• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• International Journal of Highway Engineering
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• v.15 no.3
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• pp.177-189
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• 2013

PURPOSES: This study aims to suggest a proper left-turn treatment method for the bicycle traffic flow at four-legged intersections. METHODS: Four types of crossing methods are proposed and analyzed : (1) indirect left turn, (2) direct left turn, (3) direct left turn on a Bike Box, and (4) direct left turn on bike left turn lane. The VISSIM simulation tests were conducted based on forty-eight operation scenarios prepared by varying vehicle and bicycle traffic volumes. RESULTS : The results from the four-legged signalized intersections suggest that (1) the indirect left turn is appropriate when vehicle demand is high, (2) the direct left turn is efficient on most traffic situation but the safety is a concern, (3) the direct left turn on a Bike Box is appropriate when bicycle demand is high while vehicle demand is not, and (4) the direct left turn on a bike left turn lane is appropriate when both vehicle and bicycle demand are low. CONCLUSIONS : The direct left turn of bicycle provides more efficiency than the indirect left turn at the four-legged intersections but to apply the methods and to study more, advanced evaluation methods, related law, and insurance programs are needed.

• Journal of The Korea Institute of Healthcare Architecture
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• v.23 no.1
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• pp.7-14
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• 2017

Purpose: Since the large hospitals are one of the most intensive energy users among building types in Korea, it is important to investigate and apply appropriate energy conservation measures. There are many researches on energy conservation measures for HVAC system in hospitals, but only few useful guidelines for envelope design variables were existed. The building envelope is one of the important factors to building energy consumption and patients' comfort. The purpose of this study is to suggest the most influential envelope design variables for each end-use energy demand. Methods: 100 samples were generated by LHS(Latin Hypercube Sampling) method. After energy performance simulation, global sensitivity analysis was performed by the regression method. DesignBuilder, Simlab 2.2 and JEPlus were used in this process. Results: The most influencing variables are SHGC, SHGC and VT for heating, cooling, and lighting, respectively. However, the most influencing variable for total energy demand is WWR(Window to Wall Ratio). The analysis was conducted based on the coefficient of variance results. Implications: The six envelop design variables were ranked according to the end-use energy demand.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.313-321
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• 2017

The emergence of E-book have allowed readers to interact with other readers and to actively participate (e.g. social reading). Furthermore, there is a growing demand in writer's community to take the advantage of the feedback from their readers to update the content of E-book. To do that, they require the service that utilizes the user feedback while creating or updating the e-book content. This study aims to let authors collect and to apply the reader's feedback on E-book content. However, in order to apply the user feedback, users first need to explicitly type the feedback, and even if they do, authors need to develop the software to automatically analyze and to apply the user feedback. This makes difficult for authors without programming background to produce E-book with automatic content adaptation. In this paper, we propose Feedback Clue Model to generate, analyze and apply the user feedback into E-book content. Based on this model, we develop the block editor which allows easy implementation of E-book that can be dynamically updated.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.37-44
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• 2016

The demand for electricity has a considerable impact on various energy sectors since electricity is generated from various energy sources. This paper attempts to estimate the electricity demand function and obtain some quantitative information on price and income elasticities of the demand. To this end, we apply a lagged dependent variable model to derive long-run as well as short-run elasticities using the time-series data over the period 1991-2014. Our dependent variable is annual electricity demand. The independent variables include constant term, real price of electricity, and real gross domestic product. The results show that the short-run price and income elasticities of the electricity demand are estimated to be -0.142 and 0.866, respectively. They are statistically significant at the 5% level. That is, the electricity demand is in-elastic with respect to price and income changes in the short-run. The long-run price and income elasticities of the electricity demand are calculated to be -0.210 and 1.287, respectively, which are also statistically meaningful at the 5% level. The electricity demand is still in-elastic with regard to price change in the long-run. However, the electricity demand is elastic regarding income change in the long-run. Therefore, this indicates that the effect of demand-side management policy through price-control is restrictive in both the short- and long-run. The growth in electricity demand following income growth is expected to be more remarkable in the long-run than in the short-run.

• Economic Analysis
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• v.25 no.3
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• pp.30-69
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• 2019

This paper analyzes the effects of trade on demand for labor of trading firms in Korea. We apply system GMM methodology to estimate the effects of imports and exports on employment of Korean manufacturing firms using firm-level data from the Survey of Business Activities of Statistics Korea between 2006 and 2014. According to our estimated results, for firms with high-productivity, exports have a positive and significant effect on the labor demand, while other firms do not show any such significant effects. Furthermore, our results show that offshoring mitigates the positive effects of exports on employment, since tasks within the firms can be relocated abroad. On the other hand, an increase in imports reduces demand for labor because labor is replaced with low-priced imported inputs. Also, when firms partake in global outsourcing, the negative effects of imports are mitigated as those firms expand their production by enhancing their efficiency in the process of offshoring. Therefore, our results suggest that it is important to consider heterogeneous firm productivity as well as offshoring in analyzing the effect of trade on labor demand of firms.

• Journal of Environmental Policy
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• v.7 no.1
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• pp.101-118
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• 2008

In this study, we estimate the labor demand function of environmental industry with environmental industry survey of Ministry of Environment. To do this, we apply the panel estimation technique. We follow the widely accepted estimation methods: panel generalized least square, panel generalized least square with heteroskedasticity/auto-correlation, random effect model and random effect model with auto-correlation. On the average, each industry is estimated at the elasticity of sales on labor demand from 0.193 to 0.259. It means that the increase of sales by 214billion won can create around $1,600{\sim}2,300$ jobs, and this is merely a direct effect. So when we consider the whole effect of labor demand increase including indirect derived job creation, the labor demand increase will be higher than this. So it is desirable for the government to support the development of environmental industry for sustainable development.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.161-164
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• 2006

The sales of consumer durables are composed of first time purchases and replacement purchases. Since the sales for most mature durable products are dominated by replacement sales, it is necessary to develop a model incorporating replacement component of sales in order to forecast total sales accurately. Several single product diffusion models incorporating replacement demand have been developed, but research addressing the multi-product diffusion models has not considered replacement sales. In this paper, we propose a model based on consumer choice behavior that simultaneously captures the diffusion and the replacement process for multi-product relationships. The proposed model enables the division of replacement sales into repurchase by previous users and transition purchase by users of different products. As a result, the model allows the partitioning of the total sales according to the customer groups (first-time buyers, repurchase buyers, and transition buyers), which allows companies to develop their production and marketing plans based on their customer mix. We apply the proposed model to the Korean automobile market, and compare the fitting and forecasting performance with other Bass-type multi-product models.