• Journal of the Korean Regional Science Association
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• v.6 no.1
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• pp.39-55
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• 1990

The principal measure of housing demand is income and the preferences expressed by households through their respective indifference curves. In this context, housing essentially becomes a derived demand, i.e., the household consumes land and a location (or distance-in time and money costs), according to its relative preferences for space, accessibility, and all other nonhousing goods. This paper attempts to deal with both aspects of housing (apartment) demand and household mobility in the Seoul Metropolitan Areas. Housing services will be measured using hedonic regression technique. From observations on the market prices of dwelling units and on the underlying characteristics of housing, one can estimte the relationships between the two empirically. In predicting the probability of the future moves into new towns in the Seoul Metropolitan areas, the best predictors of the future moves into new best predictors are found to be the degree of satisfaction not only with the current residence as a whole, but with some of the major amenities, accessibility and child education. The reasons for moving into new towns are diverse depending on the households' current situation; the most frequently cited is "improvement of housing conditions," followed by "improvement of living environment," "asset improvement" and "home ownership". It appears that people move houses because of a dissatisfaction with their current housing status, relative their income or needs, or a desire to improve their housing and neighborhood amenities, or both. On the other hand, it is clear that the development of new towns in the Seoul Metropolitan Areas should be based on the analysis of housing demand and the pattern of household mobility in Seoul housing market.sehold mobility in Seoul housing market.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.9-14
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• 2018

For an efficient management of electricity market and power systems, accurate forecasts for electricity demand are essential. Since there are many factors, either known or unknown, determining the realized loads, it is difficult to forecast the demands with the past time series only. In this paper we perform a cluster analysis on electricity demand data collected from Jan. 2000 to Dec. 2017. Our purpose of clustering on electricity demand data is that each cluster is expected to consist of data whose latent variables are same or similar values. Then, if properly clustered, it is possible to develop an accurate forecasting model for each cluster separately. To validate the feasibility of this approach for building better forecasting models, we clustered data with t-SNE. To apply t-SNE to time series data effectively, we adopt the dynamic time warping as a similarity measure. From the result of experiments, we found that several clusters are well observed and each cluster can be interpreted as a mix of well-known factors such as trends, seasonality and holiday effects and other unknown factors. These findings can motivate the approaches which build forecasting models with respect to each cluster independently.

• Journal of Korea Technology Innovation Society
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• v.3 no.1
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• pp.113-126
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• 2000

The purpose of this paper is to introduce and test an extension of dichotomous choice contingent valuation(DC-CV) methods of pre-test-market evaluation using the specific case of a cable telev-ision service. Specifically we used a double-bounded DC-CV approach. On the whole respondents accepted the contingent market and were willing to contribute a significant amount on average. This willingness varies according to individual characteristics and concerns about the service. It is concluded that the methods are potentially a useful tool for decision-makers in measuring the po-tential demand for a new cable television service and considering the provision of the service.

• Culinary science and hospitality research
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• v.5 no.1
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• pp.89-101
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• 1999

This study attempts to identify the most accurate quantitative forecasting technique for measuring the future level of demand for food ＆ beverage in super deluxe hotel in Seoul, which will subsequently lead to determining the optimal level of purchasing food ＆ beverage. This study, in detail, examines the food purchasing system of H hotel, reviews three rigorous univariate time series models and identify the most accurate forecasting technique. The monthly data ranging from January 1990 to December 1997 (96 observations) were used for the empirical analysis and the 1998 data were left for the comparison with the ex post forecast results. In order to measure the accuracy, MAPE, MAD and RMSE were used as criteria. In this study, Box-Jenkins model was turned out to be the most accurate technique for forecasting hotel food ＆ beverage demand among selected models generating 3.8％ forecast error in average.

• KDI Journal of Economic Policy
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• v.43 no.1
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• pp.29-52
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• 2021

The purpose of this paper is to measure the impact of the recent Korea-Japan trade dispute on the Korean economy using supply-driven input-output analysis. In July 2019, Japan announced the decision to tighten the export control of three materials which are indispensable in the manufacturing of semiconductors and electronic display panels. Japan's decision directly affects production in Korea's semiconductor and display sectors and is hence not a demand shock. For this reason, a standard demand-driven input-output analysis is not valid despite the fact that it can still be applied. The impact of Japan's decision on Korea's aggregate and individual sectors' gross output, GDP and employment were computed using both methods.

• Korean Management Science Review
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• v.22 no.1
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• pp.179-197
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• 2005

This study examines market acceptance for DMB service, one of the touted new business models in Korea's next-generation mobile communications service market, using adoption end diffusion of innovation as the theoretical framework. Market acceptance for DMB service was assessed by predicting the demand for the service using the Bass model, and the demand variability over time was then analyzed by integrating the innovation adoption model proposed by Rogers (2003). In our estimation of the Bass model, we derived the coefficient of innovation and coefficient of imitation, using actual diffusion data from the mobile telephone service market. The maximum number of subscribers was estimated based on the result of a survey on satellite DMB service. Furthermore, to test the difference in diffusion pattern between mobile phone service and satellite DMB service, we reorganized the demand data along the diffusion timeline according to Rogers' innovation adoption model, using the responses by survey subjects concerning their respective projected time of adoption. The comparison of the two demand prediction models revealed that diffusion for both took place forming a classical S-curve. Concerning variability in demand for DMB service, our findings, much in agreement with Rogers' view, indicated that demand was highly variable over time and depending on the adopter group. In distinguishing adopters into different groups by time of adoption of innovation, we found that income and lifestyle (opinion leadership, novelty seeking tendency and independent decision-making) were variables with measurable impact. Among the managerial variables, price of reception device, contents type, subscription fees were the variables resulting in statistically significant differences. This study, as an attempt to measure the market acceptance for satellite DMB service, a leading next-generation mobile communications service product, stands out from related studies in that it estimates the nature and level of acceptance for specific customer categories, using theories of innovation adoption and diffusion and based on the result of a survey conducted through one-to-one interviews. The authors of this paper believe that the theoretical framework elaborated in this study and its findings can be fruitfully reused in future attempts to predict demand for new mobile communications service products.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.440-448
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• 2022

Recently, due to the introduction of distributed energy resources, the optimal resource allocation problem of the power network is more and more important, and the distributed resource allocation method is required to process huge amount of data in large-scale power networks. In the optimal resource allocation problem, many studies have been conducted on the case when the supply-demand balance is satisfied due to the limitation of the generation capacity of each generator, but the studies considering the supply-demand imbalance, that total demand exceeds the maximum generation capacity, have rarely been considered. In this paper, we propose the consensus-based distributed algorithm for the optimal resource allocation of power network considering the supply-demand imbalance condition as well as the supply-demand balance condition. The proposed distributed algorithm is designed to allocate the optimal resources when the supply-demand balance condition is satisfied, and to measure the amount of required resources when the supply-demand is imbalanced. Finally, we conduct the simulations to verify the performance of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.350-353
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• 2009

The shielding enclosure is very essential device to test the electromagnetic wave power generated by various RF equipments. Some standards for the shielding enclosures were established to test them in right method. Generally, There are IEEE-STD-299 and MIL-STD-285 and NSA-65-6 of the method for measuring the effectiveness of shielding enclosures, the IEEE-STD-299 combined MIL-STD-285 and NSA-65-6 about the method for measuring shielding effectiveness(SE) about 1969 years, but, the measurement point of 299 proposal is many points(including shielding wall, seam, coner beat, shielding door, etc) and demand long time of measurement. To improve SE test method for shielding enclosures was studied and suggested to develop a proper test procedure. First, we measure reference level as frequency range and H/V polarization, secondly, measure leakage point, and finally, measure shield effect and calculate SE. Our method has a merit of the less measurement point than IEEE-STD-299, and shorten time than 299, and define representation SE of shielding enclosure effectively.

• Korean Journal of Childcare and Education
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• v.11 no.4
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• pp.147-166
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• 2015

The purpose of this study was to investigate the effects of maternal psychological control and the disparity between children's temperament and mothers' demand level on children's externalizing and internalizing behavior problems. The participants of this study were 221 children aged 4 to 5 and their mothers from three kindergartens located in Busan and Gyeongsangnam-do. To measure children's behavior problems, the Preschool and Kindergarten Behavior Scales were used. Maternal psychological control was assessed by the Psychological Control Scale-Youth Self-Report. The Revised Dimension of Temperament Survey and DOTS-R: ethnotheory was used, to measure the disparity between children's temperament and mothers' demand level. The data were analyzed by means of descriptive statistics, t-test and hierarchical regression analysis. The results of this study were as follows. First, both for boys and girls, the disparity between children's activity temperament and mothers' demand level had a negative effect on externalizing behavior problems. However, maternal psychological control had no significant effect on externalizing behavior problems both for boys and girls. Second, for boys, the high level of maternal psychological control and the disparity between children's adaptive temperament and mothers' demand level had a negative effect on internalizing behavior problems. However, for girls, such effects were not significant.