• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.44-47
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• 2011

In this paper, we propose context-aware based on Low Power Sensing Algorithm. The proposed sensing algorithm reduces power consumptions using low-power sensing algorithms and low-power sensing protocols. Experimental results show that the average power consumption of the proposed method is up to half consumption that of the conventional method.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.851-854
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• 2003

The low power motion estimation for MPEG-4 is a soft-core for hardwired motion estimation block in MPEG-4. This motion estimation is modified by 10 difference mode. So, this motion estimation decrease a power consumption compare conventional step search. This modified 4SS Low power Motion Estimation has been tested and verified to be valid for implementation of FPGA. The average PSNR between the original image and the motion-compensated image is 28.25dB. And Power consumption is 26mW.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.139-146
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• 2012

In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

• Journal of Communications and Networks
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• v.13 no.1
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• pp.17-25
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• 2011

A convergecast is a popular routing scheme in wireless sensor networks (WSNs) in which every sensor node periodically forwards measured data along configured routing paths to a base station (BS). Prolonging lifetimes in energy-limited WSNs is an important issue because the lifetime of a WSN influences on its quality and price. Low-energy adaptive clustering hierarchy (LEACH) was the first attempt at solving this lifetime problem in convergecast WSNs, and it was followed by other solutions including power efficient gathering in sensor information systems (PEGASIS) and power efficient data gathering and aggregation protocol (PEDAP). Our solution-chain routing with even energy consumption (CREEC)-solves this problem by achieving longer average lifetimes using two strategies: i) Maximizing the fairness of energy distribution at every sensor node and ii) running a feedback mechanism that utilizes a preliminary simulation of energy consumption to save energy for depleted Sensor nodes. Simulation results confirm that CREEC outperforms all previous solutions such as LEACH, PEGASIS, PEDAP, and PEDAP-power aware (PA) with respect to the first node death and the average lifetime. CREEC performs very well at all WSN sizes, BS distances and battery capacities with an increased convergecast delay.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.12
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• pp.103-107
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• 2014

Together with housings, general buildings and industrial facilities, multi-purpose complexes are equipped with various and special equipment. They are often used by many unspecified people, which causes an increase in annual electricity consumption. Because of this, a great amount of money has been spent for electric charge, far more in excess of the budget, so a reasonable electricity rate needs to be estimated. In this study, we surveyed the power consumption, average power use, and annual electricity bill of multi-purpose complexes in the past five years. To see the general tendency of the survey, we conducted a statistical analysis with such parameters as average, maximum, and minimum values. Through regression analysis, we could see the trend of the survey in linear way. Based on the survey, we have developed an electric-rate calculation program to estimate the next year's budget on electricity.

• ETRI Journal
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• v.37 no.1
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• pp.157-164
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• 2015

As GPU power consumption in smartphones increases with more advanced graphic performance, it becomes essential to estimate GPU power consumption accurately. The conventional GPU power model assumes, simply, that a GPU consumes constant power when turned on; however, this is no longer true for recent smartphone GPUs. In this paper, we propose an accurate GPU power model for smartphones, considering newly adopted dynamic voltage and frequency scaling. For the proposed GPU power model, our evaluation results show that the error rate for system power estimation is as low as 2.9%, on average, and 4.6% in the worst case.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.462-466
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• 2010

A new buffer cache management scheme that aims at reducing power consumption in mixed HDD and NAND flash memory storage systems is presented. The proposed scheme reduces power consumption by considering different energy-consumption rate of storage devices, I/O operation type (read or write), and reference potential of cached blocks in terms of both recency and frequency. Simulation shows that the proposed scheme reduces power consumption by 18.0% on average and up to 58.9%.

• Journal of Biosystems Engineering
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• v.36 no.5
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• pp.303-313
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• 2011

This study was conducted to investigate the effects of axle weight distribution and inflation pressure of tire on the fuel economy of tractors as well as operational range of tractor engine in terms of engine speed and power when a 4WD tractor of 38.2 kW rated power at 2500 rpm is used for plowing and flooded-field rotavating in paddy fields. (1) Plowing operation required an average engine power of 9.6~13.5 kW which equals 25~35% of rated PTO power. Engine speed ranged from 1,320.4 to 1,737.4 rpm, work velocity from 3.4 to 4.8 km/h, and fuel consumption from 3.2 to 4.2 L/h, respectively. (2) Flooded-field rotavating required an average engine power of 11.5~18.5 kW which equals 30~48.4% of rated PTO power. Out of this 6.2~12.2 kW was used for PTO power. Engine speed ranged from 1,557 to 2,067 rpm, work velocity from 2.5~5.4 km/h and fuel consumption from 3.2~5.5 L/h, respectively. (3) Axle weight distribution, inflation pressure of tire and moisture content of soil did not affect significantly the specific volumetric fuel consumption but affected significantly the fuel consumption per unit area of operation. Fuel savings amounted to 65% in plowing operation and 20% in flooded-field rotavating when the axle weight distribution and inflation pressure of tire were optimally adjusted. (4) Optimal adjustment of axle weight distribution and inflation pressure of tire are expected to save fuel consumption by 10~65% per unit area of operation in plowing and 10~20% in flooded-field rotavating.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5149-5167
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• 2017

Reducing energy consumption in a wireless video sensor network (WVSN) is a crucial problem because of the high video data volume and severe energy constraints of battery-powered WVSN nodes. In this paper, we present an adaptive dynamic resizing approach for a SRAM communication buffer in a WVSN node in order to reduce the energy consumption and thereby, to maximize the lifetime of the WVSN nodes. To reduce the power consumption of the communication part, which is typically the most energy-consuming component in the WVSN nodes, the radio needs to remain turned off during the data buffer-filling period as well as idle period. As the radio ON/OFF transition incurs extra energy consumption, we need to reduce the ON/OFF transition frequency, which requires a large-sized buffer. However, a large-sized SRAM buffer results in more energy consumption because SRAM power consumption is proportional to the memory size. We can dynamically adjust any active buffer memory size by utilizing a power-gating technique to reflect the optimal control on the buffer size. This paper aims at finding the optimal buffer size, based on the trade-off between the respective energy consumption ratios of the communication buffer and the radio part, respectively. We derive a formula showing the relationship between control variables, including active buffer size and total energy consumption, to mathematically determine the optimal buffer size for any given conditions to minimize total energy consumption. Simulation results show that the overall energy reduction, using our approach, is up to 40.48% (26.96% on average) compared to the conventional wireless communication scheme. In addition, the lifetime of the WVSN node has been extended by 22.17% on average, compared to the existing approaches.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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• pp.33-39
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• 2011

This paper considers a performance analysis of set-top box (STB) power saving schemes. STB converts the signal into content which is then displayed on the television (TV) screen, and there are typically two operation modes: on mode and stand-by mode. The total energy consumption (TEC), a typical measure of power consumption of STB, is defined by the sum of power consumption in each mode. Recently there are some works of STB power saving schemes that transit STB operation modes efficiently, and the mode transition time point of those schemes can be different. Thus it is required to develop a performance evaluation method that reflects mode transition time points of each scheme to get TEC correctly. This paper proposes a performance evaluation method for STB power consumption using Poisson process to consider the mode transition time point. By modeling STB mode transitions as events of Poisson process, the average time duration of STB mode is computed and accordingly the effect of power saving is evaluated. The performance evaluation result shows that the proposed method achieves 1 to 19% improvement in power consumption compared with a conventional performance evaluation method.