http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
한편 북한 언론은 지난 20년간 대한민국 대통령과 정부를 어떻게 보았는가? : 감성어, 의미연결망, 토픽모델링을 활용한 북한 언론 보도 빅데이터 분석
박종민(Jongmin Park),조원정(Wonjeong Jo),최종환(Jonghwan Choi) 한국언론학회 2023 한국언론학보 Vol.67 No.1
This study examined how the North Korean media covered the president-related news during each of the South Koreas 20-year presidential terms, from 2003 to the present. The media analyzed were two North Korean media, 〈Chosun Central News〉and 〈Joseon Today〉. To examine the differences between the presidential and governmental periods, sentiment analysis and QAP correlation analysis were used. In addition, semantic network analysis and topic modeling analysis were performed to analyze each governments reporting characteristics. First, sentiment analysis showed that the positivity was ranked in the order of Roh Moo-hyun, Moon Jae-in, Lee Myung-bak, and Park Geun-hye. There was a significant positive correlation between the two conservative presidents, Lee Myung-bak and Park Geun-hye. However, Roh Moo-hyun and Moon Jae-in were not significantly correlated. The positivity of the Roh administration was exceptionally high. This result was identified as the cause of a surge in negative articles about the Moon Jae-in government in the North Korean media since 2019. Second, the semantic network analysis of the context of the presidents comments showed that Roh Moo-hyun and Moon Jae-in used the title of President. However, Lee Myung-bak and Park Geun-hye used a lot of negative modifiers such as rebel, weightlifting, and gang rather than their titles. During the Roh Moo-hyun administration, the North Korean media sentiment index gradually increased and was low during the entire Lee Myung-bak administration. During the Park Geun-hye administration, it was initially positive, but after a sharp decline in 2013, the emotional index continued to be low. The Moon Jae-in administration had an upward trend until 2018, but it has been on a downward trend since 2018 and has been on a sharp decline since 2019. The results of an analysis of context word clusters and topic modeling for each president were derived, and each word cluster and topic were analyzed. Finally, in the discussion part of this study, various implications of the analysis results were also discussed.
Stable and high emission current from carbon nanotube paste with spin on glass
Park, Jae-Hong,Moon, Jin-San,Han, Jae-Hee,Berdinsky, Alexander S.,Yoo, Ji-Beom,Park, Chong-Yun,Nam, Joong-Woo,Park, Jonghwan,Lee, Chun Gyoo,Choe, Deok Hyeon American Vacuum Society 2005 JOURNAL OF VACUUM SCIENCE & TECHNOLOGY B - Vol.23 No.2
Screen printed carbon nanotube field emitter array for lighting source application
Park, Jae-Hong,Son, Gil-Hwan,Moon, Jin-San,Han, Jae-Hee,Berdinsky, Alexander S.,Kuvshinov, D. G.,Yoo, Ji-Beom,Park, Chong-Yun,Nam, Joong-Woo,Park, Jonghwan,Lee, Chun Gyoo,Choe, Deok Hyeon American Vacuum Society 2005 JOURNAL OF VACUUM SCIENCE & TECHNOLOGY B - Vol.23 No.2
Resource Metric Refining Module for AIOps Learning Data in Kubernetes Microservice
Jonghwan Park,손재기,Dongmin Kim 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.6
In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.