• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.493-495
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• 2008

캐시 에너지의 소비 전력을 줄이기 위해 필터 캐시가 제안되었다. 필터 캐시의 사용으로 인해 많은 전력 사용 감소 효과를 가져왔으나, 상대적으로 시스템 성능도 더불어 감소하게 되었다. 필터 캐시의 사용으로 인한 성능 감소를 최소화하기 위해서, 본 논문에서는 기존에 제안된 주요 필터 캐시 예측 모델들을 소개하며, 각각의 방식에 있어서의 핵심 특징 및 해당 방식의 문제점을 분석한다. 이를 바탕으로 본 논문에서는 모드 선택 비트를 활용하는 개선된 형태의 새로운 필터 캐시 예측기 모델을 제안한다. 제안된 방식은 MSB라 불리는 참조 비트를 고안하여, 이를 기존의 필터캐시와 BTB에 새롭게 활용한다. 실험 결과, 제안된 방식은 기존 방식 대비, 전력 소모량 시간 지연면에서 평균 5%의 성능 향상을 가져 왔다.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.532-541
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• 2022

Purpose: The purpose of this study is to suggest that the existing power demand prediction method including power demand according to fine dust is included in the existing power consumption by using an air purifier to improve the air quality due to fine dust. Method: The method of the study was compared and analyzed using data on the concentration of fine dust in Seoul for three years, household power consumption, and climate observation, and the effect of fine dust on power consumption in Seoul was identified in April and October. Result: The power consumption of home air purifiers in Seoul due to fine dust differences between April and October was calculated to be 2,141 MWh, accounting for 3.4% of the total difference in the use of home appliances in April and October. Conclusion: The effect of fine dust on household power consumption was verified, and power demand prediction is essential for economic system operation and stable power supply, so power consumption due to fine dust should be considered as well as focusing on power consumption of existing air conditioners and heaters.

• Environmental and Resource Economics Review
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• v.23 no.3
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• pp.409-434
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• 2014

This paper estimates household demand for electricity using a micro-level household expenditure data set. A two-stage estimation method where the endogenous block price estimates are obtained from a discrete block choice model is used. This method successfully identifies a downward sloping conditional demand function with the data, while both the usual two-stage method with instrumental variable estimation and the Hewitt-Hanemann discrete-continuous model fail to do that. The paper simulates the impacts of two hypothetical pricing reforms that reduce the number of blocks and make the price gap smaller. It is shown that the reform may increase the overall consumer benefit, but is regressive.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.47-56
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• 2015

In an energy saving server cluster environment, the power modes of servers are controlled according to load situation, that is, by making ON only minimum number of servers needed to handle current load while making the other servers OFF. This algorithm works well under normal circumstances, but does not guarantee QoS under abnormal circumstances such as sharply rising or falling loads. This is because the number of ON servers cannot be increased immediately due to the time delay for servers to turn ON from OFF. In this paper, we propose a new prediction algorithm of the power consumption for improving QoS under not only normal but also abnormal circumstances. The proposed prediction algorithm consists of two parts: prediction based on the conventional time series analysis and prediction adjustment based on trend analysis. We performed experiments using 15 PCs and compared performance for 4 types of conventional time series based prediction methods and their modified methods with our prediction algorithm. Experimental results show that Exponential Smoothing with Trend Adjusted (ESTA) and its modified ESTA (MESTA) proposed in this paper are outperforming among 4 types of prediction methods in terms of normalized QoS and number of good reponses per power consumed, and QoS of MESTA proposed in this paper is 7.5% and 3.3% better than that of conventional ESTA for artificial load pattern and real load pattern, respectively.

• Journal of the Korean Vacuum Society
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• v.12 no.2
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• pp.93-104
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• 2003

For evaluation of durability of low vacuum pumps, it is required to examine the performance and degradation of low vacuum pumps. Pump degradation may result from abnormalities associated with the performance in many areas of pump operation. The diagnostics method can be used to monitor the pump performance in the semi-conductor process line. Based on the mechanical defect of the pump, the dynamic response and reliability of the system for performance test, and the dynamic characteristics of the pump were experimentally assessed. The theoretical work rate for the compression process in the pump was calculated, and then the efficiency of the pump associated with the power consumption was evaluated. This analysis will be useful in detecting pump degradation with increasing the power consumption. To determine the predominant factors of pump degradation, it is important to evaluate the entire pumping system. We studied vibration, dynamic pressure, pumping speed, and power consumption of low vacuum pumps. Our results can be utilized for the future research on the evaluating technology of durability of low vacuum pumps.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.43-50
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• 2022

Correlation Power Analysis(CPA) is a sub-channel attack method that measures the detailed power consumption of attack target equipment equipped with cryptographic algorithms and guesses the secret key used in cryptographic algorithms with more than 90% probability. Since CPA performs analysis based on statistics, a large amount of data is necessarily required. Therefore, the CPA must measure power consumption for at least about 15 minutes for each attack. In this paper proposes a method of using a Convolutional Neural Network(CNN) capable of accumulating input data and predicting results to solve the data collection problem of CPA. By collecting and learning the power consumption of the target equipment in advance, entering any power consumption can immediately estimate the secret key, improving the computational speed and 96.7% of the secret key estimation accuracy.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2942-2944
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• 2005

마이크로 프로세서의 클럭 속도를 공급 전압에 따라 변하게 하는 방법을 Dynamic Voltage Scaling 방법이라 한다. 이것은 운영체제를 내장한 컴퓨터 시스템의 에너지 소비 효율성을 높일 수 있는 매우 효과적인 방법이다. 본 논문에서는 Dynamic Voltage Scaling 방법을 응용하여 실시간 운영체제의 스케줄링 방법을 제안하였다. 이 방법은 다음에 실행할 태스크의 양을 예하여 적절하게 공급전압과 클럭 속도를 조절함으로써 에너지 소비 효율성을 높였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.78-85
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• 2008

Most researches for power management have focused on increasing the utilization of system performance by scaling operating frequency or operating voltage. If operating frequency is changed frequently, it reduces the real system performance. To reduce power consumption, alternative approaches use the limited number of operating frequencies or set the smoothing frequencies during execution to increase the system performance, but they are not suitable for real time applications. To reduce power consumption and increase system performance for real time applications, this paper proposes a new power-aware schedule method by allocating operating frequencies and by setting smoothing frequencies. The algorithm predicts so that frequencies with continuous interval are mapped into discrete operating frequencies. The frequency smoothing reduces overheads of systems caused by changing operating frequencies frequently as well as power consumption caused by the frequency mismatch at a wide frequency interval. The simulation results show that the proposed algorithm reduces the power consumption up to 40% at maximum and 15% on average compared to the CC RT-DVS.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.