• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.32-39
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• 1999

Video-On-Demand servers have to provide timely processing guarantees and reduce the storage and reduce the storage and bandwidth requirements for continuous media However, compression techniques used in Video-On-Demand servers make the bit rates of compressed video data significantly variable from frame to frame Consequently, most pervious Video-On-Demand servers which use constant bit rate retrieval to guarantee deterministic service under-utilize the system resources and restrict the number of clients. In this paper, I propose a buffer management scheme called CAP(Caching And Prefetching) for Video-On-Demand server using variable bit rate continuous media. By caching and prefetching the data CAP reduces the variation of the compressed data and increases the number of clients simultaneously served and maximizes the utilization of system resources. Results of trace-driven simulations show the effectiveness of the scheme.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.10
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• pp.389-398
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• 1985

Recently maximum power demand of our country has become to be under the great in fluence of electric cooling and air conditioning demand which are sensitive to weather conditions. This paper presents the technique and algorithm to forecast the long-term maximum power demand considering the characteristics of electric power and weather variable. By introducing a weather load model for forecasting long-term maximum power demand with the recent statistic data of power demand, annual maximum power demand is separated into two parts such as the base load component, affected little by weather, and the weather sensitive load component by means of multi-regression analysis method. And we derive the growth trend regression equations of above two components and their individual coefficients, the maximum power demand of each forecasting year can be forecasted with the sum of above two components. In this case we use the coincident dry bulb temperature as the weather variable at the occurence of one-day maximum power demand. As the growth trend regression equation we choose an exponential trend curve for the base load component, and real quadratic curve for the weather sensitive load component. The validity of the forecasting technique and algorithm proposed in this paper is proved by the case study for the present Korean power system.

• Journal of Korean Society of Forest Science
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• v.97 no.2
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• pp.140-143
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• 2008

This study compared the plywood demand prediction accuracy of econometric and vector autoregressive models using Korean data. The econometric model of plywood demand was specified with three explanatory variables; own price, construction permit area, dummy. The vector autoregressive model was specified with lagged endogenous variable, own price, construction permit area and dummy. The dummy variable reflected the abrupt decrease in plywood consumption in the late 1990's. The prediction accuracy was estimated on the basis of Residual Mean Squared Error, Mean Absolute Percentage Error and Theil's Inequality Coefficient. The results showed that the plywood demand prediction can be performed more accurately by econometric model than by vector autoregressive model.

• Korean Journal of Agricultural Science
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• v.25 no.1
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• pp.41-51
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• 1998

This study not only carried out to grasp about the sununarized characteristics of the relationship between international timber market and production trend of wood products, but also focused on the analysis of korean wood demand and the long-term forecast with econometric analysis. The result of regression analysis for wood demand in Korea is that coniferous roundwood demand(CIWD) is explained by coniferous foreign roundwood price(CWRI), Gross domestic product(GDP), a dummy variable. Non-coniferous roundwood demand(NCIWD)is explained by non-coniferous roundwood price(NCWRI), coniferous roundwood price(CWRI), a dummy variable. As the result of long-term forecast by base case, the total roundwood demand was forecasted $11,107,000m^3$ in the year 2000, $11,781,000m^3$ in 2005, $12,565,000m^3$ in 2010. As the result of scenario 1, total roundwood demand was forecasted $11,027,000m^3$ in 2000, $11,435,000m^3$ in 2005, $11,952,000m^3$ in 2010. And as the result by scenario 2, total roundwood demand was forecasted $11,341,000m^3$ in 2000, $12,208,000m^3$ in 2005 $13,257,000m^3$ in 2010.

• The Journal of Fisheries Business Administration
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• v.54 no.2
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• pp.31-44
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• 2023

This study examines the demand system of shrimp imported from top four countries and domestically produced by using AIDS (Almost Ideal Demand System) model. Top four import countries are Vietnam, Ecuador, China, and Malaysia based on the value of imports in 2021. As results of the analysis, the demand system of shrimp turn out to be below. First, the relationship of domestic shrimp and imported shrimp (Ecuadorian and Vietnamese) is identified as complements or substitutes depending on whether the income effect is considered. This result implies that imported shrimp supplements domestic supply against excess demand while homogeneous shrimp products competes with domestic shrimp in fish market. Second, the relationship among imported shrimps turned out to be both substitutes and complements. Especially, the Vietnamese shrimp is complementary with Chinese and Malaysian shrimp, but substitutes of Ecuadorian. It is assumed that adjoining Asian countries shares similar shrimp species and processing system which differentiates from Ecuadorian. Finally, the study included quarter as dummy variable and GDP as instrumental variable of expenditure in the model. The result confirmed that domestic shrimp is highly on demand during the main production season while imported shrimp is mainly demanded during the rest of the season.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.147-156
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• 2009

We present a methodology for modeling and solving the transit frequency design problem with variable demand. The problem is described as a bi-level model based on a non-cooperative Stackelberg game. The upper-level operator problem is formulated as a non-linear optimization model to minimize net cost, which includes operating cost, travel cost and revenue, with fleet size and frequency constraints. The lower-level user problem is formulated as a capacity-constrained stochastic user equilibrium assignment model with variable demand, considering transfer delay between transit lines. An efficient algorithm is also presented for solving the proposed model. The upper-level model is solved by a gradient projection method, and the lower-level model is solved by an existing iterative balancing method. An application of the proposed model and algorithm is presented using a small test network. The results of this application show that the proposed algorithm converges well to an optimal point. The methodology of this study is expected to contribute to form a theoretical basis for diagnosing the problems of current transit systems and for improving its operational efficiency to increase the demand as well as the level of service.

• Journal of Biosystems Engineering
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• v.31 no.3 s.116
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• pp.194-202
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• 2006

Using a multi-variable linear regression technique and SUR(seemingly unrelated regression) model, the demand functions of tractor, combine and rice transplanter were estimated. The demand was regarded as an annual supply of each machine and modeled as a function of 11 independent variables which reflect the actual farmer's income, actual prices of farm machines, previous supply, previous stock, actual amount of available subsidy, actual amount of available loan, arable land, import of farm machines and rice price. The actual amount of available loan affects most significantly the demand functions. The actual farmer's income, actual farmer's asset, loan coverage, and rice price affect the demand positively while prices of farm machines and import negatively. The annual demands of tractor, combine and rice transplanter estimated using the demand functions were also presented over the next 4 years.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.1-10
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• 2004

Video-On-Demand servers use compression techniques to reduce the storage and bandwidth requirements. The compression techniques make the bit rates of compressed video data significantly variable from frame to frame. Consequently, Video-On-Demand servers with a constant bit rate retrieval can not maximize the utilization of resources. It is possible that when variable bit rate video data is stored, accurate description of the bit rate changes could be computed a priori. In this paper, I propose a buffer management scheme called MAX for Video-On-Demand server using variable bit rate continuous media. By caching and prefetching the data, MAX buffer management scheme reduces the variation of the compressed data and increases the number of clients simultaneously served and maximizes the utilization of system resources. Results of trace-driven simulations show the effectiveness of the scheme.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.148-155
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• 2009

The objective of this study is to know market structures of softwood logs being imported to South Korea from log producing countries. Import demand of softwood logs imported to South Korea from America, New Zealand and Chile is fixed as a function of log prices, the lagged dependent variable and output. On the basis of the adaptive expectations model, linear regression models that the explanatory variables included and the lagged dependent variable were estimated by Seemingly Unrelated Regression Equations (SURE). The short-run and long-run own price elasticity of America's softwood log import demand is -1.738 and -4.250 respectively. Then long-run elasticity is much higher than short-run elasticity. Short-run and long-run crosselasticity of New Zealand's softwood log import demand with respect to American's softwood log import price are inelastic at 0.505 and 0.883 respectively. Short-run and long-run cross-elasticity of Chile's softwood log import demands with respect to American's softwood log import prices were highly elastic at 2.442 and 4.462 respectively. Long-run elasticity was almost twice as high as short-run elasticity.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.