humor in the prose of other writers besides Shin Jeong-ha. But Shin Jeong-ha liked Sūshì so much that he tended to equate him with Sūshì, and he was a man who rated Lyricism and Humor high than Sūshì s other merits. Therefore, the Sūshì-esque seen in Shin Jeong-ha’s prose will help us understand the literary influence of each other.
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최일영(ILYOUNG CHOI),김혜경(HYEA-KYEONG KIM) 한국수소및신에너지학회 2022 한국수소 및 신에너지학회논문집 Vol.33 No.2
With the advent of the post-2020 climate regime, the hydrogen industry is growing rapidly around the world. In order to build the hydrogen economy, it is important to identify social issues related to hydrogen and prepare countermeasures for them. Accordingly, this study conducted a semantic network analysis on hydrogen news from NAVER. As a result of the analysis, the number of hydrogen news in 2020 increased by 4.5 times compared to 2016, and as of 2018, the hydrogen issue has shifted from an environmental aspect to an economic aspect. In addition, although the initial government-led hydrogen industry is expanding to the mobility field such as privately-led fuel cell electric vehicles and hydrogen fuel, terms showing concerns about the safety such as explosions are constantly being exposed. Thus, it is necessary not only to expand the hydrogen ecosystem through the participation of private companies, but also to promote hydrogen safety.
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E-커머스 사용자의 평점과 리뷰 유용성이 상품 추천 시스템의 성능 향상에 미치는 영향 분석
LIU FAN,이병현(Byunghyun Lee),최일영(Ilyoung Choi),정재호(Jaeho Jeong),김재경(Jaekyeong Kim) 한국지능정보시스템학회 2022 지능정보연구 Vol.28 No.1
Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the users past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.
KCI
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