• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.94-101
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• 1994

This paper describes a short-term prediction model for the movement of an oil slick in shallow waters. Under the assumption that the initial movement of the oil slick is governed by spreading and advection, the model has been developed and applied to Kyungki-Bay near Incheon Harbor. The initial spreading is estimated by using an empirical formula. The depth-averaged momentum equations are solved numerically for the volume transport velocities, in which the $M_2$ tide is the main driving source. A staggered grid system is adopted fur spatial discretization and the half-time method is implemented for time marching. The numerical result is visualized with the help of animation and thus the contaminated area is displayed on a monitor in time sequence. The input data are the time, the location and the volume of spill accident as well as environmental data such as md and $M_2$ tide.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.224-224
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• 2017

지구온난화로 인한 기후변화 등으로 안전한 하천구조물을 설계하기 위해서는 신뢰할 수 있는 홍수량 산정이 필요하다. 신뢰할 수 있는 홍수량 산정을 위해서는 정도 높은 과거 수문자료가 필요하나 국내의 많은 중소 규모유역이 미계측 유역 또는 과거 수문자료 부족으로 신뢰 할 수 있는 홍수량 산정이 어려운 실정이다. 본 연구에서는 미계측 유역의 홍수량 산정을 위하여 확률분포모형(PDM)의 매개변수 지역화를 수행하였다. 매개변수 지역화 연구를 수행하기 위하여, 금강 25개 유역을 대상으로 유역별 9~18개의 단기홍수수문사상을 선정하였다. 선정된 단기홍수수문사상을 확률분포모형에 적용하기위하여, MCAT (Monte Carlo Analysis Toolbox)을 활용하여 검정 및 검증을 수행하였으며, 목적함수는 수문곡선 모든 구간을 반영하는 NSE (Nash Sutcliffe Efficiency)와 고유량 부분을 반영하는 RMSE (Root Mean Squared Error) - FH를 적용하였다. 각각의 목적함수에 대하여 검정 모형 매개변수와 유역 특성인자의 다중 선형회귀식을 강우유출모형 매개변수 지역화 모형으로 제시하였다. 매개변수 지역화 결과의 평가를 위하여 청주 유역을 미계측 유역으로 가정하였다. 청주 유역에 대하여 지역화 매개변수를 적용한 결과, 17개의 사상 중 11개의 사상에서 NSE 목적함수 값이 0.5이상으로 전체적인 수문곡선의 경향성을 보였으며, 첨두 홍수량은 17개 사상 중 11개 사상에서 관측 첨두 홍수량 값의 20%이내를 제시하여 적합한 결과를 제시하였다. 또한 금강 25개 유역에 Jackknife 방법으로 검정 결과 관측 첨두 홍수량 값 20%이내의 성능을 보이는 사상이 56%를 포함하고 있어 의미있는 지역화 모형을 제시하였다고 판단된다. 본 연구에서 제시한 매개변수 지역화 방법은 미계측 유역의 유출모의에 활용될 수 있음을 확인하였다.

• Restorative Dentistry and Endodontics
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• v.36 no.5
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• pp.425-430
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• 2011

Objectives: This in vitro study investigated whether short-term application of calcium hydroxide in the root canal system for 1 and 4 wk affects the fracture strength of human permanent teeth. Materials and Methods: Thirty two mature human single rooted mandibular premolars in similar size and dentin thickness without decay or restorations were hand and rotary instrumented and 16 teeth vertically packed with calcium hydroxide paste and sealed coronally with caviton to imitate the endodontic procedure and the other 16 teeth was left empty as a control group. The apicies of all the samples were sealed with resin, submerged in normal saline and put in a storage box at $37^{\circ}C$ to mimic the oral environment. After 1 and 4 wk, 8 samples out of 16 samples from each group were removed from the storage box and fracture strength test was performed. The maximum load required to fracture the samples was recorded and data were analysed statistically by the two way ANOVA test at 5% significance level. Results: The mean fracture strengths of two groups after 1 wk and 4 wk were similar. The intracanal placement of calcium hydroxide weakened the fracture strength of teeth by 8.2% after 4 wk: an average of 39.23 MPa for no treatment group and 36.01 MPa for CH group. However there was no statistically significant difference between experimental groups and between time intervals. Conclusions: These results suggest that short term calcium hydroxide application is available during endodontic treatment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.6
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• pp.1453-1463
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• 1994

The SCS Curve Number(CN) method has become widely accepted as a procedure of estimating stormflow volumes for design and natural events in small watersheds. The applicability of this method for calculating the flushing time was evaluated as compared with the net volume transport(NVT) method in Masan Bay, Korea. It is shown that the flushing time using the CN method ranged from 10.9 to 15.3 days under the well mixed condition, that the time using the NVT method was 13.9 days averaged over 6 days of field data. These results were revealed that two methods calculated the approximate times as shown above. The relationships between the run-off, Qr, and the flushing time, t, are expressed as the following forms. $t_1=228.79Q_r^{-0.9996}$ in case of well mixed condition, (1) $t_2=131.06Q_r^{-1.0}$ in case of two layered model. (2) Those empirical expressions are represented that the relationships between Q and t are nonlinear as those as Bumpus obtained in Boston Inner Harbour. Therefore, the CN method will permit calculation of the flushing time for any given bay to be unexpected as water balance under the condition of short-time (0.5 day) data, instead of NVT method based on the long-time (at least 3 days over) data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.875-886
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• 1994

This study is to develop an advanced storm runoff analysis program which takes geomorphological characteristics of watershed into consideration in determining model parameters. Basic concept of storm runoff modelling is based upon the kinematic wave theory. And numerical solution is obtained by the characteristic curve method. The storm runoff analysis program developed by this study is composed of multiple equivalent roughness sub-basins, each of which has two equivalent catchments on both side of a stream. Because it is based upon the stream-order of the Strahler system, the equivalent catchment-stream network reflects the stochastic geomorphological characteristics in the model parameter. Applicability and reliability of the storm runoff analysis program is evidenced by model calibration and verification process utilizing geographical and hydrological data of the Bocheong-river area which is a representative watershed of IHP projects in Korea. This study will hopefully contribute to hydrological calculation essentially required to understand water quality effect caused by regional development.

• Korean Journal of Childcare and Education
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• v.11 no.1
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• pp.289-303
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• 2015

The aim of this study was to examine the short-term longitudinal effect of creativity-fostering programs for pre-early childhood teachers on their problem solving ability and self-efficacy. The participants of this study were 67 pre-early childhood teachers (36 from the experimental group and 31 from the control group) in Seoul, Kyunggi-do, and Chungcheong-do, Korea. Pre-early childhood teachers' problem solving ability and self-efficacy was measured three times. Data were analyzed with a $2{\times}3$ ($group{\times}test$) two-way analysis of variance for repeated measures. The change in problem solving and self-efficacy appeared to be dependent on the group and test. Further statistical analysis of the data indicated that the participation of creativity-fostering programs for pre-early childhood teachers was longitudinally effective on their problem solving ability and self-efficacy.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1007-1012
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• 2008

유역통합수자원관리의 시작은 기상예측정보의 제공으로부터 시작된다. 하지만, 기상예측정보는 단기, 중기, 장기로 구분되며, 제공되는 정보가 수자원 운영에 필요한 정보와 시간적으로나 공간적으로 차이가 나며, 가공에 많은 전문가들의 노력이 필요하여 실무에서의 적용에 많은 어려움이 따른다. 따라서 본 연구에서는 이러한 문제들을 해결하고 용이하게 수자원 운영자에게 필요한 기상정보를 적절한 형태의 가공을 통하여 자동적으로 제공해 주는데 그 목적이 있다. 이러한 시스템의 구축을 통해 향후 수자원 운영에 있어 필수적인 의사결정 정보를 제공해 주어 수자원의 이용효율을 높이고자 한다. 구축된 시스템은 금강 유역에 대해 소유역단위로 장기 유출의 입력자료인 일단위 예측 강수를 30일간 제공하도록 시스템을 구축하였다. 단기(1일$\sim$2일)에는 RDAPS의 모의 결과인 Grib파일을 자동 추출하여 예측 강수를 제공한다. 1일에 두 번 모의되는 RDAPS의 결과를 일단위로 제공하기 위해 여러 가지 case별 분석을 실시하여 가장 적합한 기법을 이용하여 일단위 시계열을 구축하는 시스템을 설계하였다. 중기(3일$\sim$10일)에는 GDAPS 결과인 Grib파일을 자동 추출하여 유역단위 시계열을 구축한 뒤 과거 자료를 이용한 연 평균 자료를 이용하여 가중치를 곱하여 시계열을 구축하였다. 장기(11일$\sim$30일) 시계열의 구축을 위해서는 단기 및 중기 예측 시계열을 이용하여 과거 시계열 자료와의 통계적 비교 분석을 이용하여 유사 시계열을 추출한 후 과거 자료에 대한 평균값과 기상 전망을 이용하여 가중치를 부여하는 방법 등을 이용하여 시스템을 구축하였다. 본 시스템은 한국수자원공사에서 운영 중인 RRFS모형의 입력 자료를 자동 생성할 수 있는 기능을 제공하도록 설계되었다. 이러한 시스템의 구축을 통해 기상정보를 다루는데 익숙하지 않은 수자원 운영자들에게 비교적 용이하게 유역단위 기상예측 정보를 추출하는데 큰 도움이 될 것으로 기대한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.