• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.4
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• pp.93-99
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• 2008

Portable devices generally have limited energy sources, so there is a need to minimize the power consumption of processor using energy conservation methods. One of the most common energy conservation methods is dynamic voltage scaling (DVS). In this paper, we propose a new DVS algorithm which uses workload of application to determine frequency and voltage of processors. The posed DVS algorithm consists of DVS module in kernel and specified function in application. The DVS module monitors the processor utilization and changes frequency and voltage periodically. The other part monitors workload of application. With these two procedures, the processor can change the performance level to meet their deadline while consuming less energy. We implemented the proposed DVS algorithm on PXA270 processor with Linux 2.6 kernel.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.866-874
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• 2006

This paper proposes a new Dynamic Voltage Scaling(DVS) algorithm to achieve low-power scheduling of aperiodic hard real-time tasks. Aperiodic tasks schedulingcannot be applied to the conventional DVS algorithm and result in consuming energy more than periodic tasks because they have no period, non predictable worst case execution time, and release time. In this paper, we defined Virtual Periodic Task Set(VTS) which has constant period and worst case execution time, and released aperiodic tasks are assigned to this VTS. The period and worst case execution time of the virtual task can be obtained by calculating task utilization rate of both periodic and aperiodic tasks. The proposed DVS algorithm scales the frequency of both periodic and aperiodic tasks in VTS. Simulation results show that the energy consumption of the proposed algorithm is reduced by 11% over the conventional DVS algorithm for only periodic task.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.2
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• pp.95-103
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• 2007

Dynamic voltage scaling (DVS) is a well-known and effective power management technique. While there has been research on slack distribution, voltage allocation and other aspects of DVS, its effects on non-voltage-scalable devices has hardly been considered. A DC-DC converter plays an important role in voltage generation and regulation in most embedded systems, and is an essential component in DVS-enabled systems that scale supply voltage dynamically. We introduce a power consumption model of DC-DC converters and analyze the energy consumption of the system including the DC-DC converter. We propose an energy-optimal off-line DVS scheduling algorithm for systems with DC-DC converters, and show experimentally that our algorithm outperforms existing DVS algorithms in terms of energy consumption.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.428-430
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• 2006

This paper proposes a new means of performance optimization for multimedia algorithm utilizing the Microscopic DVS (Dynamic Voltage Scaling). The Microscopic DVS technique controls the operating frequency and the supply voltage levels dynamically according to the processing requirement for each frame of multimedia data. The huffman decoding algorithm of MPEG-2 AAC audio decoder is optimized to maximize the power saving efficiency of Microscopic DVS technique. The experimental results show the reduction of computational complexity by more than 30% and the reduction of power consumption by more than 17% compared with those of the conventionally fast method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.12
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• pp.544-546
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• 2006

This paper proposes a new means of performance optimization for multimedia algorithm utilizing the Microscopic DVS (Dynamic Voltage Scaling). The Microscopic DVS technique controls the operating frequency and the supply voltage levels dynamically according to the processing requirement for each frame of multimedia data. The huffman decoding algorithm of MPEG-2 AAC audio decoder is optimized to maximize the power saving efficiency of Microscopic DVS technique. The experimental results show the reduction of computational complexity by more than 30% and the reduction of power consumption by more than 17% compared with those of the conventionally fast method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.12
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• pp.736-748
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• 2003

Dynamic voltage scaling(DVS), which adjusts the clock speed and supply voltage dynamically, is an effective technique in reducing the energy consumption of embedded real-time systems. The energy efficiency of a DVS algorithm largely depends on the performance of the slack estimation method used in it. In this paper, we propose novel DVS algorithms for periodic hard real-time tasks based on an improved slack estimation algorithm. Unlike the existing techniques, the proposed method can be applied to most priority-driven scheduling policies. Especially, we apply the proposed slack estimation method to EDF and RM scheduling policies. The experimental results show that the DVS algorithms using the proposed slack estimation method reduce the energy consumption by 20∼40 % over the existing DVS algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.95-100
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• 2005

This paper proposes a DVS method which effectively reduces the power consumption of multimedia signal processor. Analytic derivations of effective range of its power saving factor are obtained with the assumption of a Gaussian distribution for the frame-based computational burden of the multimedia processor. A closed form equation of the power saving factor is derived in terms of the mean-standard deviation of the distribution. An MPEG-2 video decoder algorithm and AAC encoder algorithm are tested on ARM9 RISC processor for the experimental verification of the power saying of the proposed DVS approach. The experimental results with diverse MPEG-2 video and audio files show 50～30% power saving factor and show good agreement with those of the analytically derived values.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.421-428
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• 2008

The DVS(Dynamic Voltage Scaling) technique is the method to reduce the dynamic energy consumption. As using slack times, it extends the execution time of the big load operations by changing the frequency and the voltage of variable voltage processors. Researches, that controlling the energy consumption of the processors and the data transmission among processors by controlling the bandwidth to reduce the energy consumption of the entire system, have been going on. Since operations in multiprocessor systems have the data dependency between processors, however, the DVS techniques devised for single processors are not suitable to improve the energy efficiency of multiprocessor systems. We propose the new scheduling algorithm based on DVS for increasing energy efficiency of multiprocessor systems. The proposed DVS algorithm can improve the energy efficiency of the entire system because it controls frequency and voltages having the data dependency among processors.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.236-243
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• 2019

Hydrologic time series has been analyzed and forecasted by using classical linear models. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. Daily streamflow series at St. Johns river near Cocoa, Florida, USA showed an interesting result of a low dimensional, nonlinear dynamical system but daily inflow at Soyang reservoir, South Korea showed stochastic property. Based on the chaotic dynamical characteristic, DVS (deterministic versus stochastic) algorithm is used for short-term forecasting, as well as for exploring the properties of the system. In addition to the use of DVS algorithm, a neural network scheme for the forecasting of the daily streamflow series can be used and the two techniques are compared in this study. As a result, the daily streamflow which has chaotic property showed much more accurate result in short term forecasting than stochastic data.