• 한국시뮬레이션학회논문지
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• 제12권4호
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• pp.83-94
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• 2003

Managing a call center is a complex and diverse challenge. Call center becomes a very important contact point and a data ware house for successful CRM. Improving performance of call center is critical and valuable for providing better service. In this study we applied forecasting technique to estimate incoming calls and ProModel based simulation model to enhance performance of a mobile telecommunication company's call center. The simulation study shows reduction in managing cost and better customer's satisfaction.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.197-197
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• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

• 한국지능시스템학회논문지
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• 제25권2호
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• pp.126-132
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• 2015

기술예측은 현재까지 관측된 특정기술에 대한 데이터를 바탕으로 미래에 그 기술이 어떠한 상태가 될 지를 알아보는 것으로써 기술경영 전략 수립 시 유용하게 사용된다. 현재는 전문가 의견을 바탕으로 한 분석법을 이용하여 기술예측을 실시하고, 국가, 기업 그리고 연구자는 이를 근거로 연구개발의 방향 및 전략을 수립한다. 전문가의 의견을 바탕으로 하는 정성적 기술예측은 전문가마다 다른 결과를 예상할 수 있고, 여러 전문가의 의견을 수집하여야 하므로 많은 시간과 비용을 필요로 한다. 이러한 문제점을 극복하고 예측에 대한 객관성을 확보하여 기업의 연구개발 의사결정을 돕기 위해 정량적 예측법을 바탕으로 한 기술예측 방법이 연구되고 있다. 본 논문에서는 정량적 분석법에 기반 한 기술예측 방법론에 대한 연구를 제안한다. 제안된 방법은 데이터 수집, 주성분 분석, 그리고 데이터마이닝 기법 중 하나인 로지스틱 회귀분석을 이용한 예측 단계로 구성되어 있다. 본 연구에서는 무인자동차에 관련된 특허 문서를 이용하여 데이터를 수집 및 추출하고, 특허문서의 텍스트를 마이닝하여 분석이 가능한 형태로 구축한다. 주성분분석 후 추출된 주성분 점수를 이용하여 로지스틱 회귀분석을 실시하며 이를 바탕으로 개발현황 분석 및 기술예측을 시행한다.

• 한국환경과학회지
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• 제9권6호
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• pp.455-462
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• 2000

This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.301-303
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• 2000

This paper presents Kohonen neural network and wavelet transform analysis based technique for industrial peak load forecasting for the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a six-scale synthesis technique.

• 한국습지학회지
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• 제21권4호
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• pp.281-289
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• 2019

본 연구에서는 남부 내륙지역에 속한 시군구별 태풍으로 인한 피해를 예측할 수 있는 태풍피해예측모형을 개발하였다. 내륙지역의 태풍 피해는 호우, 강풍으로 인한 피해가 복합적으로 발생하므로, 모형을 구성하는 변수가 많고 다양하나, 내륙지역 시군구 단위의 피해사례는 모형을 개발할 만큼 충분하지 않다. 태풍피해 관련 수문기상 자료는 3시간 간격 지속기간별 최대 강우량, 총강우량, 1-5일 선행강우량, 최대풍속 및 제주도 인근 지역에서의 태풍중심기압을 이용하였다. 피해자료의 부족을 고려하기 위해 군집화를 하였으며, 강우 관련 자료의 다중공선성을 제거하기 위하여 주성분분석 등 다변량 통계분석을 이용하여 권역별(경남, 경북, 전남, 전북)로 피해예측모형을 개발하였다. 모형에 의한 태풍피해추정치와 실측치는 최대 2.2배 정도까지 차이가 발생하였는데, 이는 강풍에 의한 피해를 추정하기 어렵고, 전국 69개 ASOS 관측소에 의한 강우자료가 지역적 강우특성을 제대로 반영하지 못하기 때문인 것으로 추정된다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.39-42
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• 1991

The Application of Artificial Neural Network(ANN) to forecast a load in a power system is investigated. The load forecasting is important in the electric utility industry. This technique, methodology based on the fact that parallel structure can process very fast much information is a promising approach to a load forecasting. ANN that is highly interconnected processing element in a hierachy activated by the each input. The load pattern can be divided distinctively into two patterns, that is, weekday and weekend. ANN is composed of a input layer, several hidden layers, and a output layer and the past data is used to activate input layer. The output of ANN is the load forecast for a given day. The result of this simulation can be used as a reference to a electric utility operation.

• 한국농공학회지
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• 제43권2호
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• pp.85-93
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• 2001

The basic concept of the model is to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance depending on the concentration time(Tc) and soil moisture retention storage(Sa). Simplex method that is a multi-level optimization technique was used to search for the determination of the best parameters of RETFLO (REal-Time FLOod forecasting) model. The flood forecasting model developed was applied to several strom event of Yedang reservoir during past 10 years. Model perfomance was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1995년도 추계학술대회발표논문집; 서울대학교, 서울; 30 Sep. 1995
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• pp.334-343
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• 1995

In recent years, neural networks have been developed as an alternative to traditional statistical techniques. In this study, a neural network model was compared to traditional forecasting models in terms of their capabilities to forecast passenger traffic for flights between U.S. and Korea. The results show that the forecasting ability of the neural networks was superior to the traditional models. In terms of accuracy, the performance of the neural networks was quite encouraging. Using mean absolute deviation, the neural network performed best. The new technique is easy to learn and apply with commercial neural network software. Therefore, airline decision makers should benefit from using neural networks in forecasting passenger loads.