• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.53-58
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• 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 Software Engineering Society
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• v.27 no.1
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• pp.1-7
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• 2018

The consumption of mobile device batteries which are limited resources is an important criterion when circuit designers analyze and evaluate circuits. For this reason, researchers conducted researches with different models of battery consumption to analyze power consumption of mobile devices. The battery consumption model generation techniques have various characteristics depending on availability of sensors, run-time model generation, and models for using in verification and testing. However, there is lack of comparison and analysis between varied battery consumption model generation methods. In this research, we compare and evaluate the analysis methods which have been studied so far to support the circuit investigation for circuit designers. Finally, we suggest the direction of researches in battery consumption analysis using the comparison result.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.193-199
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• 2011

In this paper, we proposed battery driven low power algorithm in mobile device. Algorithm the mobile devices in power of the battery for the task to perform power consumption to reduce the frequency alters. Power of the battery perform to a task power consumption of is less than the task perform to frequency the lower. Frequency control the task, depending on in the entire system devices used among the highest frequency with devices first target perform to. Frequency in the decrease the second largest frequency with of the device the frequency in changes the power consumption to calculate. The calculated consumption power the battery of level is greater than level the frequency by adjusting power consumption, lower power of the battery the task perform when you can to the frequency to adjust. Experiment the frequency by adjusting power consumption a method to reduce using [6] and in the same environment power of the battery consider the task to perform frequency were controlled. The results in [6] perform does not battery power on task operates that the result was.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.2
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• pp.75-80
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• 2014

Energy consumption of sensor nodes is minimized because it has limited energy generator in wireless sensor network. Electronic shelf label system is one of application fields using wireless sensor networks. Battery size of small apparatus for displaying price is restricted. Therefore its current consumption have to be minimized. Furthermore the method for minimization of peak current would be considered because life cycle of coin battery used to display or RF is vulnerable to intensity of drain current. In this paper, we analyze current consumption pattern of low-power electronic shelf label system. Then we propose the method for minimization of current consumption by modification of software and hardware. Current consumption of the system using proposed method are approximately 15 to 20 percent lower than existing system and the life cycle of the system is approximately 10 percent higher than existing system.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.433-438
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• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.536-545
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• 2020

When general statistical regression methods are applied to predict the battery remaining time of a mobile smart device, they yielded the poor accuracy of estimating battery remaining time as the deviations of battery usage time per battery level became larger. In order to improve the estimation accuracy of general statistical regression methods, a preprocessing task is required to refine the measured raw data with large deviations of battery usage time per battery level. In this paper, we propose a data preprocessing framework that preprocesses raw measured battery consumption data and converts them into refined battery consumption data. The numerical results obtained by experimenting the proposed data preprocessing framework confirmed that it yielded good performance in terms of accuracy of estimating battery remaining time under general statistical regression methods for given refined battery consumption data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7B
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• pp.1009-1017
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• 2010

Offering of different attractive opportunities by different wireless technologies trends the convergence of heterogeneous networks for the future wireless communication system. To make a seamless handover among the heterogeneous networks, the optimization of the power consumption, and optimal selection of interface are the challenging issues. The access of multi interfaces simultaneously reduces the handover latency and data loss in heterogeneous handover. The mobile node (MN) maintains one interface connection while other interface is used for handover process. However, it causes much battery power consumption. In this paper we propose an efficient interface selection scheme including interface selection algorithms, interface selection procedures considering battery power consumption and user mobility with other existing parameters for overlaying networks. We also propose a priority based network selection scheme according to the service types. MN‘s battery power level, provision of QoS/QoE and our proposed priority parameters are considered as more important parameters for our interface selection algorithm. The performances of the proposed scheme are verified using numerical analysis.

• Journal of Electrochemical Science and Technology
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• v.10 no.4
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• pp.381-386
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• 2019

Zn-air battery uses oxygen from the air, and hence, air holes in it are kept open for cell operation. Therefore, loss of water by evaporation through the holes is inevitable. When the water is depleted, the battery ceases to operate. There are two water consumption routes in Zn-air batteries, namely, active path (electrolysis) and passive path (evaporation and corrosion). Water loss by the active path (electrolysis) is much faster than that by the passive path during the early stage of the cycles. The mass change by the active path slows after 10 h. In contrast, the passive path is largely constant, becoming the main mass loss path after 10 h. The active path contributes to two-thirds of the electrolyte consumption in 24 h of cell operation in 4.0 M KOH. Although water is an important component for the cell, water vapor does not influence the cell operation unless the water is nearly depleted. However, high oxygen concentration favors the discharge reaction at the cathode.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.12
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• pp.43-49
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• 2009

We present an efficient estimation method of the battery lifetime considering various power consumption profiles in wireless sensor nodes. The power profiles in single and periodic modes and the current dissipations in different operating modes are taken into account to find the total current consumption. Also, the self-discharge rate of a battery is taken into account to estimate the battery lifetime of a given sensor node. Finally we present a governing equation for finding the battery lifetime. We believe the proposed estimation method of the battery lifetime can be an efficient and effective method for low power design of sensor nodes.