• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.519-519
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• 2012

유도 결합 플라즈마에서 안테나 전류의 측정을 통해 시스템 저항을 계산하여 플라즈마 소비 전력을 구하는 기존의 방법은 정밀한 전류 측정의 한계를 가지고 있다. 본 연구에서는 유도 결합 방전 시스템에서 정합회로와 코일 사이에 설치된 전류 측정 장치를 사용하여 방전된 상태에서의 인가한 전력에 따른 코일 전류를 측정하였고, 방전되지 않은 상태에서 방전되었을 때와 같은 전류를 흐르게 인가 전력을 조절하였다. 이때의 측정값이 시스템이 소비하는 전력이라고 할 수 있다. 결과적으로 기존의 시스템 저항의 오차를 고려하지 않기 때문에 개선된 소비 전력값을 좀 더 용이하게 구할 수 있었다.

• Journal of Energy Engineering
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• v.13 no.3
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• pp.214-218
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• 2004

In the present study, the variation of input power and efficiency improvement of a scroll compressor applying PWM method are experimentally investigated. The cooling capacity, input power and COP are measured under the cooling operation. The input power due to the change of the condenser and the coil addition in a main current part is measured to enhance the compressor efficiency. Measured results show that the input power and COP increase with increasing the tooling capacity. And the minimum input power of the compressor is observed. By the adoption of the double system, the consumption of compressor input power is reduced, compared with the existing system.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.877-879
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• 2005

배터리를 이용하는 모바일 장비에서 전력 소비를 줄이기 위한 많은 연구들이 있다. 그 중에 동적 전력 관리(Dynamic Power Management)는 시스템의 각 컴포넌트의 상태를 쉽게 관찰할 수 있다는 측면에서 운영체제에서 접근하기 적합한 전력 관리 방법이다. 본 논문에서는 대표적인 모바일 장비인 노트북에서 하드 디스크의 전력소비를 줄이기 위하여 기계 학습 기반의 동적 전력 관리 방법을 제안한다. 하드 디스크 접근 패턴을 분석하여 Artificial Neural Network(ANN) 기법으로 모형을 만들고 이 모형을 바탕으로 하드 디스크의 다음 유휴기간을 예측하였다. 예측된 유휴기간 동안 하드 디스크로의 공급 전력을 감소시키지 않았을 경우에 소비하는 비용이 전력을 줄였다 다시 늘이는 비용보다 크다면 하드 디스크로 공급되는 전력을 줄임으로써 유휴기간 동안 낭비되는 배터리 전력을 줄일 수 있었다. 본 연구에서 생성된 모형을 하드 디스크 디바이스 드라이버에 적용하면 기존의 시간 경계 값을 이용한 방법에 비해 약 23.05W의 전력 소비 감소를 기대할 수 있다.

• Journal of IKEEE
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• v.8 no.2 s.15
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• pp.181-185
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• 2004

This paper reduces power dissipation with the minimum switching activity of functional units that have many operators. Therefore, it has more effects of power dissipation that operator dissipation to reduce power dissipation of whole circuit preferentially. This paper proposes an algorithm that minimize power dissipation in functional units operations that affect much as power dissipation in VLSI circuit. The algorithm has scheduled operands using power library that has information of all operands. The power library upgrades information of input data in each control step about all inputs of functional units and the information is used at scheduling process. Therefore, the power dissipation is minimized by functional units inputs in optimized data. This paper has applied algorithm that proposed for minimizing power dissipation to functional unit in high level synthesis. The result of experiment has effect of maximum 9.4 % for minimizing power dissipation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.961-970
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• 2017

The differential power analysis calculates the intermediate value related to sensitive information and substitute into the power model to obtain (hypothesized) power consumption. After analyzing the calculated power consumption and measuring power consumption, the secret information value can be obtained. Hamming weight and hamming distance models are most commonly used power consumption model, and the power consumption model is obtained through the modeling technique. If the power consumption model assumed by the actual equipment differs from the power consumption of the actual equipment, the side channel analysis performance is declined. In this paper, we propose a method that records measured power consumption and exploits as power consumption model. The proposed method uses the power consumption at the time when the information (plain text, cipher text, etc.) available in the encryption process. The proposed method does not need template in advance and uses the power consumption measured by the actual equipment, so it accurately reflects the power consumption model of the equipment.. Simulation and experiments show that by using our proposed method, side channel analysis is improved on the existing power modeling method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.204-209
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• 2002

본 실험은 주택용, 일반용, 교육용, 산업용, 농사용, 심야전력 등 국내의 종별요금체계에 따라 인공광을 이용한 접목묘 활착촉진장치에서 소비된 전기에너지와 전력요금을 비교하고자 수행되었다. 그 결과 전력요금은 일반용>교육용>산업용>심야전력>농사용의 순서로 나타났으며 접목묘 1주를 활착시키는 데 필요한 전기요금은 농사용의 경우 9.24원이었으며, 일반용 전력요금과 비교할 때 봄과 가을, 겨울, 여름철의 1주당 생산단가는 각각 2.3배, 2.4배, 3.4배로 나타났다. 또한 본 실험에서 소비된 전력은 컴프레서, 가열기, 조명, 가습기, 송풍기의 순서로 각각 28%, 57%, 11%, 3%, 1%로 나타났다. 활착촉진장치에서 소비된 전력 가운데 공조 시스템, 즉 냉·난방이 차지하는 비중이 전체의 85%로서 매우 높게 나타난 바, 공조 시스템에 대한 전력소모량의 절감 방안이 우선적으로 강구되어야 할 것으로 판단된다.

• Journal of Navigation and Port Research
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• v.36 no.6
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• pp.501-506
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• 2012

To serve basic data for the design of capacity and management of Distributed(or On-site) Power Generation System using renewable energies, this study measured the electric power consumption(hereafter abbreviated as EPC) of 5 families of fishing village located at island in southeastern area of Korea. The results are as following. The maximum monthly average EPC occurred in December or January. Although the total monthly EPC of H family is 2~3 times more than J family, individual monthly EPC of J family is 10~30 % more than H family. Hourly EPC pattern shows that the maximum EPC occurred between 20~24 o'clock in summer season, but it occurred between 18~24 o'clock in winter season. Compared to summer, the height of fluctuation through a day is small. And the EPC patterns of weekdays and weekend estimated as very similar.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.11
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• pp.411-418
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• 2016

Context-aware application has a lot of power consumption because it creates context by using a number of smartphone's sensors. Furthermore, only few kinds of researches have been conducted that provide information for the evaluation result of power consumption in the aspect of applications. In addition, evaluation of power consumption do not consider user's usage pattern or provide only total amount of power consumption, and inform developers power consumption of sensors undistinguishable. It makes developers hard to develop a power consumption-considered application. If developers could get information for power consumption of context-aware application in detail, a development of power-considered context-aware applications would be possible. Consequently, this paper proposes a BMT(Bench Mark Test) model which is able to inform developers useful evaluation criteria and result about power consumption of smartphone's components and sensors with usage pattern considered.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.7-14
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• 2016

Recently, as cloud computing technologies are developed, it enable to work anytime and anywhere by smart phone and computer. Also, cloud computing technologies are suited to reduce costs of maintaining IT infrastructure and initial investment, so cloud computing has been developed. As demand about cloud computing has risen sharply, problems of power consumption are occurred to maintain the environment of data center. To solve the problem, first of all, power consumption has been measured. Although using power meter to measure power consumption obtain accurate power consumption, extra cost is incurred. Thus, we propose prediction method about power consumption without power meter. To proving accuracy about proposed method, we perform CPU and Hard disk test on cloud computing environment. During the tests, we obtain both predictive value by proposed method and actual value by power meter, and we calculate error rate. As a result, error rate of predictive value and actual value shows about 4.22% in CPU test and about 8.51% in Hard disk test.