• The Journal of Sustainable Design and Educational Environment Research
• /
• v.12 no.2
• /
• pp.61-70
• /
• 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 Digital Contents Society
• /
• v.15 no.3
• /
• pp.433-438
• /
• 2014

In this paper, we proposed the low power algorithm consider the battery and the task. The proposed algorithm setting the power consumption of unit time consider the capacity of the battery and the target time. Calculate the power consumption of all tasks. Calculate the average power consumption by the task have maximum power consumption and the task have minimum power consumption. Recalculate average power consumption consider the unit time of task. Compare calculated average power consumption and average power consumption of task. Compared results, low power algorithm processing the average power consumption less than or equal calculated power consumption of task. Low-power algorithm is greater than the average power consumption of the task to perform targeted tasks. Low-power processors and the task by dividing the power consumption of the device in large part for the low-power consumption is performed. Experiments [6] were compared with the results of the power consumption. The experimental results [6] is reduced power consumption than the efficiency of the algorithm has been demonstrated.

• The Journal of Sustainable Design and Educational Environment Research
• /
• v.11 no.2
• /
• pp.19-27
• /
• 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 Digital Contents Society
• /
• v.14 no.1
• /
• pp.59-64
• /
• 2013

In this paper, we proposed low power algorithm for a task. The task means the inside of a necessary processor and external resources to work accomplishment of a system. Each task analyzes a life time and a number of called for implement a low power circuit. First of all, reduce power consumption of a task have maximum power consumption for low power circuit implementation. Therefore, first selecting a task had maximum power consumption. The task had a maximum power consumption ranking consider a life time and a number of called for each task. While a life time of task is long, top priority ranking to decrease power consumption to the task that the number of call generates the power consumption how a disguise is large in case of a lot of task becomes. Frequency decision to have minimum power consumption, and decrease power consumption all the circuit by a change of frequency of the task which the minimum task that a wasting past record is the maximum becomes. Also, keep continuously minimum power consumption, with every effort task until last life time in opening life time, and decrease gets total power consumption. Experiments results show reduction in the power consumption by 5.43% comparing with that [7] algorithm.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.1
• /
• pp.53-58
• /
• 2013

In this paper, we proposed low power algorithm for battery residual capacity and a task. Algorithm the mobile devices power of the battery residual capacity for the task to perform power consumption to reduce the frequency alters. Task is different in power consumption according to kinds of in time accomplishment device to use. Adjustment of power consumption analyzes kinds of given tasks from having the minimum power consumption task to having the maximum power consumption task. Control frequency so that power consumption waste to be exposed to battery residual capacity can be happened according to the results analyzed. Experiment the frequency by adjusting power consumption a method to reduce using [7] and in the same environment power of the battery residual capacity consider the task to perform frequency were controlled. Efficiency was proved compare with the experiment results [7]. The experiments results show increment in the number of processing by 45.46% comparing with that [7] algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.2
• /
• pp.1027-1035
• /
• 2014

The most important design factor for portable devices is power consumption. In this paper, in the early design stage of a mobile device which measures temperature and humidity a power consumption model will be proposed and then the overall power consumption will be estimated based on this model. We will verify previously the correctness of such estimated power consumption before implementation of the real device. That is our proposed design methodology based on power consumption model. An improved design method for efficiently reducing the current consumption in the idle mode is also presented. By implementing a real prototype of the mobile device for measuring temperature and humidity, the correctness of our proposed design methodology based on power consumption modeling will be verified.

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

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.8
• /
• pp.472-476
• /
• 2004

Standby power is the electricity consumed in an electrical equipment when it is switched-off or not performing its main function. Due to the acceleration of digital electronics and home networking, standby power use tends to increase rapidly year by year. In this paper, standby power consumption in residential sector in Korea has been surveyed and reported for the first time. Totally 825 pieces of electrical equipments that consume standby power in 53 households were investigated. The average standby power per equipment and total standby power per household were 3.66W and 57.0W, respectively. Annual standby power consumption per household was estimated 306kWh; which means the standby power consumption in residential sector in Korea can be estimated 4.6TWh a year representing 1.67 percent of total electrical consumption (274TWh).

• ETRI Journal
• /
• v.37 no.1
• /
• pp.107-117
• /
• 2015

This paper presents an energy-efficient (low power) prime-field hyperelliptic curve cryptography (HECC) processor with uniform power draw. The HECC processor performs divisor scalar multiplication on the Jacobian of genus 2 hyperelliptic curves defined over prime fields for arbitrary field and curve parameters. It supports the most frequent case of divisor doubling and addition. The optimized implementation, which is synthesized in a $0.13{\mu}m$ standard CMOS technology, performs an 81-bit divisor multiplication in 503 ms consuming only $6.55{\mu}J$ of energy (average power consumption is $12.76{\mu}W$). In addition, we present a technique to make the power consumption of the HECC processor more uniform and lower the peaks of its power consumption.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.11 no.2
• /
• pp.765-781
• /
• 2019

This study predicts the power consumption of an Electric Propulsion Ship (EPS) in marine environment. The EPS is driven by a propeller rotated by a propulsion motor, and the power consumption of the propeller changes by the marine environment. The propulsion motor consumes the highest percentage of the ships' total power. Therefore, it is necessary to predict the power consumption and determine the power generation capacity and the propeller capacity to design an efficient EPS. This study constructs a power estimation simulator for EPS by using a ship motion model including marine environment and an electric power consumption model. The usage factor that represents the relationship between power consumption and propulsion is applied to the simulator for power prediction. Four marine environment scenarios are set up and the power consumed by the propeller to maintain a constant ship speed according to the marine environment is predicted in each scenario.