• Korean Management Science Review
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• v.27 no.2
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• pp.31-44
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• 2010

It is very important to forecast demand and determine the optimal procurement quantities of spare parts. The Army has been forecasting demand not with actual usage of spare parts but with request quantities. However, the Army could not purchase all of forecasted demand quantities due to budget limit. Thus, the procurement quantities depend on the item managers' intuition and their meetings. The system currently used contains many problems. This study suggests a new determination procedure; 1) forecasting demand method based on actual usage, 2) determining procurement method through LP model with budge and other constraints. The newly determined quantities of spare parts is verified in the simulation model, that represents the real operational and maintenance situation to measure the operational availability. The result shows that the new forecasting method with actual usage improves the operational availability. Also, the procurement determination with LP improves the operational availability as well.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.139-151
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• 2000

Developing a new commercial product, it is need to connect the end users demand of quality to the industrial technology of company. For this reason, this study is to build up the users demand for the imminent marketing product of a certain company by Analytic Hierarchy Process, analyze quantitatively users subjective thoughts collected by Group Consensus, calculate the added-value of users demands and verify the consistency of users opinions by consistency-exponential-calculation. The added value obtained by this method is substituted into a user-demand item of Quality Function Deployment. And, the technical characteristic data transferred from the extracted essential factor for developing and manufacturing a new product is substituted into a technical characteristic item of QFD. The faculty of quality is firstly finished by this procedure. But, because the relation a technical characterization with users demand do not be known in new product, Wassermans method was introduced for the correlation users demand with technology and for the processing and marketing of a new product. The all assumption on this thesis was based on the reliable real data of a certain company.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1695-1699
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• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.126-133
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• 2007

This paper proposes information technology based data mining to forecast short term power demand. A time-series analyses have been applied to power demand forecasting, but this method needs not only heavy computational calculation but also large amount of coefficient data. Therefore, it is hard to analyze data in fast way. To overcome time consuming process, the author take advantage of universally easily available information technology based data-mining technique to analyze patterns of days and special days(holidays, etc.). This technique consists of two steps, one is constructing decision tree, the other is estimating and forecasting power flow using decision tree analysis. To validate the efficiency, the author compares the estimated demand with real demand from the Korea Power Exchange.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.59-66
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• 2020

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.225-231
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• 2016

Peak load rate(i.e., maximum daily flow/average daily flow) has not been considered for industrial water demand planning in Korea to date, while area unit method based on average daily flow has been applied to decide capacity of industrial water treatment plants(WTPs). Designers of industrial WTPs has assumed that peak load would not exist if operation rate of factories in industrial sites were close to 100%. However, peak load rates were calculated as 1.10~2.53 based on daily water flow from 2009 to 2014 for 9 industrial WTPs which have been operated more than 9 years(9-38 years). Furthermore, average operation rates of 9 industrial WTPs was less than 70% which means current area unit method has tendency to overestimate water demand. Therefore, it is not reasonable to consider peak load for the calculation of water demand under current area unit method application to prevent overestimation. However, for the precise future industrial water demand calculation more precise data gathering for average daily flow and consideration of peak load rate are recommended.

• Journal of Korean Society of Rural Planning
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• v.14 no.4
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• pp.109-119
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• 2008

The objectives of this study are to develop a green tourism potential evaluation method with rural amenity and demand of citizen. The new index which was named GPD(green tourism potential degree) is designed to propose the green tourism potential of rural areas using spatial analysis of geographic information system and spatial interaction of gravity model. And in order to evaluate the green tourism potential with supply side and demand side, two indices were defined; One is green tourism demand degree(GDD) which is developed to quantify a demand side potential by the analysis of urban population and urbanization index, and the other is green tourism attraction degree(GAD) which is developed to quantify a supply side potential by the analysis of rural amenity values using AHP algorithm, based on opinion of related experts. The developed method was applied to a part of Kyounggi province, Seoul and Incheon. All the study area's GAD, GDD and GPD were assessed and the proposed green tourism potential evaluation method could be used in developing rural development plans and green tourism policies considering spatial interaction with citizen and green tourism resources.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1304-1310
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• 2015

In remote islands, due to expense of existing generation systems, installation of photovoltaic cells (PVs) and wind turbines has a chance of reducing generation costs. However, in island power systems, even short-term power fluctuations change the frequency of grids because of their small inertia constant. In order to compensate power fluctuations, the authors proposed the power consumption control of pumps which send water to tanks. The power control doesn’t affect water users’ convenience as long as tanks hold water. Based on experimental characteristics of a pump system, this paper shows methods to determine reference power consumption of the system with compensation for short-term PV fluctuations while satisfying water demand. One method uses a PI controller and the other method calculates reference power consumption from water flow reference. Simulations with a PV and a pump system are carried out to find optimum parameters and to compare the methods. Results show that both PI control method and water flow calculation method are useful for satisfying the water demand constraint. The water demand constraint has a little impact to suppression of the short-term power fluctuation in this condition.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.2
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• pp.86-96
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• 2010

The paper describes development and evaluation of a simple method for determining gradient of modified linear setpoint variation to reduce peak electrical cooling demand in buildings using building precooling and setpoint adjustment. The method is an approximated approach for minimizing electrical cooling demand during occupied period in buildings and involves modified linear adjustment of cooling setpoint temperature between $26^{\circ}C$ and $28^{\circ}C$. The gradient of linear variation or final time of linear increase is determined based on the cooling load shape in conventional cooling control having a constant setpoint temperature. The potential to reduce peak cooling demand using the simple method was evaluated through building simulation for a calibrated office building model considering four different weather conditions. The simple method showed about 30% and 20% in terms of reducing peak cooling demand and chiller power consumption, respectively, compared to the conventional control.