• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.298-300
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• 2001

This paper presents a forecasting method for production volume of each model manufactured goods by using Back-Propagation technique of Neural Networks. As the learning constant and the momentum constant are respectively 0.65 and 0.94, the learning number is the least, and the forecating accuracy is the highest. When the learning process is more than 1,000 times, the accurate forecating was possible regardless of kind of product.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.62-64
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• 2001

This paper presents a forecasting method for production volume of each model manufacture d goods by using Back-Propagation technique of Neural Networks. As the learning constant and the momentum constant are respectively 0.65 and 0.94, the teaming number is the least, and the forecating accuracy is the highest. When the learning process is more than 1,000 times, the accurate forecating was possible regardless of kind of product.

• Journal of Korean Society of Transportation
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• v.13 no.1
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• pp.95-110
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• 1995

n this research neural -based model was developed to forecast link travel times , And it is also compared wiht other time series forecasting models such as Box-Jenkins model, Kalman filter model. These models are validated to evaluate the accuracy of models with real time series data gathered by the license plate method. Neural network's convergency and generalization were investigated by modifying learning rate, momentum term and the number of hidden layer units. Through this experiment, the optimum configuration of the nerual network architecture was determined. Optimumlearining rate, momentum term and the number of hidden layer units hsow 0.3, 0.5, 13 respectively. It may be applied to DRGS(dynamic route guidance system) with a minor modification. The methods are suggested at the condlusion of this paper, And there is no doubt that this neural -based model can be applied to many other itme series forecating problem such as populationforecasting vehicel volume forecasting et .

• Proceedings of the Korean System Dynamics Society
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• 2003.08a
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• pp.49-81
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• 2003

It is one of the important problems how to maintain the optimal electric power generation mix. The Objective of this study is development of a computer model which can be used to forecast the investment of power generation unit by the plant owners after restructuring the electricity industry. The impacts of the various government policies can be analyzed using the computer model, thus the government can formulate effective policies for achieving the desired electric power generation mix.

• 전기의세계
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• v.31 no.5
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• pp.368-374
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• 1982

This paper describes a combined algorithm for short-term-load forecating. One of the specific features of this algorithm is that the base, weather sensitive and residual components are predicted respectively. The base load is represented by the exponential smoothing approach and residual load is represented by the Box-Jenkins methodology. The weather sensitive load models are developed according to the information of temperature and discomfort index. This method was applied to Korea Electric Company and results for test periods up to three years are given.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.1
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• pp.51-58
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• 1998

The opening of an event in a given geographical area may be defined as an innovation. Visitors to the event adopt the innovation; therefore, their visitation patterns since the opening can be regarded as a diffusion process. Bass' model of diffusion process was applied to analyzing weekly visitation of Kwang-ju Viennale. Parameters of the Bass' model were estimated by regression analysis, and then reviewed in terms of applicability. Actual estimation of event visitation was implemented by calculation of the three parameters of the model based on the actual data. After comparing estimated value with actual value, it was concluded that Bass' model is applicable to estimating event visitation as far as it is the only prediction method available at this point.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.156-163
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• 2001

Business forecasting is vital to the success of business. There has been an increasing demand for building business forecasting software system to assist human being to do forecasting. However, the uncertain and complex nature makes is a challenging work to analyze, design and implement software solutions for business forecasting. Traditional forecasting systems in which their models are trained based on small collection of historical data could not meet such challenges at the information explosion over the Internet. This paper presents an agent oriented business forecasting approach for building intelligent business forecasting software systems with high reusability. Although agents have been applied successfully to many application domains. little work has been reported to use the emerging agent oriented technology of this paper is that it explores how agent can be used to help human to manage various business forecasting processes in the whole business forecasting life cycle.

• 한국데이터정보과학회:학술대회논문집
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• 2002.06a
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• pp.9-15
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• 2002

This paper presents the seasonal forecasting of the occurrence of tropical cyclone (TC) over Western North Pacific (WNP) using the generalized linear model (GLM) and dynamic linear model (DLM) based on 51-year-data (1951-2001) in TC season (June to November). The numbers of TC and TY are predictands and 16 indices (the E1 Nino/Southern Oscillation, the synoptic factors over East asia and WNP) are considered as potential predictors. With 30-year moving windowing, the estimation and prediction of TC and TY are performed using GLM. If GLM forecasts have some systematic error like a bias, DLM is applied to remove the systematic error in order to improve the accuracy of prediction.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.456-460
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• 2005

The purpose of this study is to develop a estimation model of migration with only population data using the cohort-survival model which has been used for forecasting future population. The fluctuation of population can be bisected to the natural change which can be occurred by birth and death and the social change which means migration. The factors of the social change are usually very important for establishing rural policies. However, researches using migration data has limitations because the usage of them are restricted. For verifying a estimation model of migration, comparing estimated population in 2000 year and migration quantity between 1996 and 2000 of 25 gu with real values, using population data and death ratio from 1995 to 2000 of the 25 gu in Seoul. Result shows a reliable data that R-square of forecating population model is 0.9755 and migration is 0.9180. So these model are worth to estimate a population and migration quantity to restricted migration data.

• Electronics and Telecommunications Trends
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• v.17 no.3 s.75
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• pp.80-86
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• 2002

본 연구는 현재 통신서비스 산업에서 가장 많이 사용하고 있는 5개 예측모형(단순 성장 모형, 단순 Logistic 모형, Gompertz 모형, 확장 Bass 모형, 시간 변동 Bass 모형)을 이용한 초고속 인터넷 가입자에 대한 예측력을 비교 평가하는 데 있다. 예측모형의 추정 방법으로 비선형 회귀방정식(nonlinear regression)을 사용하여 추정의 효율성을 높였다. 예측력 비교분석 기준은 (i) 포화점에 대한 타당성 (ii) 모수에 대한 통계적 유의성 (iii) 실제치 대비 예측치에 대한 AAD 기준을 통하여 예측모형의 예측력을 비교 평가하였다. 본 연구에서 실시한 방법론에 따라 다섯 가지 통신서비스 예측모형의 예측력을 분석한 결과 가장 작은 AAD를 나타낸 예측모형은 Log-Logistic 모형으로 나타났으며, 가장 큰 AAD를 나타낸 예측모형은 단순 Logistic 모형으로 나타났다. 또한 AAD 기준에서 보면 일반적으로 많이 사용하고 있는 Gompertz 예측모형과 Bass 모형 중에서는 Gompertz 예측모형이 더 우월한 것으로 나타났다.