• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.233-242
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• 2014

A magnetically levitated vehicle (Maglev) system is under commercialization as a new transportation system in Korea. The Maglev is operated by an unmanned automatic control system. Therefore, the plan of train operation should be carefully established and validated in advance. In general, when making a train operation plan, statistically predicted traffic data is used. However, a traffic wave often occurs in real train service, and demand-driven simulation technology is required to review a train operation plan and service quality considering traffic waves. We propose a method and model to simulate Maglev operation considering continuous demand changes. For this purpose, we employed a discrete event model that is suitable for modeling the behavior of railway passenger transportation. We modeled the system hierarchically using discrete event system specification (DEVS) formalism. In addition, through implementation and an experiment using the DEVSim++ simulation environment, we tested the feasibility of the proposed model. Our experimental results also verified that our demand-driven simulation technology can be used for a priori review of train operation plans and strategies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1217-1220
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• 1996

For the purpose of the good tracking to variable load demands of the thermal power plant, a decentralized multilevel control(DMC) scheme is presented. It is applied to the drum type boiler-turbine system which is simplified from Boryung T/P #1,2 model[4]. A linearized model is decomposed into three subsystems by means of linear transformation. Then the DMC based on such subsystem is designed. Simulation using Matlab-Simulink shows that the proposed algorithm works very well to the large step change of power demand.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.99-108
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• 2007

This paper presents an elastic demand stochastic user equilibrium traffic assignment that could not be easily tackled. The elastic demand coupled with a travel performance function is known to converge to a supply-demand equilibrium, where a stochastic user equilibrium (SUE) is obtained. SUE is the state in which all equivalent path costs are equal, and thus no user can reduce his perceived travel cost. The elastic demand SUE traffic assignment can be formulated based on a dynamic system, which is a means of describing how one state develops into another state over the course of time. Traditionally it has been used for control engineering, but it is also useful for transportation problems in that it can describe time-variant traffic movements. Through the Lyapunov Function Theorem, the author proves that the model has a stable solution and confirms it with a numerical example.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.567-574
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• 2006

VG-AnyLAN is a local area network standard developed by the IEEE 802.12 project. While preserving the frame format of IEEE 802.3, VG-AnyLAN adopts a new medium access control called Demand Priority where transmission requests of stations are arbitrated by a control hub in a round-robin manner. Unlike CSMA/CD which is the medium access control of IEEE 802.3, the Demand Priority, while providing the maximum bound on the packet delay, does not put the limit on the network segment size. In this paper, we analyze the delay and the channel utilization performances of the medium access control of IEEE 802.12 VG-AnyLAN. We develope an analytic model of the system under assumptions that each station generates traffic of the equal priority and that the packets are of fixed length. Using the analytic model, we obtain the recursive expression of the average channel utilization and the average access delay The numerical results obtained via analysis are compared to the simulation results of the system for a partial validation of our analysis.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• 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.

• Journal of Information Technology Services
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• v.21 no.6
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• pp.117-125
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• 2022

In respond to climate change caused by global environmental problems, countries around the world are actively promoting the advancement of new electricity industries. The new energy business is being applied to energy storage systems (ESS), electric vehicle charging business, and power demand response using cutting edge technologies. In 2022, the Korean government is also establishing a policy stance to foster new energy industries and making efforts to improve its responsiveness to power demand response with the innovative technologies. In Korea, attempts to commercialize energy power are also being made in the private and public sectors to control energy power in houses, buildings, and industries. For example, private companies, local governments, and central government are making all-out efforts to develop new energy industry models through joint investment. There are forms such as establishing energy-independent facilities by region, establishing an electric vehicle charging system, controlling urban lighting systems with Information technologies, and managing demand between power suppliers and power consumers. This study examined the business model applied with energy storage system, electric vehicle charging business, smart lighting, and power demand response based on information communication technology to examine the site where smart energy system was introduced. According to this study, company missions and government tasks are suggested to apply new energy business technologies as economical energy solutions that meet the purpose of use by region, industry, and company.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1056-1061
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• 2009

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.