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지능형 교통시스템에 적합한 위성항법 기반의 정밀측위 구조 설계
이병현(Byung-Hyun Lee),임성혁(Sung-Hyuck Im),허문범(Moon-Beom Heo),지규인(Gyu-In Jee) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.11
In this paper, a structure of precise positioning based on satellite navigation system is proposed. The proposed system is consisted with three parts, range domain filter, navigation filter and position domain filter. The range domain filter generates carrier phase-smoothed-Doppler and Doppler-smoothed-code measurements. And the navigation filter calculates position and velocity using double-differenced code/carrier phase/Doppler measurements. Finally, position domain filter smooth position error, and it means enhancement of positioning performance. The proposed positioning method is evaluated by trajectory analysis using precise map date. As a result, the position error occurred by multipath or cycle slip was reduced and the calculated trajectory was in true lane.
EMTP-MODELS를 이용한 Multi-Agent System 기반의 자동 재폐로 계전 알고리즘 구현
李秉炫(Byung-Hyun Lee),成魯珪(No-Kyu Sung),呂相敏(Sang-Min Yeo),李瑜珍(You-Jin Lee),金喆煥(Chul-Hwan Kim) 대한전기학회 2008 전기학회논문지 Vol.57 No.1
This paper presents auto-reclosing algorithms with reference to power system stability based on MAS(Multi-Agent System). And this paper shows auto-reclosing algorithms considering power system stability. It includes the variable dead time, optimal reclosing, sequential reclosing, emergency extended equal-area criterion (EEEAC) algorithm, and modified EEEAC algorithm. This paper divides Auto-reclosing algorithms into respectively agents according to their tasks. A separated agent is merely a software entity that is situated in some environment and is able to autonomously react to changes in the environment. And all the simulations in this parer were tested by EMTP MODELS.
Exponential Smoothing기법을 이용한 전기자동차 전력 수요량 예측에 관한 연구
이병현(Byung-Hyun Lee),정세진(Se-Jin Jung),김병식(Byung-Sik Kim) 한국방재안전학회 2021 한국방재안전학회 논문집 Vol.14 No.2
본 논문은 전기자동차 충전시설 확충계획에 중요한 요소인 전기자동차 전력 수요량 예측정보를 생산하기 위하여 Exponential Smoothing를 이용하여 전력 수요량 예측 모형을 제안하였다. 모형의 입력자료 구축을 위하여 종속변수로 월별 시군구 전력수요량을 독립변수로 월별 시군구 충전소 보급대수, 월별 시군구 전기자동차 충전소 충전 횟수, 월별 전기자동차 등록대수 자료를 월 단위로 수집하고 수집된 7년간 자료 중 4년간 자료를 학습기간으로 3년간 자료를 검증 기간으로 적용하였다. 전기자동차 전력 수요량 예측 모형의 정확성을 검증하기위하여 통계적 방법인 Exponential Smoothing(ETS), ARIMA모형의 결과와 비교한 결과 ETS, ARIMA 각각의 오차율은 12%, 21%로 본 논문에서 제시한 ETS가 9% 더 정확하게 분석되었으며, 전기자동차 전력 수요량 예측 모형으로써 적합함을 확인하였다. 향후 이 모형을 이용한 전기자동차 충전소 설치 계획부터 운영관리 측면에서 활용될 것으로 기대한다. In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.