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생태교육의 실천적 지식 형성에 영향을 주는 초등교사의 경험 탐색
정세종 ( Se Jong Jeong ),김재근 ( Jae Geun Kim ) 한국생물교육학회 2022 생물교육 Vol.50 No.1
Studies that explored teachers' practical knowledge in environmental and ecological education studies have focused mainly on the practical aspect of class rather than the teachers' personal experiences. This study aimed to explore the experiences of elementary school teachers that influence the formation of practical knowledge in ecological education. Through in-depth interviews with six elementary school teachers who are passionate about ecological education, experiences that influence elementary school teachers' formation of practical knowledge in ecological education were explored. Interviews showed that elementary school teachers formed their practical knowledge from ‘experience from nature’, ‘experience as a subject of ecological education’, ‘experience through practice of ecological class’, and ‘experience through others and media’. Based on the orientation presented by Elbaz, experiences from nature were deeply related to experimental orientation, and experiences through practice of ecological class were deeply related to experimental and situational orientation. In addition, experiences as a subject of ecological education were deeply related to theoretical orientation, and experiences through others and media were deeply related to personal and social orientation.
약계자 영역에서의 토크분 전류제어 오차를 이용한 자속 보상
정세종(Se-Jong Jeong),김성기(Sung-Ki Kim),김승환(Seung-Hwan Kim),한기준(Ki-Joon Han),정명길(Myung-Gil Jung),이세현(Se-Hyun Lee) 전력전자학회 2008 전력전자학술대회 논문집 Vol.- No.-
농형 유도전동기의 센서리스 벡터제어시 기존의 약계자 알고리즘은 오토튜닝 오차에 의해 자속기준값이 크게 계산되어질 때, 역기전력과 전압제한값에 의해 최대전류를 출력할 수 없다. 본 논문에서는 약계자 영역에서 토크분 전류제어 오차를 이용하여 자속 기준값을 보상함으로써 전류부족에 의한 속도강하를 방지하는 알고리즘을 소개하였고, 시뮬레이션을 통해 그 타당성을 검증하였다.
온라인 파라메터 추정을 이용한 위상제어 정류기의 예측전류제어 특성 개선
정세종(Se-Jong Jeong),송승호(Seung-Ho Song) 전력전자학회 2002 전력전자학술대회 논문집 Vol.- No.-
위상제어 정류기 시스템에서 예측전류제어는 전류 응답속도가 매우 빠르고 오버슈트가 없는 것으로 알려져 있다. 하지만 전원과 부하의 전압 전류 방정식에 의존하는 예측전류제어는 부하 파라메터 값이 틀릴 경우 전류지령값과 피드백 사이에 정상 상태 오차를 보이게 된다. 본 논문에서는 디지털 순시치 샘플링과 최소자승법을 이용하여 온라인으로 부하의 파라메터를 추정하는 알고리즘을 제안하였고, 이를 이용하여 예측전류제어를 수행함으로써 빠르고 정밀한 전류제어응답을 보였다.
산업용 전기 차량의 저 분해능 마그네틱 엔코더를 사용한 속도 측정 방법
박기형(Gi-Hyoung Park),정세종(Se-Jong Jeong) 전력전자학회 2011 전력전자학술대회 논문집 Vol.2011 No.7
Recently, many industrial electric vehicles have been developed using various ac-motor drive technologies including field oriented vector control. Generally, a magnetic encoder is installed to have resistance to vibration and dust, and it is cost-effective. However, it is difficult to get an accurate rotor speed for high performance of vector control, because a resolution of the magnetic encoder is low and its phase accuracy is poor. In order to overcome this hardware problem, this study proposes a speed measurement algorithm using moving window for low-resolution magnetic encoder. This algorithm is experimentally tested and successfully applied to traction application of industrial electric vehicle.
우수연(Soo-Yeon Woo),정세종(Se-Jong Jeong),이경운(Kyungwoon Lee) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
This paper analyzes and evaluates recent machine learning (ML)-based estimation techniques for predicting computing resource usage. Even though recent techniques allow cloud administrators to manage cloud computing resources more efficiently, they require a significant amount of computing resources, such as GPUs, for training ML models. As such powerful hardware is limited in Edge computing environments that provide low-latency data processing for the Internet of Things (IoT) devices, the ML models can require a long training time while reducing the accuracy. Our evaluation results show that the training time increases by 28 times in the Edge computing environment compared to that of the server with a GPU. This result points out that recent ML-based computing resource estimation techniques require further optimization to reduce the training time when we utilize the models in the Edge computing environment.