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메이커 교육을 활용한 에너지 교육이 초등학생의 과학적 태도와 에너지 소양에 미치는 영향
정경재 ( Jung¸ Kyoungjae ),배진호 ( Bae¸ Jinho ) 한국초등과학교육학회 2021 초등과학교육 Vol.40 No.4
본 연구에서는 메이커 교육을 활용한 에너지 교육이 초등학생들의 과학적 태도와 에너지 소양에 어떤 영향을 미치는지 알아보고자 하였다. 연구 대상은 B광역시 초등학교 6학년 학생 남학생 13명 여학생 10명 총 23명이며 메이커 교육을 활용하여 에너지 교육 수업을 실시하여 수업 전과 후의 과학적 태도와 에너지 소양의 변화를 알아보았다. 본 연구의 결과는 다음과 같았다. 첫째, 메이커 교육을 활용한 에너지 교육은 초등학생의 과학적 태도에 긍정적인 영향을 미쳤다. 과학적 태도의 하위 영역 중 호기심, 개방성, 비판성, 협동성, 자진성, 끈기성, 창의성에서 유의한 향상이 있었다. 둘째, 메이커 교육을 활용한 에너지 교육은 초등학생의 에너지 소양에 긍정적인 영향을 미쳤다. 에너지 소양의 모든 하위 영역에서 유의한 향상이 있었다. This study investigated the effect of maker education on the scientific attitude and energy literacy of elementary school students. The subjects of this study were 23 6th grade students, 13 male students and 10 female students, in a B Metropolitan city elementary school. Students’ scientific attitude and energy literacy was observed before and after the introduction of maker education into energy education class. The results of this study were as follows. First, energy education using maker education had a positive effect on elementary school students’ scientific attitude; there was a significant improvement in curiosity, openness, critical thinking, cooperation, spontaneity, persistence, and creativity. Second, maker education had a positive effect on the energy literacy of elementary school students. There was a significant improvement in all components of energy literacy.
Application of Support Vector Machines to the Prediction of KOSPI
Kyoungjae Kim 한국지능정보시스템학회 2003 한국지능정보시스템학회 학술대회논문집 Vol.- No.-
Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (DVMs) are regarded as promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index(KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back=propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.
김경재(Kyoungjae Kim),박호연(Hoyeon Park) 동국대학교 경영연구원 2016 경영과 사례연구 Vol.39 No.2
회사채 신용등급은 기업의 지본비용 및 기업가치를 결정하는 중요한 재무의사결정요소로서 기업 내부 관계자뿐만 아니라 외부투자자, 기관, 종업원 등 많은 이해관계자의 관심을 받는 정보이다. 정교한 회사채 신용등급평가모형은 재무와 회계분야에서의 전통적인 연구주제였으며, 선행연구들을 통해 많은 정성적인 평가모형과 계량적인 평가모형이 제안되어 왔다. 특히, 신용등급평가모형은 전통적으로 난해한 다중분류문제(multi-class classification problem)로 알려져 있으며, 이를 위해 선형분류모형은 물론, 의사결정 나무, 인공신경망, 서포트벡터머신 등의 비선형 데이터마이닝 모형도 활용되어 왔다. 본 연구에서는 비교적 최근에 제안되어 경영학 분야에서 적용이 많이 되지 않은 랜덤포레스트 기법을 이용하여 회사채 신용등급평가모형과 같은 경영학 분야에서의 다중분류문제에 활용기능성을 실험적으로 검토해 보고자 한다. 실험 결과, 랜덤포레스트는 다른 데이터마이닝 기법에 비해 탁월하고 안정적인 다중분류 성능을 보이는 것으로 나타났다. Corporate bond rating is one of the most important factors of financial decision making for determination of corporate financial costs and corporate value. Thus, it is subject of interest of many stakeholders, including corporate insiders as well as outside investors. institutions, and employees. Sophisticated corporate bond rating model was the traditional research topics in the field of finance and accounting, so many previous studies proposed various qualitative and quantitative evaluation models. In particular, the corporate bond rating has traditionally been accepted as difficult multi-class classification problems. many prior studies incorporated nonlinear data mining models such as decision trees, artificial neural networks. support vector machines as well as linear classification model to solve the problem. In this study, we exploratory test the usability of random forests in business area such as corporate bond rating because they are relatively recent proposed in the area. Experimental results showed that random forests appeared to exhibit excellent and stable multi-class classification performance compared to other data mining techniques including logistic regression, support vector machines and simple decision trees.
Inference for differential equation models using relaxation via dynamical systems
Lee, Kyoungjae,Lee, Jaeyong,Dass, Sarat C. Elsevier 2018 Computational statistics & data analysis Vol.127 No.-
<P><B>Abstract</B></P> <P>Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomena which are dynamical in nature, and which are abundant in areas such as biology, climatology and genetics. The estimation of parameters of ODE based models is essential for understanding its dynamics, but the lack of an analytical solution of the ODE makes estimating its parameter challenging. The aim of this paper is to propose a general and fast framework of statistical inference for ODE based models by relaxation of the underlying ODE system. Relaxation is achieved by a properly chosen numerical procedure, such as the Runge–Kutta, and by introducing additive Gaussian noises with small variances. Consequently, filtering methods can be applied to obtain the posterior distribution of the parameters in the Bayesian framework. The main advantage of the proposed method is computational speed. In a simulation study, the proposed method was at least 35 times faster than the other Bayesian methods investigated. Theoretical results which guarantee the convergence of the posterior of the approximated dynamical system to the posterior of true model are presented. Explicit expressions are given that relate the order and the mesh size of the Runge–Kutta procedure to the rate of convergence of the approximated posterior as a function of sample size.</P>
이경재(Kyoungjae Lee),한표영(Pyoyoung Han),이현석(Hyunseok Lee),배진웅(Jinwoong Bae),김응수(Eungsoo Kim),남철(Chul Nam) 大韓電子工學會 2011 電子工學會論文誌-SD (Semiconductor and devices) Vol.48 No.5
본 논문은 24 채널 정전 용량형 터치 검출 ASIC에 대한 것이다. 제안된 회로는 아날로그 회로부와 디지털 회로부로 구성되어 있다. 아날로그 회로부는 사용자의 접촉을 전기적인 신호로 변환시키며 디지털 회로부는 전기적인 신호의 변화를 디지털 데이터로 변환시키는 역할을 담당한다. 디지털 회로는 I2C가 내장되어 시스템 동작 계수들을 호스트 프로세서에서 변경해 줄 수 있도록 설계되었다. 따라서 온도 변화 등 외부환경 변화에도 안정적으로 동작할 수 있다. 본 ASIC은 0.18㎛ CMOS 공정으로 구현되었으며 그 크기는 약 3 ㎟ 이고 소비전력은 5.3 ㎽이다. 설계에는 Cadence사와 Synopsys사의 상용 개발환경이 사용되었다. This paper presents a 24 channel capacitive touch sensing ASIC. This ASIC consists of analog circuit part and digital circuit part. Analog circuits convert user screen touch into electrical signal and digital circuits represent this signal change as digital data. Digital circuit also has an I2C interface for operation parameter reconfiguration from host machine. This interface guarantees the stable operation of the ASIC even against wide operation condition change. This chip is implemented with 0.18 ㎛ CMOS process. Its area is about 3 ㎟ and power consumption is 5.3㎽. A number of EDA tools from Cadence and Synopsys are used for chip design.