• Journal of Agricultural Extension & Community Development
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• v.19 no.2
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• pp.301-320
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• 2012

The study uses a seasonal ARIMA model to forecast the number of tourists of Yeongdeok in an uni-variable time series. The monthly data for time series were collected ranging from 2006 to 2011 with some variation between on-season and off-season tourists in Yeongdeok county. A total of 72 observations were used for data analysis. The forecast multiplicative seasonal ARIMA(1,0,0)$(0,1,1)_{12}$ model was found the most appropriate one. Results showed that the number of tourists was 10,974 thousands in 2012 and 13,465 thousands in 2013, It was suggested that the grasping forecast model is very important in respect of how experts in tourism development in Yeongdeok county, policy makers or planners would establish strategies to allocate service in Yeongdeok tourist destination and provide tourism facilities efficiently.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.47-56
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• 2012

Service Parts Management is defined as a supply management associated with service parts from the part suppliers to the final customer. A series of process to improve the customer service level by forecasting the demand and to minimize cost by maintaining the inventory level is included. Uniqueness such as missing value correction, the data pattern analysis and planned order system is designed and implemented. Main feature of order management system is to calculate order amount and order time based on selection of optimal forecasting algorithm.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.507-510
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• 2003

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. A short-term water demand forecasting technology will be developed taking into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-time Water Information System. The well-known SSARR model was selected for the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Proceedings of the KIEE Conference
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• summer
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• pp.698-700
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• 2004

Electric power restructuring offers a major change to the vertically integrated utility monopoly. Deregulation has had a great impact on the electric power industry in various countries. Bidding competition is one of the main transaction approaches after deregulation. The energy trading levels between market participants is largely dependent on the short-term price forecasts. This paper presents the short-term System Marginal Price (SMP) forecasting implementation using backpropagation Neural Network in competitive electricity market. Demand and SMP that supplied from Korea Power Exchange (KPX) are used by a input data and then predict SMP. It needs to analysis the input data for accurate prediction.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1481-1483
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• 1999

Power distribution system planning for maximum customer satisfaction and system efficiency requires accurate forecast of future demand in service area. Spatial load forecasting method provides a more accurate estimation of both magnitudes and location of future electrical load. This method considers the causes of load growth due to addition of customers and per capita consumption among customers by land use (residential, commercial and industrial). So the land-use study and it's preference for small area is quite important. This paper proposes land-use preference estimation method based on fuzzy logic. Fuzzy logic is applied to computing preference scores for each land-use and by these scores the customer growth is allocated in service area. An simulation example is used to illustrate the proposed method.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.101-105
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• 1998

A large system predictor, which can perform prediction of sales trend in a huge number of distribution centers, is presented using neural predictive model. There are 20,000 number of distribution centers, and each distribution center need to forecast future demand in order to establish a reasonable inventory policy. Therefore, the number of forecasting models corresponds to the number of distribution centers, which is not possible to estimate that kind of huge number of accurate models in ERP (Enterprise Resource Planning)module. Multilayer neural net as universal approximation is employed for fitting the prediction model. In order to improve prediction accuracy, a sequential simulation procedure is performed to get appropriate network structure and also to improve forecasting accuracy. The proposed simulation procedure includes neural structure identification and virtual predictive model generation. The predictive model generation consists of generating virtual signals and estimating predictive model. The virtual predictive model plays a key role in tuning the real model by absorbing the real model errors. The complement approach, based on real and virtual model, could forecast the future demands of various distribution centers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.239-240
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• 2015

This study aims to clarify current situation of transportation flow and determine to demand forecasting in new route between Korea and Mongolia. By follows the mongolia government information, there will be open 5 new routes in nearest 10 years. Thus, this study purpose is determining the efficiency way by consider time and cost factors for new route between Korea and Mongolia.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.491-493
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• 1995

In this paper, the method of long-term load forecasting using a fuzzy neural network of which input is a fuzzy membership function value of a input variable like as GNP which is considered to affect demand of load. The proposed method was applicated in Korea Electric Power Corporation (KEPCO). The comparison with Error Back-Propagation Neural Network has been shown.

• IE interfaces
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• v.16 no.spc
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• pp.28-32
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• 2003

In this paper, the fuzzy group method data handling-type(GMDH) neural networks and their application to the forecasting of mobile communication system are described. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to neural networks, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of neuro-fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the neuro-fuzzy GMDH. The GMDH-type neural networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the neuro-fuzzy GMDH. The computer program is developed and successful applications are shown in the field of estimating problem of mobile communication with the number of factors considered.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.259-260
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• 2023

The rapid urbanization and modernization observed in countries like Myanmar have led to significant concerns regarding traffic congestion, especially in urban areas. This study focuses on the analysis and revitalization of urban transport in selected areas of Myanmar. The core of urban transportation planning lies in travel forecasting, which employs models to predict future traffic patterns and guide decisions related to road capacity, transit services, and land use policies. Travel demand modeling involves a series of mathematical models that simulate traveler behavior and decision-making within a transportation system, including highways, transit options, and policies. The paper offers an overview of the traditional four-step transportation modeling system, utilizing a simplified transport network in the context of Mandalay City, Myanmar.