• Management Science and Financial Engineering
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• v.11 no.3
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• pp.125-136
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• 2005

As the importance of time-based competition is increasing, information systems for supporting the immediate decision making is strongly required. Especially high -tech product firms are under extreme pressure of rapid response to the demand side due to relatively short life cycle of the product. Therefore, the objective of our research is proposing a framework of estimating demand curve based on e-auction data, which is extremely easy to access and well reflect the limited demand curve in that channel. Firstly, we identify the advantages of using e-auction data for full demand curve estimation and then verify it using Agent-Eased-Modeling and Tobin's censored regression model.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1427-1434
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• 2017

Since the late 2000s, there has been growing preparation in South Korea for a sudden reunification of South and North Korea. Particularly in the power industry field, thorough preparations for the construction of a power infrastructure after reunification are necessary. The first step is to estimate the peak load demand. In this paper, we suggest a new peak demand estimation methodology by integrating existing correlation analysis methods between economic indicators and power generation quantities with a power supply interruption model in consideration of power consumption patterns. Through this, the potential peak demand and actual peak demand of the Nation, which experiences power supply interruption can be estimated. For case studies on North Korea after reunification, the potential peak demand in 2015 was estimated at 5,189 MW, while the actual peak demand within the same year was recorded as 2,461 MW. The estimated potential peak demand can be utilized as an important factor when planning the construction of power system facilities in preparation for reunification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.3
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• pp.639-645
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• 2007

In this study, an estimation of call demand for the internet telephony was carried out using the monthly time-series data from June 2001 to December 2004. In the estimation, the call traffic was assumed to be explained by tariff of the internet telephony service, tariff of fixed and wireless services, income, quality of service, and lagged traffic variable. The traffic is assumed to follow the partial adjustment mechanism. The estimation result shows that the call traffic demand is elastic to the tariff of the service while it is inelastic to the change of income. The qualisty of service is regarded as an important factor of demand. Also there appeared the call demand is adjusting to the change of explanatory variables with some lags.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.673-678
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• 2012

Accurate demand estimating process in the construction of urban railway is very important, and precise validation is required. Existing model formula in the 4 phase model is limited in the estimation of the demand the administrative boundary-based zone system reflects no spatial railway demand characteristics around railway stations. The purpose of this study is improving the accuracy of urban rail demand estimation through segmentations of station influence areas and modal split characteristics within the areas. According to the case analysis, it is possible to set up the ststion influence area with a radius of 500m in the urban region and 1,000m in the suburban. And eastablishing proper segmentations of the ststion influence area shows more accurate results to the real demand of railway stations.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1407-1415
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• 2008

Recently, there has been growing interest in Gyongin Line demand estimation, because SMRT(Seoul Metropolitan Rapid Transit Corporation) Line 7 will be extended west by 9.8km from Onsu to meet the Gyongin Line at Bupyeong-gu Office. This study was designed to estimate the change of traffic demand in Gyongin Line before and after the completion year 2011, also provide against the economic loss caused by the derived demand from Gyongin Line to SMRT Line 7. In this paper we give preliminary results for the strategic plans for traffic demand estimation and train operating plan in Gyongin Line by analyzing transport performance of Seoul metropolitan subway, and an additional study on the transportation market and the urban development plan is needed for more accurate results.

• ETRI Journal
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• v.7 no.3
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• pp.35-41
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• 1985

The objective of this study is to estimate the price elasticity of demand for telephone use in Korea. This paper provides a comprehensive review of the existing empirical study and establishes the econometric demand estimation model applicable to Korean telephone service. By this model, the price elasticities of demand in local and toll uses are estimated and their implications are discussed.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.11-22
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• 2007

This paper is to discuss analytical techniques to estimate demand sizes and volumes that determine optimal locations for multiple facilities for a given services. While demand size estimation is a core part of location modeling to enhance solution quality and practical applicability, the estimation method has been used in limited and restrict parts such as a single population centroid in a given larger census boundary area or small theoretical application experiments(e.s. census track and enumeration district). Therefore, this paper strives to develop an analytical estimation method of demand size that converts area based demand data to point based population weighted centroids. This method is free to spatial boundary units and more robust to estimate accurate demand volumes regardless of geographic boundaries. To improve the estimation accuracy, this paper uses house weighted value to the population centroid calculation process. Then the population weighted centroids are converted to individual demand points on a grid formated surface area. In turn, the population weighted centroids, demand points and network distance measures are operated into location-allocation models to examine their roles to enhance solution quality and applicability of GIS location models. Finally, this paper demonstrates the robustness of the weighted estimation method with the application of location-allocation models.

• Journal of Korean Society of Transportation
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• v.19 no.1
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• pp.77-88
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• 2001

Until now, though most of the studies related to demand estimation method using traffic counts use methods based on singleclass, travel demands or flows are made by mixing various vehicles in real networks. In general, existing demand estimation methods based on traffic counts estimate O/D by converting a multiclass O/D matrix and traffic counts into a singleclass O/D matrix and traffic counts through PCE conversion, and analyze a O/D matrix by dividing into a multiclass O/D matrix and traffic counts after multiplying an estimated O/D matrix by the fixed ratio of a singleclass O/D matrix and traffic counts before PCE conversion. However, the merits of a demand estimation method based on multiclass calculate each route choice ratio about multiclass O/D, and maximize the estimation capability of multiclass by calculating each gradient, the reduction direction of objective function. Therefore, this study aims to establish a demand estimation method which considers congestion between vehicle and vehicle by using multiclass instead of singleclass.

• Journal of Biosystems Engineering
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• v.29 no.5 s.106
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• pp.441-450
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• 2004

This study was performed to estimate mid and long term demands of a tractor, a rice transplanter, a combine and a grain dryer by using logistic curve function and Markov chain model. Field survey was done to decide some parameters far logistic curve function and Markov chain model. Ceiling values of tractor and combine fer logistic curve function analysis were 209,280 and 85,607 respectively. Based on logistic curve function analysis, total number of tractors increased slightly during the period analysed. New demand for combine was found to be zero. Markov chain analysis was carried out with 2 scenarios. With the scenario 1(rice price $10\%$ down and current supporting policy by government), new demand for tractor was decreased gradually up to 700 unit in the year 2012. For combine, new demand was zero. Regardless of scenarios, the replacement demand was increased slightly after 2003. After then, the replacement demand is decreased after the certain time. Two analysis of logistic owe function and Markov chain model showed the similar trend in increase and decrease for total number of tractors and combines. However, the difference in numbers of tractors and combines between the results from 2 analysis got bigger as the time passed.