• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.389-390
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• 2007

배터리 기반 시스템(휴대폰, PDA, 노트북)에서 현재 배터리에 대한 정확한 잔량 표시는 중요하다. 사용자입장에서 언제 배터리를 충전시켜야 하는지 알아야 하기 때문이다. 그런데 지금까지의 배터리 잔량 측정 장치를 보게 되면, 배터리의 전압만을 측정[1]하여 잔량을 표시하는 방식으로서 여기에는 여러 가지 문제점이 있다. 가장 중요한 문제점으로는 순간적인 배터리 전압강하에 따른 실 시간적이고 정확한 보상체계가 갖춰져 있지 않다는 점이다. 물론 하드웨어적으로 Schmitt Trigger라는 회로[2]를 구성하여 이를 방지해 놓고 있기는 하지만 Hysteresis margin[3]을 벗어난 값에 대해서는 보상을 해주지 않는다. 이런 보상은 보통 소프트웨어적으로 각 이밴트별 룩업 테이블을 만들어서 compensation하고 있기는 하지만, 수많은 이벤트에 대한 보상 값들과 예상치 못한 동작상의 오류를 막을 수는 없다. 따라서 이에 대한 근본적인 대안으로서 본 논문에서는 load current를 측정하여 그에 따른 전압강하를 계산하고 실시간적으로 배터리 전압에 보상을 해줌으로서 보다 정확한 배터리 잔량 표시를 구현하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.565-567
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• 2004

배터리를 효율적으로 사용하기 위한 기존 연구에서 제한된 용량의 배터리를 최대한 연장하여 사용하는 방법들에 관하여 다루었다. 본 논문에서는 시스템을 사용하는 정해진 시간동안 배터리가 소진되지 않도록, 시스템에서 동작하는 작업량을 동적으로 조절하는 방법을 제시한다. 제시된 방법에서는 시스템에서 동작하는 작업들을 중요성에 따라 분류하고, 시스템을 연동시키는 배터리 잔량을 주기적으로 측정한다. 그리고 측정된 배터리 잔량이 충분하면 모든 작업들을 동작시키고 배터리 잔량이 부족하면 중요성이 떨어지는 작업들부터 동작을 정지시켜서 전체 작업량을 감소시키고, 이를 통하여 배터리 지속 시간을 점진적으로 연장시키는 방법이다

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.106-108
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• 2004

본 논문은 기존 전력 소비 감소만을 고려한 DVS와 달리 배터리 특성에 기반하여 DVS를 수행하는 기법을 제시한다. 배터리는 잔량에 따라 다른 전력 소비형태를 갖고 있으므로 잔량에 따라서 적합한 DVS 정책을 수행해야 효율적이다. 본 논문에서는 실험을 통해서 배터리의 특성을 파악하고, 그 특성에 따라 배터리 구간을 설정한 후 그에 적합한 DVS 알고리즘을 적용하는 기법을 제시한다. 이를 통해 효율적인 DVS 정책을 수립 할 수 있음을 IPAQ 5550 PDA에서 리눅스 운영 체제에서의 실험을 통해 보여준다.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.71-78
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• 2020

In the case of power generation using renewable energy, power production may not be smooth due to the influence of the weather. The energy storage system (ESS) is used to increase the efficiency of solar and wind power generation. ESS has been continuously fired due to a lack of battery protection systems, operation management, and control system, or careless installation, leading to very big casualties and economic losses. ESS stability and battery protection system operation management technology is indispensable. In this paper, we present a battery level calculation algorithm and a failure prediction algorithm for ESS optimization and stable operation. The proposed algorithm calculates the correct battery level by accumulating the current amount in real-time when the battery is charged and discharged, and calculates the battery failure by using the voltage imbalance between battery cells. The proposed algorithms can predict the exact battery level and failure required to operate the ESS optimally. Therefore, accurate status information on ESS battery can be measured and reliably monitored to prevent large accidents.

• 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 KIPS Transactions:PartC
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• v.13C no.2 s.105
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• pp.219-226
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• 2006

The shortest path is only maintained during short time because network topology changes very frequently and each mobile nodes communicate each other by depending on battery in MANET(Mobile Ad-hoc Network). So many researches that are to overcome a limitation or consider a power have executed actively by many researcher. But these protocols are considered only one side of link stability or power consumption so we can make high of stability but power consumption isn't efficient. And also we can reduce power consumption of network but the protocol can't make power consumption of balancing. For that reason we suggest RBSSPR(Residual Battery Capacity and Signal Strength Based Power-aware Routing Protocol in MANET). The RBSSPR considers residual capacity of battery and signal strength so it keeps not only a load balancing but also minimizing of power consumption. The RBSSPR is based on AODV(Ad-hoc On-demand Distance Vector Routing). We use ns-2 for simulation. This simulation result shows that RBSSPR can extense lifetime of network through distribution of traffic that is centralized into special node and reducing of power consumption.

• Journal of KIISE:Information Networking
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• v.33 no.5
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• pp.395-406
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• 2006

In ad hoc networks, the limited battery capacity of nodes affects a lifetime of network Recently, a large variety of power-aware routing protocols have been proposed to improve an energy efficiency of ad hoc networks. Existing power-aware routing protocols basically consider the residual battery capacity and transmission power of nodes in route discovery process. This paper proposes a new power-aware routing protocol, TDPR(Traffic load & lifetime Deviation based Power-aware Routing protocol), that does not only consider residual battery capacity and transmission power, but also the traffic load of nodes and deviation among the lifetimes of nodes. It helps to extend the entire lifetime of network and to achieve load balancing. Simulations using ns-2[14] show the performance of the proposed routing protocol in terms of the load balancing of the entire network, the consumed energy capacity of nodes, and an path's reliability TDPR has maximum 72% dead nodes less than AODV[4], and maximum 58% dead nodes less than PSR[9]. And TDPR consumes residual energy capacity maximum 29% less than AODV, maximum 15% less than PSR. Error messages are sent maximum 38% less than PSR, and maximum 41% less than AODV.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1054-1056
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• 2016

As energy consumptions of smartphone increase, many smartphone users suffer from the lack of energy. Thus, many researches have been studied to save energy consumed in smartphone. To avoid inconvenience from the battery depletion, we first propose a remaining energy prediction model derived from the analysis on energy consumption pattern. Based on the model, we propose the energy efficient data transmission method for delay-tolerant applications.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1241-1248
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• 2014

An efficient battery usage of sensor nodes is main goal in a sensor network, which is the substructure of Internet of Things. Maximizing the battery usage of sensor nodes makes the lifetime of sensor network increase as well as the reliability of the network improved. The previous solutions to solve these problems are mainly focused on the cluster head selection based on the remaining energy. In this paper, we consider both the head selection and the replacement interval which is determined by a threshold that is based on the remaining energy, density of alive nodes, and location. Our simulation results show that the proposed scheme has outstanding contribution in terms of maximizing the life time of the network and balancing energy consumption of all nodes.