• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.244-244
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• 2022

4차 산업혁명에 따라 유역 및 하천관리 사업, 각종 풍수해 예방사업 분야에 다양한 스마트기술이 도입되고 있으나 건설현장 침수 피해 사고는 지속적으로 발생하고 있다. 굴착공사 현장에서 발생할 수 있는 침수피해를 사전에 예측하기 위해서는 공정별로 변화하는 현장상황을 반영하여 다양한 강우 시나리오를 기반으로 침수 예측 모델링이 선행되어야한다. 따라서 본 연구에서는 2차원 동수역학 모형인 HDM-2D 모형을 기반으로 굴착공사 현장 침수피해 예측 모델을 개발하여 굴착공사현장 침수 예·경보 시스템에 탑재하고자 한다. 침수피해 예측 모델은 천수방정식을 Petrov-Galerkin stabilized scheme 으로 이산화하여 해석하는 수평 2차원 동수역학 흐름해석모델로서 수로 및 지표면 등 다양한 지형 상황에서의 물 흐름을 상세하게 해석할 수 있다. 지형자료 생성 이후 경계조건 부여를 통해 수행되며 침수발생지역의 유속, 수심, 수위를 취득할 수 있다. 배수지나 굴착공사 현장에 2차원 흐름해석을 적용하는 경우 지형의 경사나 배치가 공간에 따라 변화하므로 불연속적인 흐름을 유발하여 모의결과의 계산 오차를 검토해야 한다. 2차원 침수피해 예측 모형의 정확성을 확인하기 위해 지면 돌출부가 있는 흐름 문제와 테스트베드 대상지에 침수해석 모형을 적용하였다. 돌출부 흐름 문제의 경우 돌출부를 지나며 발생하는 유속과 수심 모의 결과를 상용모형과 비교검증 하였으며 테스트베드 대상지역에 침수피해예측모형을 적용했을때 지형 경사에 따른 흐름의 변화와 침수양상을 확인할 수 있었다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.599-605
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• 2012

It is important to decide traffic opening time for construction plan of epoxy asphalt pavement. For this purpose, strength prediction model of epoxy asphalt concrete is required. In this study, Marshall stability was measured according to temperature and time for making strength properties equation. Strength prediction model was developed using chemical kinetics considering temperature variation. The traffic opening time of epoxy asphalt pavement on bridge deck has been predicted using the developed model. The prediction and actual traffic opening times were different by 17-days, because weathers of year 2009-2011 used in prediction model were different from weather of year 2012. When the prediction model used the actually measured temperatures of pavement, the difference between real opening time and prediction opening time was two days. The correlation analysis result between measured strength and prediction strength revealed that the $R^2$ using accurate temperature of pavement was 0.95. An improved precise prediction result is to be obtained if the prediction model uses accurate temperature data of pavement.

• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.91-101
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• 2012

In planning phase of Public Multi-Housing Projects, it is required to develop the methodology and criteria for fair cost prediction with influencing power from planning phase to occupancy phase. Many studies still have focused on the prediction of cost by multiple regression. However, there is no logical explanation about the influence of nonmetric variables for the prediction of cost in planning phase. Accordingly, this research pursues a cost prediction model including nonmetric variables for use in planning phase. There are 3 steps of this research : 1) Finding the factors influencing construction cost and assigning variables for a multiple regression. 2) Conducting a dummy regression analysis with nonmetric variables and model validation by comparing actual cost data. 3) Developing the ratio of RC structure cost to wall structure cost by using cost predection model. The results could establish cost prediction process including the influence of nonmetric variables and the ratio of RC structure cost to wall structure cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.20-26
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• 2019

Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.61-68
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• 2022

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.450-450
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• 2018

최근 기후변화와 수온상승으로 인한 녹조발생이 빈번하게 나타나며, 녹조발생에 관한 관심은 꾸준히 증가하고 있는 추세이다. 본 연구는 효율적인 녹조관리를 위하여 모델트리를 활용하여 클로로필-a 단기조류예측 기법을 개발하였다. 대상지역으로 영산강수계의 죽산보를 선정하였으며, 2013년 1월부터 2016년 12월까지 나주 수질자동측정망의 일 단위자료와 동일기간 광주 기상청의 일별 기상자료를 이용하였다. 상관 분석을 통해 T-N, T-P, N/Pratio와 클로로필-a, 수온, 일사량, 강수량을 독립변수로, 단기(t+1일, t+3일, t+5일, t+7일) 클로로필-a를 종속변수로 선정하여 단기조류예측기법을 개발하였다. 수집한 자료의 데이터세트는 격일 간격으로 Training, Testing 기간으로 구분하여 적용한 결과, 상관계수는 1일 예측 시, Training 기간에 0.89, Testing 기간에 0.91, 3일 예측 시, Training 기간에 0.74, Testing 기간에 0.68, 5일 예측 시, Training 기간에 0.70, Testing 기간에 0.66, 7일 예측 시, Training 기간에 0.63, Testing 기간에 0.62로 나타났다. RMSE(Root Mean Square Error)는 1일 예측 시, Training 기간에 13.96, Testing 기간에 12.22, 3일 예측 시, Training 기간에 20.03, Testing 기간에 22.14, 5일 예측 시, Training 기간에 21.32, Testing 기간에 22.57, 7일 예측 시, Training 기간에 23.52, Testing 기간에 23.45로 나타났다. 예측주기에 따라 모델트리와 회귀식에서 활용한 독립변수는 1일 예측 시, 모델트리는 N/Pratio, 클로로필-a, 회귀식은 클로로필-a로 다르게 나타났다. 반면, 3일, 5일, 7일 예측 시, 모델트리와 회귀식에 활용된 변수는 같게 나타났다. 클로로필-a, 수온, 일사량은 5일 예측 시 활용된 변수로, 3일 예측 시에는 기상항목인 강수량이, 7일 예측 시에는 수질항목인 T-N, N/Pratio가 추가되었다. 특히 1일 예측 시 일 때, 높은 예측정도와 활용된 변수의 수가 적게 나타나는 것을 확인하였으며, 예측기간이 길어질수록 예측의 정확성이 낮아지고, 활용된 변수의 수가 많아지는 것을 확인하였다. 향후 적정한 예측기간을 판단하고 예측가능성을 높이기 위해서는 지속적인 자료취득 및 개선이 필요하며, 이를 바탕으로 적절한 단기조류예측이 가능할 것으로 판단된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.263-270
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• 2013

Recently, the prediction and analysis technology of marine environment are actively being studied since the ocean resources in the world is taken notice. The prediction of marine disaster by automatic collecting marine environment data and analyzing the collected data can contribute to minimized the damages with respect to marine pollution of oil spill and fisheries damage by red tide blooms and marine environment upsets. However the studies of the marine environment monitoring and analysis system are limited in South Korea. In this paper, we study the marine disasters prediction system model to analyze collection marine information of out sea and near sea. This paper proposes the models for the marine disasters prediction system as communication system model, a marine environment data monitoring system model, prediction and analyzing system model, and situations propagation system model. The red tide prediction model and summarizing and analyzing model is proposed for prediction and analyzing system model.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.211-224
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• 1996

In the research area of estimation of the software development efforts, a number of researches have been accomplished in order to control the costs and to make software more competitive. However, most of them were restricted to the functional algorithm models or the statistic models. Moreover, since they are dealing with the cases of foreign countries, the results are hard to apply directly to the domestic environment for the efficient project management because of lack of accuracy, fitness, flexibility and portability. Therefore, it is appropriate to suggest and propose a new approach supported by artificial neural network which is composed of back propagation and feel-forward algorithms to improve the exactness of the efforts estimation and to advance practical uses. In this study, the artificial neural network approach is used to model the software cost estimation and the results are compared with the revised COCOMO and the multiregression model in order to validate the superiority of the model.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.2
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• pp.124-129
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• 2007

Prediction methods for a rotor averaged inflow in forward flight are investigated in this study. The investigated methods are Drees linear inflow model, Mangler & Squire model and free vortex wake(FVW) method. Predictions have been performed for a four-blade rotor operating at three different advance ratios i.e. 0.15, 0.23 and 0.30, at which experimental data are available. According to results, Drees model has a limitation for the inflow non-uniformity prediction due to an inherent linear characteristics. Mangler & Squire model has a reasonable accuracy except the disk edge region. KARI FVW method has very good accuracy and has better accuracy than the other FVW method especially in inboard region. However, there are some discrepancies in retreating side due to the dynamic stall effect and in near hub region due to the fuselage upwash effect.

• Journal of the Korean Society for Marine Environment & Energy
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• v.4 no.2
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• pp.3-14
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• 2001

In order to predict nutrient circulation in Hakata bay, we have developed an ecosystem model named the Sediment-Water Ecological Model (SWEM). The model, consisting of two sub-models with hydrodynamic and biological models, simulates the circulation process of nutrient between water column and sediment, such as nutrient regeneration from sediments as well as ecological structures on the growth of phytoplankton and zooplankton. This model was applied to prevent eutrophication in Hakata bay, located in western Japan. The calculated results of the tidal currents by the hydrodynamic model showed good agreement with the observed currents. Moreover, SWEM simulated reasonably well the seasonal variations of water quality, and reproduced spatial heterogeneity of water quality in the bay, observed in the field. According to the simulation of phosphorus circulation at the head of the bay, it was predicted that the regeneration process of phosphorus across the sediment-water interface had a strong influence on the water quality of the bay.