• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.421-434
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• 2022

Fine particulate matter (PM2.5) has been the biggest environmental problem in Korea since the 2010s. The present study considers the value of urban forests and green infrastructure as an ecosystem service (ES) concept for PM2.5 reduction based on satellite and spatial data, with a focus on Seoul, Korea A method for the spatial ES assessment that considers social demand variables such as population and land price is suggested. First, an ES assessment based on natural environment information confirms that, while the vitality of vegetation is relatively low, the ES is high in the city center and residential areas, where the concentration of PM2.5 is high. Then, the ES assessment considering social demand (i.e., the ESS) confirms the existence of higher PM2.5 values in residential areas with high population density, and in main downtown areas. This is because the ESS of urban green infrastructure is high in areas with high land prices, high population density, and above-average PM2.5 concentrations. Further, when a future green infrastructure improvement scenario that considers the urban forest management plan is applied, the area of very high ESS is increased by 74% when the vegetation greenness of the green infrastructure in the residential area is increased by only 20%. This result suggests that green infrastructure and urban forests in the residential area should be continuously expanded and managed in order to maximize the PM2.5 reduction ES.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.12-23
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• 2003

The expansion of internet and the development of communication technology have brought about an explosive increasement of data. Further progress has led to the increasing demand for efficient and effective data analysis tools. According to this demand, data mining techniques have been developed to find out knowledge from a huge amounts of raw data. This paper suggests a generalization based classification method which explores rules from real world data appearing repeatedly. Also, it analyzed the relation between weather data and forest fire, and efficiently predicted through it as a prediction model by applying the suggested generalization based classification method to forest fire data. Additionally, the proposed method can be utilized variously in the important field of real life like the analysis and prediction on natural disaster occurring repeatedly, the prediction of energy demand and so forth.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.658-663
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.561-565
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• 2006

The paper presents a method of assessing the adequate tapaclty of photovoltaic generation systems for public buildings based on analysis of load variation patterns of customers. When PV systems are installed for supplying power for the customer load, reverse power relay is required by the guideline to be installed at the point of common coupling with the power network. The suggested method analyzes daily, weekly and monthly load demand of the customer that Irishes PV system installation, and determines the appropriate rating of the PV system for preventing PV generation from exceeding the customer demand. This work is expected to support renewable energy dissemination projects of public organizations.

• IE interfaces
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• v.18 no.3
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.1-13
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• 2002

We study a multi-component production/inventory system in which individual components are made to meet various demand types. We assume that the demands arrive according to a Poisson process, but there is a fixed probability that a demand requests a particular kit of different components. Each component is produced by a flow line with several stations. The production of each component is operated by the CONWIP control mechanism. To analyse this system, we propose an approximation method based on aggregation method. In application of the aggregation method, a product-form approximation technique as well as a matrix-geometric method is used. Comparisons with simulation show that the approximation method provides fairly good results.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.3
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• pp.15-23
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• 2021

Personal Air Vehicle is expected to be a promising solution to relieve traffic congestion using urban airspace. The development of related technologies such as materials or batteries has been accelerated. In addition, commercial transportation services are being prepared. When fierce competition begins in the PAV market, even technologically superior products will disappear without choices by consumers. Therefore, demand factors should be reflected in PAV development to enhance competitiveness. In the paper, values were estimated for the major technological attributes of PAV. Stated preference data were collected through a survey, and the conjoint method and ordered probit model were adopted. Thereafter, it was confirmed that the value would be high in the order of dual mode, drone-type appearance, and noise reduction. Some R&D strategies were proposed based on this.

• Journal of Electrochemical Science and Technology
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• v.14 no.3
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• pp.215-221
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• 2023

A rapid and environment-friendly electrochemical sensor to determine the chemical oxygen demand (COD) has been developed. The boron-doped diamond (BDD) thin-film electrode is employed as the anode, which fully oxidizes organic pollutants and provides a current response in proportion to the COD values of the sample solution. The BDD-based amperometric COD sensor is optimized in terms of the applied potential and the solution pH. At the optimized conditions, the COD sensor exhibits a linear range of 0 to 80 mg/L and the detection limit of 1.1 mg/L. Using a set of model organic compounds, the electrochemical COD sensor is compared with the conventional dichromate COD method. The result shows an excellent correlation between the two methods.

• The Journal of Economics, Marketing and Management
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• v.12 no.3
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• pp.23-33
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• 2024

This study aims to enhance the accuracy of effective demand analysis for publicly supported private rental housing by integrating the RIR into the traditional Mankiw-Weil (MW) model. Traditional models like the M-W model, which account for household income, housing costs, and household size, often fall short in estimating demand driven by large-scale development projects. By integrating the RIR factor, this study introduces a more accurate and practical approach to analyzing effective housing demand. Findings show that the modified M-W model incorporating RIR predicts effective demand with greater precision than traditional methods. This advancement allows developers to plan projects more efficiently and aids governments and local authorities in implementing more effective housing policies. Furthermore, the study assesses the real housing cost burden on households, elucidating their capacity to pay housing costs based on household size and income quintile. This information enables policymakers to design targeted housing support policies for specific demographic groups. Additionally, the research provides comprehensive policy recommendations tailored to various regions and housing types. Overall, this study lays a vital groundwork for the long-term analysis of the effects of economic changes and housing market trends on effective demand.