• The Journal of Sustainable Design and Educational Environment Research
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• v.12 no.2
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• pp.61-70
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• 2013

As the existing school building power consumption is expressed by total power consumption, in the view of energy saving is disadvantage. The the power consumption of school building is divided as cooling, heating, lighting and others. The cooling power consumption, heating power consumption, lighting power consumption can be calculated using real total power consumption that gained from Korea Electric Power Corporation(KEPCO). The power consumption for cooling and heating can be calculated using heat transmittance, wall area and floor area, and for lighting is calculated by artificial lighting calculation. but this calculation methods is difficult for laymen. This study was carried out in order to establish the regression equation for cooling power consumption, heating power consumption, lighting power consumption and other power consumption in school building. In order to verify the validity of the regression equation, it is compared regression equation results and calculation results based on real power consumption. As the results, difference between regression result and calculation results for cooling and heating power consumption showed 0.6% and 3.6%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.961-970
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• 2017

The differential power analysis calculates the intermediate value related to sensitive information and substitute into the power model to obtain (hypothesized) power consumption. After analyzing the calculated power consumption and measuring power consumption, the secret information value can be obtained. Hamming weight and hamming distance models are most commonly used power consumption model, and the power consumption model is obtained through the modeling technique. If the power consumption model assumed by the actual equipment differs from the power consumption of the actual equipment, the side channel analysis performance is declined. In this paper, we propose a method that records measured power consumption and exploits as power consumption model. The proposed method uses the power consumption at the time when the information (plain text, cipher text, etc.) available in the encryption process. The proposed method does not need template in advance and uses the power consumption measured by the actual equipment, so it accurately reflects the power consumption model of the equipment.. Simulation and experiments show that by using our proposed method, side channel analysis is improved on the existing power modeling method.

• Journal of the Korean Wood Science and Technology
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• v.33 no.5 s.133
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• pp.38-44
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• 2005

Peripheral milling is one of the most important wood machining processes in wood industry. Power consumption characteristics of twelve Korean domestic species in peripheral milling were investigated in this study. Image analysis technique was applied to extract proper data from the power consumption profiles. Average power consumption increased as cutting depth increased and specific cutting power decreased as cutting depth increased. However, no significant relationship could be found between density and power consumption and between cutting depth and surface roughness.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.231-233
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• 2005

For the low-power design of the mobile multimedia system architecture, this paper modeling the mobile multimedia system and analysis the power consumption profile about the whole communication environment. The mobile system model consist of air interface, RIP front-end, base-band processing module and human interface. For the result of power consumption profile analysis, the power consumption of multimedia processing is above 60% compare to the whole power consumption in mobile multimedia system. To minimize the power consumption in processing module which consumes the large power, this paper proposed the Microscopic DVS technique which applies the optimum voltage for the each multimedia frame. For the simulation result, proposed power minimization technique reduce the power consumption about 30%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.143-151
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• 2015

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.300-313
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• 2014

Various Time Synchronization protocols for a Wireless Sensor Network (WSN) have been developed since time synchronization is important in many time-critical WSN applications. Aside from synchronization accuracy, energy constraint should also be considered seriously for time synchronization protocols in WSNs, which typically have limited power environments. This paper performs analysis of prominent WSN time synchronization protocols in terms of power consumption and test by simulation. In the analysis and simulation tests, each protocol shows different performance in terms of power consumption. This result is helpful in choosing or developing an appropriate time synchronization protocol that meets the requirements of synchronization accuracy and power consumption (or network lifetime) for a specific WSN application.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.2
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• pp.19-27
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• 2012

This study was carried out in order to establish the estimation equation for school power consumption using regression analysis based on collected power consumption for two years of weather data and schools are located in Central Changwon and Masan district in Changwon city. (1) The power consumption estimation equation for Heating and cooling is calculated using power consumption per unit volume, the difference between actual power consumption and results of estimation equations is 4.1%. (2) The power consumption estimation equation for heating load is showed 2.6% difference compared to actual power consumption in Central Changwon and is expressed 2.9% difference compared to that in Masan district. Therefore, the power consumption prediction for each school using the power consumption estimation equation is possible. (3) The power consumption estimation equation for cooling load is showed 8.0% difference compared to actual power consumption in Central Changwon and is expressed 2.9% compared to that in Masan district. As the power consumption estimation equation for cooling load is expressed difference compared to heating load, it needs to investigate influence for cooling load.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.4
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• pp.369-375
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• 2023

In this paper, we compared and analyzed the power load patterns of dormitory buildings and office buildings to use them as basic data (demand analysis and capacity design) for the design and operation of microgrids for multi-use facilities, and the following conclusions were got. During the daytime on regular weekdays, the power consumption load pattern of office buildings was relatively large at 264.0~332.3 kWh, and during the evening hours, the power consumption load pattern of dormitory buildings was relatively large at 233.0~258.3 kWh. In the case of vacation, during the daytime on weekdays, the power consumption load pattern of office buildings was relatively large at 279.1~407.4 kWh, and in the evening, the power consumption load pattern of dormitory buildings was relatively high at 280.1~394.1 kWh. During the daytime on regular weekends, the power consumption of dormitory-type buildings was relatively high at 133.5~201.6 kWh, and it was found that the power consumption of dormitory-type buildings appeared relatively high at 187.5~252.1 kWh. During a vacation in the daytime on weekends, the power consumption of dormitory-type buildings was found to be 186.5 kWh~ and 218.6 kWh. The increase in power consumption during a vacation (December-February) compared to normal (April-June) was thought to be due to an increase in electricity demand, and the reason for the higher power consumption in dormitory buildings during the vacation was due to reduced working hours in office buildings.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.207-217
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• 2018

Recently, the importance of energy demand management, particularly peak load control, has been increasing due to the policy changes of the Second Energy Basic Plan. Even though the installation of distributed generation systems such as Photovoltaic and energy storage systems (ESS) are encouraged, high initial installation costs make it difficult to expand their supply. In this study, the power consumption of a university building was measured in real time and the measured power consumption data was used to calculate the optimal installation capacity of the Photovoltaic and ESS, respectively. In order to calculate the optimal capacity, it is necessary to analyze the operation methods of the Photovoltaic and ESS while considering the KEPCO electricity billing system, power consumption patterns of the building, installation costs of the Photovoltaic and ESS, estimated savings on electric charges, and life time. In this study, the power consumption of the university building with a daily power consumption of approximately 200kWh and a peak power of approximately 20kW was measured per minute. An economic analysis conducted using these measured data showed that the optimal capacity was approximately 30kW for Photovoltaic and approximately 7kWh for ESS.

• The KIPS Transactions:PartD
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• v.16D no.6
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• pp.871-880
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• 2009

Along with the complexity and size growth of embedded software, it is critical to meet the nonfunctional requirements such as power consumption as well as functional requirements such as correctness. This paper, apart from the existing studies of source code-based power analysis, proposes an approach of model-based power analysis using UML 2.0. Specially, we focus on the development of energy library to analyze the power consumption of embedded software. Our energy library supports model-based power analysis, and also supports the easy adaption for the change of embedded application.