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한국남자 프로농구 가드의 역할이 승패 결정 요인에 미치는 영향 (2013-2014 한국프로농구 정규리그)
이원민,김효진,윤정민,하현빈,전혜자 순천향대학교 기초과학연구소 2014 순천향자연과학연구 논문집 Vol.20 No.2
This study analyzed factors of victory or defeat which is affected by guard with using game records of Korean Basketball League. The subject of study is 61 of basketball players who played as guard of the first string and who are affiliated each 10 teams of Korean Basketball League in 2013-2014 season. The study gets a result about what guard affects to factors of victory or defeat from inferring factors with using data which were recorded by recorders of KBL and dividing into a winning group and a losing group through 7 of offensive factors, including 2P(2 Points), FT(Free Throw), 3P(3 points), RB(Rebound), AS(Assist), BS(Block shoot), ST(Steal), which are put by an order that power of explanation is high.
이원민(Won-Min Lee),온병원(Byung-Won On) 한국정보기술학회 2021 Proceedings of KIIT Conference Vol.2021 No.11
인공지능 시장의 성장과 함께 챗봇에 관한 연구가 활발히 진행되고 있다. 최근 챗봇은 사용자에게 필요한 서비스를 제공해주는 기술을 넘어 사람의 감정을 비슷하게 표현하며 적절한 반응을 보이는 수준으로 기술이 개발되고 있다. 하지만 챗봇이 사람과 똑같이 감정을 이해하고 표현하는 것에는 한계가 존재한다. 본 논문에서는 BERT+GPT 파이프라인 모델을 통해 자동으로 생성된 대용량의 감정 학습데이터를 적용하여 인공지능 챗봇시스템을 구현하였고, 프로토타입 시스템을 통해 실제 챗봇이 사용자의 말에 대한 감정을 파악하고 이에 적절한 반응을 보임을 확인했다. With the growth of the artificial intelligence market, research on chatbots is being actively conducted. In recent years, chatbot technology is being developed to a level where it can respond appropriately by expressing human emotions in a similar way, beyond the technology that provides necessary services to users. However, there is a limit to the ability of chatbots to understand and express emotions just like humans. In this paper, an artificial intelligence chatbot system was implemented by applying a large amount of emotion learning data automatically generated through the BERT+GPT pipeline model. It was confirmed that there was a reaction.
BERT 모델의 감성 분류 정확도 향상을 위한 감성 단어 마스킹 방안
이원민(Won-Min Lee),온병원(Byung-Won On) 한국정보기술학회 2021 Proceedings of KIIT Conference Vol.2021 No.6
감성 분석(Sentiment Analysis)이란 텍스트의 정보를 추출하고 감성에 따라 분류하는 과정으로 최근에는 딥러닝을 이용한 연구가 활발히 이루어지고 있다. 그중 BERT는 구글에서 공개한 전이 학습을 통해 좋은 성능을 발휘하는 딥러닝 모델로, 감성 분석에서 많이 사용되는 모델이다. 해당 모델은 마스크 언어 모델 태스크를 수행할 때 랜덤 마스킹을 통한 학습 데이터를 생성한다. 이때 감성 단어로 마스킹을 진행하면 감성에 집중된 학습 데이터를 생성할 수 있고, 감성 분석의 정확도를 높일 수 있다. 따라서 감성 단어로 마스킹을 진행해서 학습 데이터를 생성하고 마스크 언어 모델 태스크를 수행하는 감성 분류에 최적화된 모델을 제안한다. 제안 방안으로 감성 분석을 진행 한 결과, 기존의 방식보다 향상된 성능을 보였다. Sentiment Analysis is a process of extracting text information and classifying it according to emotion. Recently, research using deep learning has been actively conducted. Among them, BERT is a deep learning model released by Google that shows good performance through transfer learning, and is a model that is widely used in sentiment analysis. The model generates training data through random masking when performing the mask language model task. At this time, if masking is performed with emotional words, it is possible to generate learning data focused on the emotions, and to increase the accuracy of the emotion analysis. Therefore, we propose a model optimized for emotional classification that generates training data by masking with emotional words and performs the mask language model task. As a result of conducting emotional analysis as a proposed method, it showed improved performance compared to existing methods.
감성 및 감정 단어 마스킹 기반 BERT와 GPT 파이프라인 방식을 통한 감정 문장 생성
이원민(Won-Min Lee),온병원(Byung-Won On) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.9
Recently, due to advances in Artificial Intelligence(AI), there have been active studies on AI Chatbot, which has reached a level where it can similarly express human emotions. If AI Chatbot can accurately understand human emotions and respond accordingly, natural conversations between chatbots and humans are possible. Large-capacity, high-quality training data for emotion analysis is needed to train a deep learning model that automatically generates sentences. In this study, we proposes a new BERT+GPT pipeline method in which an emotion word masking-based BERT is used to automatically generate large-capacity, high-quality training data as the input of GPT that is used to generate response sentences containing human emotions. Our experimental results show that the proposed method improved up to 12% accuracy, compared to the existing BERT model, showing that most response sentences generated by GPT were better in both emotion consistency and natural meaning.
손수정,이원민,이우진,권순길,정진영,Son, Soojung,Lee, Wonmin,Lee, Woojin,Kwon, Soonkil,Jung, Jinyoung 한국군사과학기술학회 2022 한국군사과학기술학회지 Vol.25 No.3
The current development tend of the gun propellants that they should have low sensitivity and high energy. We studied a nitrocellulose based propellant composition that replaced sensitive NG with RDX and DEGDN which high energy and low sensitivity. The important factors in the design of the gun propellant were impetus and flame temperature. NC-based propellant containing RDX showed similar impetus but low flame temperature compared to KM30A1, a triple-based propellant. The developed propellant composition didn't show any abnormal combustion reaction and the characteristics of ballistic resistance were also confirmed.