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구윤서(Yoon-Seo Koo),이지형(Ji-Hyoung Lee),류상욱(Sang-Ouk Ryu),김경호(Kyung-Ho Kim) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.10
본 논문에서는 압력 · 온도 센서를 이용하여 수변자의 수면상태를 인식하고 이를 감지할 수 있는 수면상태 감지 시스템을 제안 하였다. 기존의 수면상태를 측정하는 방법에 있어 문제점으로 들 수 있는 고가의 장비, 욕정의 불편 등을 해소하기 위해 사용이 간단한 Straingage 타입의 압력센서와 프로브 타입의 온도센서를 이용하여 저비용의 효율적인 시스템을 구현 하였고, 수면 매트에 실제 적용하여 그 유효성을 평가하였다. 제안된 시스템은 압력 · 온도센서를 이용해 수면 매트부, 센싱데이터를 감지 · 수집하여 수신된 데이터를 증폭하는 수면상태 감지정보 시스템부로 구성되었다. 시스템 구축을 위해 먼저, 수면 매트부는 비접촉 방식의 압력 온도 센서를 사용하였고, 수면상태 감지정보 시스템부는 미세한 변화를 보이는 데이터를 차동 증폭기 원리를 이용하여 증폭하였다. 센서가 수면자에 의해 변환할 때 발생되는 아날로그 신호를 검출 증폭한 후 감지하는 시스템이다. 본 연구에서 제안한 수면상태 감지 시스템을 이용하여 개인생활 습관인 수면시간을 실시간으로 감지하고 데이터화하여 수면자의 수면 상태를 파악하여 건강한 수면을 위한 방법을 권고할 수 있다. 향후 감지된 데이터를 이용해 실시간으로 가족들의 수면상태를 알릴 수 있는 헬스케어 모바일 응용 서비스 로또 활용이 기대된다.
지역 분할 방법에 의한 ISCST3 모델링으로 수도권 지역에서 SO<sub>2</sub> 농도 예측 연구
구윤서,김성태,신봉섭,신동윤,이정주,Koo, Youn-Seo,Kim, Sung-Tae,Shin, Bong-Sup,Shin, Dong-Yoon,Lee, Jeong-Joo 한국환경영향평가학회 2003 환경영향평가 Vol.12 No.4
$SO_2$ concentrations in the Seoul Metropolitan Area (SMA) were predicted by the regional segment ISCST3 modeling. The SMA was segmented by three modeling regions where the weather monitoring station exists since the area of the SMA, approximately $100km{\times}100km$, is too wide to be modeled by one modeling domain. The predicted concentrations by the model were compared with the measured concentrations at 39 air monitoring stations located in the SMA to validate the ISCST3 modeling coupled with the regional segment approach. The predicted concentrations by the regional segment method showed better performance in depicting the measurements than those by the non-segment ISCST3 modeling. The correction methods of the calculated concentrations reviewed were here the correlation method by the first order linear equation and the ratio method of observed to calculated concentrations. The corrected concentrations by two methods showed good agreement with the measured data. The ratio method was, however, easily applicable to the concentration correction in case of a wide modeling region considered in this study.
대기확산 모델링 Software, AirMaster 개발
구윤서,윤희영,김성태,전경석,박성순,권희용,황주현,김종화,최종근,이임학,Koo, Youn-Seo,Yoon, Hee-Young,Kim, Sung-Tae,Jeon, Kyung-Seok,Park, Sung-Soon,Kweon, Hee-Yong,Hwang, Ju-Hyun,Kim, Jong-Hwa,Choi, Jong-Keun,Lee, Im-Hak 한국환경영향평가학회 2000 환경영향평가 Vol.9 No.4
A Korean air dispersion modeling software, AirMaster, was developed on a basis of dispersion theories adopted in U.S. EPA's ISC3 (Industrial Source Complex - version 3) model to assess the air quality impact from the stacks. Key characteristics of AirMaster are as follows: 1) The building downwash effect can be easily simulated; 2) The screen, long term, and short term models can be run independently; 3) The input data to run the model such as meteorological and terrain data are supplied automatically from the databases in AirMaster; and 4) The modeling procedure is easy and simple under the GUI window environment. In order to validate AirMaster, comparisons with ISC3 model and Indianapolis tracer experiment were carried out. It was shown that AirMaster was identical to ISCST3 and ISCLT3 models in predicting the 1 hr to annual concentrations from the stack under various stack emission and meteorological conditions. The 1 hr concentrations predicted by AirMaster also showed a good agreement with the Indianapolis tracer measurements.
PM<sub>10</sub> 예보 향상을 위한 민감도 분석에 의한 역모델 파라메터 추정
유숙현,구윤서,권희용,Yu, Suk Hyun,Koo, Youn Seo,Kwon, Hee Yong 한국멀티미디어학회 2015 멀티미디어학회논문지 Vol.18 No.7
In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM<sub>10</sub> emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.