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나상태,김자희,정민호,안주언,Na, Sang-Tae,Kim, Ja-Hee,Jung, Min-Ho,Ahn, Joo-Eon 한국시뮬레이션학회 2016 한국시뮬레이션학회 논문지 Vol.25 No.3
시뮬레이션의 활용범위와 기법이 나날이 다양해지면서 시뮬레이션의 최신 연구 동향을 분석하고 이를 대학 교육과 연구에 적용하는 노력이 요구된다. 기존에는 트렌드 분석을 위해 문헌조사 또는 전문가 평가와 같은 정성적인 연구방법이 주로 사용되었으나 이런 방법들은 많은 시간과 비용이 소요될 뿐만 아니라 전문가의 주관적인 관점이 반영될 가능성이 있다. 본 연구에서는 객관적 분석을 위해 국내 학술 논문에 대하여 토픽분석을 포함한 정량적 분석을 실시하였다. 그 결과 국내에서는 시뮬레이션이 전기전자 분야에서 가장 활발하게 활용된다는 사실을 발견하였다. 또한 사회 과학에서는 교육 및 오락의 목적으로도 활용됨을 알 수 있었다. 이 연구 결과는 국내 시뮬레이션 연구와 한국 시뮬레이션 학회가 어떤 방향으로 발전할지를 예측하는 데 도움이 된다. 본 연구결과는 시뮬레이션 활용 연구 분야의 핵심 토픽을 도출하기 위하여 텍스트마이닝 기반의 트렌드분석에 대한 활용 가능성을 제시하고, 텍스트마이닝이 미래예측 키워드를 도출하는 유용한 방법임을 증명하였으며, 전문가들의 정성적인 자료를 보조하는 정량적인 자료분석 방법으로 유용할 것으로 기대된다. The recent diversification in terms of the scope and techniques used for simulations has highlighted the importance of analyzing state of the art trends and applying these for educational and study purposes. While qualitative methods such as literature research or experts' assessments have previously been used, such methods are in fact likely to reflect the subjective viewpoint of experts, and to involve too much time and money for the results obtained. For the purpose of an objective analysis, a quantitative analysis that included the examination of topics found in domestic academic journal articles was conducted in the present study. In this regard, simulation was found to be most actively used domestically in the electrical and electronic fields. In addition, simulation was also found to be employed for the purpose of education and entertainment in the social sciences. The results of this study are expected to help to facilitate the prediction of the direction of the development of not only the Korea Society for Simulation, but also domestic simulation studies. This study also raises the possibility of applying text mining to trend analysis, and proves that it can be a useful method for deriving future key topics and helping experts' decisions regarding quantitative data.
LSTM 알고리즘을 활용한 전기 수요고객의 온라인 질문에 대한 토픽분류
나상태(Sang-Tae Na),양광동(Gwang-Dong Yang),신재섭(Jae-Seop Shin) 대한전기학회 2019 전기학회논문지 Vol.68 No.11
In response to questions or complaints posted by customers on the company"s homepage, the response time is an important measure of customer satisfaction. However, the time it takes for a customer to receive an answer includes a time for the article to be selected by the person in charge of the reply, which limits the shortening. In this study, we developed a model in which a machine, not a person, reads the article, classifies the topic, and delivers it to each person in charge of the article. The article posted on the KEPCO homepage used in this study is a short sentence consisting of an average of 49 words. Due to the scarcity of multi-frequency words, it was found that there is a limit in securing a certain level of topic modeling accuracy in unsupervised machine learning like LDA. To overcome this, we labeled topics and let the machine conduct supervised learning. Although there are limitations in improving accuracy because there are articles containing more than two topics in one article, the classification accuracy is secured up to 84% by using LSTM and Baysian Optimization. The result of this study suggests that topic classification is possible for short-term customer questions in specific fields such as the electric power industry. In addition, it is expected that a model will be developed that can provide optimal reference answers for newly received questions when the topic-labeled questions and answers are fully accumulated.
나상태(Sang-Tae Na),안주언(Joo-Eon Ahn),김자희(Ja-Hee Kim) 대한전기학회 2017 전기학회논문지 Vol.66 No.12
As the power grid has been changed to a smart grid, existing power technologies are evolving into convergence technology through interdisciplinary research. According to the government policy to increase the proportion of renewable energy to 20% by 2030, the speed seems to be accelerating. This study analyzes the relationship between research technologies in order to grasp research trends of smart grid technology. To this end, we analyze the relationship between keywords extracted from topic modeling using social network analysis methodology. This is because, in the field where interdisciplinary research such as smart grid is active, each research topic is not independent, but research technologies emerging in one topic coexist in different topics, and linkage between research technologies can be important information. Therefore, this study can contribute to the analysis of research trend as it can be used as a package tool together with a topic modeling methodology.
나상태(Sang-Tae Na),안주언(Joo-Eon Ahn),정민호(Min-Ho Jung),김자희(Ja-Hee Kim) 대한전기학회 2017 전기학회논문지 Vol.66 No.4
The power grid has been changed to a smart grid system to satisfy the growing need for power grid complexity, demand, reliability, security, and efficiency with a combination of existing power and ICT technology. This study analyzes the research trends in smart grid technology in the period since the introduction of the smart grid system and compares it with industrial trends to grasp the progress and characteristics of Smart Grid technology and look for ways to innovate the technology. To do this, we analyze the research trends using dynamic topic modeling, which is capable of time-series research topic analysis. Next, we compare the results of research trends with industrial trends analyzed by Gartner’s experts to demonstrate that smart grid research is evolving to the level of industrialization. The results of this study are quantitative analysis through data mining, and it is expected that it will be used in many fields such as companies that want to participate in industry and government agencies that need to establish policies by showing more objective analysis results.