• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.146-146
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• 2022

A flash flood is one of the most hazardous natural events caused by heavy rainfall in a short period of time in mountainous areas with steep slopes. Early warning of flash flood is vital to minimize damage, but challenges remain in the enhancing accuracy and reliability of flash flood forecasts. The forecasters can easily determine whether flash flood is occurred using the flash flood guidance (FFG) comparing to rainfall volume of the same duration. In terms of this, the hydrological model that can consider the basin characteristics in real time can increase the accuracy of flash flood forecasting. Also, the predicted radar rainfall has a strength for short-lead time can be useful for flash flood forecasting. Therefore, using both hydrological models and radar rainfall forecasts can improve the accuracy of flash flood forecasts. In this study, FFG was applied to simulate some flash flood events in the Taehwa river basin by using of SURR model to consider soil moisture, and applied to the flash flood forecasting using predicted radar rainfall. The hydrometeorological data are gathered from 2011 to 2021. Furthermore, radar rainfall is forecasted up to 6-hours has been used to forecast flash flood during heavy rain in August 2021, Wulsan area. The accuracy of the predicted rainfall is evaluated and the correlation between observed and predicted rainfall is analyzed for quantitative evaluation. The results show that with a short lead time (1-3hr) the result of forecast flash flood events was very close to collected information, but with a larger lead time big difference was observed. The results obtained from this study are expected to use for set up the emergency planning to prevent the damage of flash flood.

• 한국산학기술학회논문지
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• 제11권8호
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• pp.2793-2800
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• 2010

이 논문에서는 여름철 일일 전력수요 총량을 예측하는 회귀모형을 개발한다. 경제적인 전력 생산계획을 수립하기위해 예측 오차율을 낮추는 것은 매우 중요하다. 전력수요가 크게 증가하는 여름철 전력수요를 예측하기위해 기존 연구에서는 외기온도 및 직전일 전력수요를 고려하였으나, 이 논문에서는 기존 연구에서 제시한 예측 오차율을 개선하기 위해 전력수요의 요일별 특성을 추가적으로 고려한 회귀모형을 개발한다. 이 논문에서는 여름철 전력수요의 요일별 패턴은 최고차항의 계수가 음수인 2차 함수 형태를 나타냄을 확인하였다. 즉, 2005년부터 2009년까지 5년간의 여름철 전력수요 패턴을 살펴본 결과 전력수요 총량은 일요일에 가장 낮고 월요일부터 증가하다가 수요일이나 목요일부터 다시 감소하는 패턴을 보인다. 이 논문에서 제안하는 여름철 전력수요 예측 회귀모형의 타당성을 검증하기 위해 2005년부터 2009년까지 실제 전력수요 데이터를 바탕으로 여름철 전력수요 총량을 예측한 결과, 평균 오차율(MAPE: Mean Absolute Percentage Error)과 최대 오차율(MPE: Maximum Percentage Error)이 각각 3.08%와 8.99%를 넘지 않는 수준임을 확인하였다. 또한 기존 연구에서 제시한 방법과 비교하여도 평균 오차율과 최대 오차율 모두 기존 연구에서 제시한 오차율보다 우수함을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1327-1336
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• 2017

본 연구에서는 단기 예측을 위한 자기회귀누적이동평균모형, 역전파 신경망 및 유전자 알고리즘의 결합 적용에 대하여 논의하고 이를 통한 유전자-신경망 알고리즘의 효용성을 살펴본다. 일반적으로 역전파 알고리즘은 지역 최소값에 수렴될 수 있는 단점이 있기 때문에, 여기서는 예측 정확도를 높이기 위해 역전파 신경망 구조를 최적화하고 유전자 알고리즘을 결합한 유전자-신경망 알고리즘 기반 예측모형을 구축한다. 실험을 통한 오차 비교는 KOSPI 지수를 이용한다. 결과는 이 연구에서 제안된 유전자-신경망 모형이 역전파 신경망 모형과 비교할 때 예측 정확도에서 어느 정도 유의한 효율성을 보여주고자 한다.

• Asian Journal of Innovation and Policy
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• 제3권2호
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• pp.172-192
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• 2014

This study aims to analyze the valuation of technology firms in the stock market to answer how before-market entities should be valuated. This study analyzes 230 market reports of 2012 for technology firms in the KOSDAQ under several hypotheses. The results are as follows: 90% used the 3 multiples methods consisting of PER multiples with 80%, PBR multiples 8.7% and EBITDA multiples 1.7%. The average of PER multiples was 15 with the range of 6.9 to 83. That of PBR multiples is 2.27. Forecasting for cash flow is not applied over 4 years, but mainly 2-3 years. The accuracy of forecasting was 18.8%, 34.4% and 8% according to the different definitions. No differences were found in the accuracy of forecasting between valuation methods, between the industries having more intangible assets and the industries having less, and between startups and general companies and between ages and listed ages.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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• pp.318-327
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• 2000

Managerial intuition is a well-recognized cognitive ability but still poorly understood for the purpose of developing effective decision support systems. this research investigates whether the differences in accuracy of "time series forecasting" are related to the differences in one's cognitive style, using statistical test The hypotheses established in the research model did not have positive correlation The lack of correlation between "cognitive style and physiological measures" and accuracy in forecasting may be caused by uncontrolled external variable. Thus, further analyses on physiological characteristics and brainwaves are needed. The approaches such as neural network and data mining are proposed.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1300-1312
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• 2019

BSTRACT In this paper, PM10 forecast model using DNN(Deep Neural Network) is developed for Seoul region. The previous Julian forecast model has been developed using weather and air quality data of Seoul region only. This model gives excellent results for accuracy and false alarm rates, but poor result for POD(Probability of Detection). To solve this problem, an WA(Wide Area) forecasting model that uses Chinese data is developed. The data is highly correlated with the emergence of high concentrations of PM10 in Korea. As a result, the WA model shows better accuracy, and POD improving of 3%(D+0), 21%(D+1), and 36%(D+2) for each forecast period compared with the Julian model.

• 한국시스템다이내믹스연구
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• 제9권1호
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• pp.125-141
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• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2007년도 춘계학술대회 논문집
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• pp.1931-1936
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• 2007

Demand forecasting for railroad traffic is fairly important to establish future policy and plan. The future demand of railroad traffic can be predicted by analyzing the demand of air, marine and bus traffic which influence the demand of railroad traffic. In this study, forecasting the demand of railroad traffic is implemented through neural network using the demand of air, marine and bus traffic. Estimate accuracy of the demand of railroad traffic was shown about 84% through neural net model proposed.

• Journal of Communications and Networks
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• 제13권6호
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.