The personal exposures of nitrogen dioxide(NO2), microenvironmental levels and daily time activity patterns on Seoul subway station workers were measured from February 10 to March 12, 1999. Personal NO2 exposure for 24 hours were 29.40±9.75 ppb. NO2 level of occupational environment were 27.87±7.15 ppb in office, 33.60±8.64 ppb in platform and 50.13±13.04 ppb in outdoor. Personal exposure time of subway station workers was constituted as survey results with 7.94±3.00 hours in office, 2.82±1.63 hours in platform and 1 hours in outdoor. With above results, personal NO2 exposure distributions on subway station workers in Seoul were estimated with Monte Carlo simulation which uses statistical probabilistic theory on various exposure scenario testing. Some of distributions which did not have any formal patterns were assumed as custom distribution type. Estimated personal occupational NO2 exposure using time weighted average (TWA) model was 31.29±5.57 ppb, which were under Annual Ambient Standard (50 ppb) of Korea. Though arithmetic means of measured personal NO2 exposure was lower than that of occupational NO2 exposure estimated by TWA model, considering probability distribution type simulated, probability distribution of measured personal NO2 exposures for 24 hours was over ambient standard with 3.23%, which was higher than those of occupational exposure (0.02%). Further research is needed for reducing these 24 hour NO2 personal excess exposures besides occupational exposure on subway station workers in Seoul.

본 연구에서는 대구광역시에 위치하고 있는 서변 펌프가압장에서 발생되는 수격압을 최소화하기 위해 사용되는 감압밸브와 공기밸브의 최적 위치결정과 감압밸브의 최적 열림정도를 도모하였다. 가압장에서 발생하는 펌프의 급정지에 기인한 수격현상은 특성선방법을 이용하여 모의를 하였다. 최적화 방법은 강력한 탐색능력을 가지고 있으며 특히 비선형 문제를 해결하는데 탁월한 성능을 가지고 있는 유전자 알고리즘을 이용하여 실시하였다. 유전자 알고리즘의 계산결과는 감압밸브의 열

본 연구에서는 낙동강유역의 주요 수위표지점중 진동수위표지점에서 홍수위를 예측하기위한 신경망모형인 WSANN모형이 제시되었다. WSANN모형은 모멘트방법, 초기조건의 개선 및 적응학습속도에 의해 보완되어진 개선된 역전파훈련 알고리즘을 이용하였고, 본 연구에 사용된 자료는 훈련자료와 테스팅자료로 분할하였으며, 최적 은닉층 노드수를 결정하기 위하여 은닉층노드와 임계학습횟수로부터 경험식이 유도되었다. 그리고 WSANN모형의 보정은 4개의 훈련자료에 의해 실시되

금강에 위치한 부여취수장 부근에서의 유사이송을 모의하기 위해서 2차원 유한요소모형(RMA)이 적용되었다. 다양한 유량조건하에서 하상변동에 관한 모의수행결과는 과거 이 지역에서의 실측된 값과 잘 일치하였다. 본 연구에서 하도부의 개선과 취수구 부근의 하상저하를 위한 대안으로서 수제공 설치와 밤섬제거가 고려되었다. 수제공의 설치에 다라 좌안쪽으로 주흐름이 변환되었으며, 전 영역에 걸쳐서 비슷한 폭을 가진 하도가 형성되었다. 12,030{{{{ { m}^{3

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the most cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must build a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.