• 한국환경과학회지
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• 제12권3호
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• pp.359-366
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• 2003

Ecological society and energy conservative systems has become a subject of world wide attention. To examine the technologies of such systems as resource recycling society, this study is proposed for using rainwater as energy source and water resources in urban area. Useful informations for planning of utilizing rainfall as energy source, water resources, emergency water and controlling flood are discussed with model systems in urban area. It is calculated that the rate of utilizing rainwater, amounts of utilizing rainwater, substitution rate of supply water, amounts of overflow rainwater according to rain storage tank volume. By applying the past weather data, The optimum volume of rain water storage was calculated as 200m$^3$ which mean no benefits according to the increase of storage tank volumes. For optimum planing and control method at the model system, several running method of rainwater storage tank was calculated. The optimum operating method was the using weather data as 3hours weather forecast.

• 한국항해항만학회지
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• 제44권6호
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• pp.517-523
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• 2020

평택·당진항은 국내에서 가장 많은 완성차 물동량을 처리하는 항만으로 특히 수입 자동차는 국내 전체 물동량의 95% 이상을 처리하고 있다. 그러나 2015년부터 수입 자동차 물동량 증가가 정체되고 있어, 항만 개발이나 자동차 관련 산업에 투자를 계획하고 있는 관계자들에게 새로운 물동량 추정이 필요한 시점이다. 한편, 자동차 물동량 예측 시 그동안 GDP(국내총생산) 등 경제와 관련된 변수가 많이 활용되었으나, 선행연구를 통해 선진국에서는 이러한 경제관련 변수가 자동차 물동량에 미치는 영향이 점점 감소하고 있는 것으로 확인되었다. 특히 우리나라와 같이 짧은 시간내에 경제성장을 달성하고 선진국으로 진입한 경우에는 경제변수에 대한 주의가 필요하다. 이에 본 연구에서는 우리나라가 직면하고 있는 인구감소를 주요 요인으로 하여 시스템다이내믹스를 통해 평택·당진항의 수입 승용차 물동량을 예측하고자 한다. 예측결과 평택·당진항의 수입승용차 물량은 '21년을 기점으로 조금씩 감소하는 것으로 분석되었다. 그리고 예측된 결과값의 정확도를 측정하기 위해 MAPE 검증을 실시하였고, 수입 승용차 점유율에 대한 시나리로 분석을 실시하였다.

• Computers and Concrete
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• 제29권 6호
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• pp.361-374
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• 2022

On the premise of ensuring that the automatic and quantitative discharging function of concrete conveyors is met, the accuracy of the weight forecast by the mathematical model of the screw conveying volume is improved, and the error of the weight of the concrete parts and the accumulation thickness is reduced. In this paper, the discrete element method (DEM) is used to simulate the macroscopic flow of concrete. Using the concrete discrete element model, the size of the screw conveyor is set, and establish the response model between the influencing factors (process and structure) and the concrete mass flow rate according to the design points of the screw discharging experiment. The nonlinear data fitting method is used to obtain the volumetric efficiency function under the influence of process and structural factors, and the traditional screw conveying volume model is improved. The mass flow rate of concrete predicted by the improved mathematical model of screw conveying volume is consistent with the test results. The model can accurately describe the conveying process of concrete and achieve the purpose of improving the accuracy of forecasting the weight of discharged concrete.

• 산업경영시스템학회지
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• 제46권2호
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• pp.152-159
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• 2023

Due to COVID-19, changes in consumption trends are taking place in the distribution sector, such as an increase in non-face-to-face consumption and a rapid growth in the online shopping market. However, it is difficult for small and medium-sized export sellers to obtain forecast information on the export market by country, compared to large distributors who can easily build a global sales network. This study is about the prediction of export amount and export volume by country and item for market information analysis of small and medium export sellers. A prediction model was developed using Lasso, XGBoost, and MLP models based on supervised learning and deep learning, and export trends for clothing, cosmetics, and household electronic devices were predicted for Korea's major export countries, the United States, China, and Vietnam. As a result of the prediction, the performance of MAE and RMSE for the Lasso model was excellent, and based on the development results, a market analysis system for small and medium sellers was developed.

• 한국항해항만학회지
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• 제32권6호
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• pp.425-431
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• 2008

본 연구에서는 광양항의 장래 컨테이너 물동량 및 교통량을 일변량 시계열모형을 통해 예측하고, 컨테이너 선박교통량을 산출하였다. 광양항의 물돌량과 입항 척당 물동량의 시계열 모형은 모두 추세와 계절적 변동이 있는 Winters 가법 모형으로 최적합 되었다. 광양항의 컨테이너 물동량은 2007년과 비교하여 2011년과 2015년에 각각 7.4%, 16.2% 가량 증가하여 약 2,756천TEU, 4,470천TEU가 될 것으로 예측되었다. 또한 2011년과 2015년의 컨테이너 입항 척당 평균 물동량은 2007년 대비 약 30.3%, 54.6% 증가하여 각각 675TEU, 801TEU가 될 것으로 예측되었다. 광양항에 대한 컨테이너 선박의 교통량은 2011년과 2015년에 각각 4,078척, 5,921척이 될 것으로 추정되었다.

• 대기
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• 제31권2호
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• pp.157-170
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• 2021

To investigate the effect of snowfall on the traffic of general roads in Gangwon-do, case analysis was performed in Gangneung, Pyeongchang, and Chuncheon using ASOS (Automated Synoptic Observing System) snowfall data and VDS (Vehicle Detector System) traffic data. First, we analyzed how much the traffic volume and speed decrease in snowfall cases on regional roads compared to non-snow cases, and the characteristics of monthly reduction due to snowfall were investigated. In addition, Pearson correlation analysis and regression analysis were performed to quantitatively grasp the effect of snowfall on traffic volume and speed, and sensitivity tests for snowfall intensity and cumulative snowfall were performed. The results showed that the amount of snowfall caused decrease both in the traffic volume and speed from usual (non-snowfall) condition. However, the trend was different by region: The decrease rate in traffic volume was in the order of Gangneung (17~22%), Chuncheon (14~17%), and Pyeongchang (11~14%). The decrease rate in traffic speed was in the order of Chuncheon (9~10%), Gangneung (8~9%), Pyeongchang (5~6%). No significant results were found in the monthly decrease rate analysis. In all regions, traffic volume and speed showed a negative correlation with snowfall. It was confirmed that the greater the amount of traffic entering the road, the greater the slope of the trend line indicating the change in snowfall due to the traffic volume. As a result of the sensitivity test for snowfall intensity and cumulative snowfall, the snowfall information at intervals of 6-hours was the most significant.

• 한국농공학회지
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• 제37권5호
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• pp.81-89
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• 1995

One of the most difficult problem to estimate the flood inflow is how to understand the effective rainfall. The effective rainfall is absolutely influenced by the condition of soil moisture in the watershed just before the storm event. DAWAST model developed to simulate the daily streamflow considering the meteologic and geographic characteristics in the Korean watersheds was applied to understand the soil moisture and estimate the effective rainfall rather accurately through the daily water balance in the watershed. From this soil moisture and effective rainfall, concentration time, dimensionless hydrograph, and addition of baseflow, the rainfall-runoff model for flood flow was developed by converting the concept of long-term runoff into short-term runoff. And, real-time flood forecasting model was also developed to forecast the flood-inflow hydrograph to the river and reservoir, and called RETFLO model. According to the model verification, RETFLO model can be practically applied to the medium and small river and reservoir to forecast the flood hydrograph with peak discharge, peak time, and volume. Consequently, flood forecasting and warning system in the river and the reservoir can be greatly improved by using personal computer.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• 대기
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• 제31권3호
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• pp.251-265
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• 2021

This study examined the impact of assimilating the bending angle (BA) obtained via the global navigation satellite system radio occultation (GNSS RO) of the three new satellites (KOMPSAT-5, FY-3C, and FY-3D) on analyses and forecasts of a numerical weather prediction model. Numerical data assimilation experiments were performed using a three-dimensional variational data assimilation system in the Korean Integrated Model (KIM) at a 25-km horizontal resolution for August 2019. Three experiments were designed to select the height and quality control thresholds using the data. A comparison of the data with an analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system showed a clear positive impact of BA assimilation in the Southern Hemisphere tropospheric temperature and stratospheric wind compared with that without the assimilation of the three new satellites. The impact of new data in the upper atmosphere was compared with observations using the infrared atmospheric sounding interferometer (IASI). Overall, high volume GNSS RO data helps reduce the RMSE quantitatively in analytical and predictive fields. The analysis and forecasting performance of the upper temperature and wind were improved in the Southern and Northern Hemispheres.

• 한국가스학회지
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• 제23권5호
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• pp.52-58
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• 2019

본 연구에서는 국내 도시가스 인수량에 대한 예측 모델을 개발하였다. 국내의 도시가스 회사는 KOGAS에 차년도 수요를 예측하여 보고해야 하므로 도시가스 인수량 예측은 도시가스 회사에 중요한 사안이다. 도시가스 사용량에 영향을 미치는 요인은 용도구분에 따라 다소 상이하나, 인수량 데이터는 용도별 구분이 어렵기 때문에 특정 용도에 관계없이 영향을 주는 요인으로 외기온도를 고려하여 모델개발을 실시하였다.실험 및 검증은 JB주식회사의 2008년부터 2018년까지 총 11년 치 도시가스 인수량 데이터를 사용하였으며, 전통적인 시계열 분석 중 하나인 ARIMA(Auto-Regressive Integrated Moving Average)와 딥러닝 기법인 LSTM(Long Short-Term Memory)을 이용하여 각각 예측 모델을 구축하고 두 방법의 단점을 최소화하기 위하여 다양한 앙상블(Ensemble) 기법을 사용하였다. 본 연구에서 제안한 일별 예측의 오차율 절댓값 평균은 Ensemble LSTM 기준 0.48%, 월별 예측의 오차율 절댓값 평균은 2.46%, 1년 예측의 오차율 절댓값 평균은 5.24%임을 확인하였다.