• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.3
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• pp.164-173
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• 2007

In this paper, we present a time-triggered mechanism for providing energy consumption profiles in the level of C functions. The similar mechanisms have already been introduced at the previous researches such as PowerScope and ePRO. Instead, we, in this paper, introduce our efforts to extend these researches to incorporate power domains and DVS(Dynamic Voltage Scaling), then to provide GUI-based tool as a plug-in to ESTO which is an IDE for Embedded S/W development based on Eclipse. From our experimental results, we could conclude that our approach worked and produced consistent energy consumption profiles on the DVS-applied program codes, and also displayed function level and time domain power consumption information with diverse presentation skills such as tables, phi-chart, bar-chart, 2-D graphs, consequently, is expected to provide more ease-to-use and productive IDE for lower power embedded S/W developers.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9B
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• pp.811-820
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• 2006

In this paper, The correlation of the power consumption of sensor node is analyzed according to the analyze parameter in IEEE 802.15.4 star sensor network. And It is studied the influence on analysis parameter. The power consumption of sensor network in transmission process and average transmission power consumption drives to numerical formula. And CSEM WiseNET system measurement value is used. As a simulation result, The power consumption of sensor node in star network consist of 10 sensor nodes is more than 20 % that in single network in average. When beacon signal interval is 0.1 second in all frequency bands, the power consumption of sensor node in up-link is more than 2.5 times that in down-link in average. When beacon signal interval is 1 second and the number of sensor nodes increases to 100 and sensing data increases to 100 byte, the power consumption of sensor node increases to 2.3 times. And The superior performance of 2.4 GHz frequency band has than 868/915 MHz frequency band up to $6{\sim}12$ times.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.91-96
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• 2019

There are so many low power MAC protocols for wireless sensor network. IEEE802.15.4 among them has disadvantage of a large power consumption for synchronization. To save power consumption it use the superframe operation alternating sleep mode and awake mode. But latency is longer result from superframe operation. Typical asynchronous B-MAC can have shorter latency according to check interval. But transmitter consumes more power because of long preamble. And receiver is suffering from overhearing. In this paper, we propose the adaptive check interval scheme of B-MAC for enhancing the power consumption and delay latency performance. Its power consumption is evaluated by comparing the proposed scheme with a typical IEEE802.15.4.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.155-158
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• 2005

An invesitigation on power consumption of a mobile multimedia system using OFDM and MDVS technique is reported here. Analysis and simulation are performed to find the significances of proposed Microscopic Dynamic Voltage Scaling(MDVS) tehnique[4] on digital processor in terms of power saving. A study is also made to show power reduction in mobile multimedia system by incorporating OFDM modulation scheme in RF front-end. Finally, overall power consumption by functionally distinguished blocks ie. RF front-end, digital processor and human interface unit is shown here. Total power consumption is 8.2W for 2Mbps SD-quality WCDMA multimedia video service - the power consumption of digital processor is 3.9W(48%), the power consumption of RF front-end is 3.2W (36%), and the power consumption of interface is 1.8W(16%). Power saving of applying purposed MDVS technique is 35% in digital processor, and power saving of OFDM technique is 10-12dB in RF front-end.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3918-3934
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• 2017

This paper presents a viewport resolution scaling technique to reduce power consumption in mobile graphic processing units (GPUs). This technique controls the rendering resolution of applications in proportion to the resolution factor. In the mobile environment, it is essential to find an effective resolution factor to achieve low power consumption because both the resolution and power consumption of a GPU are in mutual trade-off. This paper presents a resolution factor that can minimize image quality degradation and gain power reduction. For this purpose, software and hardware viewport resolution scaling techniques are applied in the Android environment. Then, the correlation between image quality and power consumption is analyzed according to the resolution factor by conducting a benchmark analysis in the real commercial environment. Experimental results show that the power consumption decreased by 36.96% on average by the hardware viewport resolution scaling technique.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.906-911
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• 1993

There will be about 0.25 to 0.3billion tons of grain product including rice, wheat and corn etc. each year in China. An energy analysis on grain drying system on which electricity , oil , coal or sun power and batch, tower with thick or thin layer of grain, infra red radiation. fluidized flowing types grain drying systems were made and compared for the sake of energy saving is shown in this paper.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.457-466
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• 2016

To achieve eco-friendly hospitals it is necessary to empirically verify the effect of meteorological factors on the power consumption of the hospital. Using daily meteorological big data from 2009 to 2013, we studied the weather conditions impact to power consumption and analyzed the patterns of power consumption of two hospitals. R analysis revealed that temperature among the meteorological factors had the greatest impact on the hospital power consumption, and was a significant factor regardless of hospital size. The pattern of hospital power consumption differed considerably depending on the hospital size. The larger hospital had a linear pattern of power consumption and the smaller hospital had a quadratic nonlinear pattern. A typical pattern of increasing power consumption during a hot summer and a cold winter was evident for both hospitals. The results of this study suggest that a hospital's functional specificity and meteorological factors should be considered to improve energy savings and eco-friendly building.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.469-477
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• 2009

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance, Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software, In this paper, power estimation model is developed using statistical analysis, The proposed model analyzes processor behavior Quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs, And then representative hardware events on power consumption are selected using hierarchical clustering, The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable, The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.1-6
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• 2009

The traditional demand prediction was based on the technique wherein electric power corporations made monthly or seasonal estimation of electric power consumption for each area and subscription type for the next one or two years to consider both seasonally generated and local consumed amounts. Note, however, that techniques such as pricing, power generation plan, or sales strategy establishment were used by corporations without considering the production, comparison, and analysis techniques of the predicted consumption to enable efficient power consumption on the actual demand side. In this paper, to calculate the predicted value of electric power consumption on a short-term basis (15 minutes) according to the amount of electric power actually consumed for 15 minutes on the demand side, we performed comparison and analysis by applying a 15-minute interval prediction technique to the average and that to the time series analysis to show how they were made and what we obtained from the simulations.