• 전자공학회논문지
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• 제51권7호
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• pp.96-102
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• 2014

본 논문에서는 dual-rail data encoding방식을 적용하여 비동기식시스템을 설계할 때, 신호천이를 줄이고 소비전력을 줄이기 위하여 4-phase handshaking 프로토콜과 2-phase handshaking 프로토콜을 혼합한 dual-rail data encoding방식을 제안한다. 기존의 dual-rail data encoding 4-phase handshaking 프로토콜은 space state가 존재함으로 말미암아 신호 천이가 많이 발생하게 되고 많은 전력소비를 발생한다. 이론적으로 dual-rail data encoding 2-phase handshaking 프로토콜은 dual-rail data encoding 4-phase handshaking 프로토콜보다 빠르고 신호천이도 적지만 표준 라이브러리를 사용하여 설계할 수 없다. 제안하는 혼합형 dual-rail data encoding 방식의 성능을 평가하기 위하여 Adder블록, Multiplier블록, Latch를 포함한 benchmark회로를 설계를 설계하였다. Benchmark회로를 이용하여 시뮬레이션해본 결과, 제안하는 혼합형 dual-rail data encoding방식은 기존의 dual-rail data encoding 4-phase handshaking 프로토콜에 비해 35%이상 전력소비가 감소되는 결과를 얻었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권11호
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• pp.5357-5381
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• 2018

Cloud computing offers a wide range of on-demand resources over the internet. Utility-based resource allocation in cloud data centers significantly increases the number of cloud users. Heavy usage of cloud data center encounters many problems such as sacrificing system performance, increasing operational cost and high-energy consumption. Therefore, the result of the system damages the environment extremely due to heavy carbon (CO2) emission. However, dynamic allocation of energy-efficient resources in cloud data centers overcomes these problems. In this paper, we have proposed Energy and Service Level Agreement (SLA) Aware Resource Allocation Heuristic Algorithms. These algorithms are essential for reducing power consumption and SLA violation without diminishing the performance and Quality-of-Service (QoS) in cloud data centers. Our proposed model is organized as follows: a) SLA violation detection model is used to prevent Virtual Machines (VMs) from overloaded and underloaded host usage; b) for reducing power consumption of VMs, we have introduced Enhanced minPower and maxUtilization (EMPMU) VM migration policy; and c) efficient utilization of cloud resources and VM placement are achieved using SLA-aware Modified Best Fit Decreasing (MBFD) algorithm. We have validated our test results using CloudSim toolkit 3.0.3. Finally, experimental results have shown better resource utilization, reduced energy consumption and SLA violation in heterogeneous dynamic cloud environment.

• 전기전자학회논문지
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• 제22권3호
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• pp.822-828
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• 2018

최근 전력 사용량의 증가로 인한 대규모 블랙아웃 등 에너지 문제가 대두되고 있으며, 이 문제들로 인해 전력 소비량 예측에 대한 정확도를 개선할 필요성이 부각되었다. 본 연구에서는 딥 러닝 기반의 전력 사용량 예측 실험을 통해서 실제 전력 소비량과 예측된 전력 소비량의 차이를 계산하고, 이를 통해서 전력 예비율을 기존 대비 하향 조정할 수 있는 가능성에 대해서 살펴본다. 예비 전력은 사용하지 않으면 손실되는 전력으로, 본 논문에서의 딥 러닝 기반 전력 소비량 예측을 통해서 여분의 전력을 과도하게 생산하지 않도록 오차범위 내에서 전력 예비율을 감소시킬 수 있는 기반을 마련할 수 있다. 본 논문에서 사용하는 딥 러닝 기법은 시계열 데이터를 처리하는 Long-Short-Term-Memory(LSTM) 구조의 학습 모델을 이용한다. 컴퓨터 시뮬레이션에서는 임의 생성한 전력 소비 데이터를 토대로 모델을 학습시키고, 학습된 모델을 토대로 전력 사용 예측값을 구하고 실제 전력 소비량 간에 오차를 계산한 결과 오차율 21.37%를 얻을 수 있었다. 이는 최근의 전력 예비율 45.9%를 고려할 때, 본 연구에서 제안한 전력 소비량 예측 알고리즘을 적용하는 경우 20% 포인트 정도의 예비율 감축이 가능하다.

• 전기전자학회논문지
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• 제22권2호
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• pp.476-479
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• 2018

This paper proposes a method for reducing the amount of current consumption by the transceiver based remote control and the ZigBee RF4CE network layer step. We have studied how to improve power efficiency at short transition time through duty rate management. Also, comparing the measured current consumption before and after the improvement, we confirmed the correlation between the data transmission speed improvement and the current reduction.

• 정보과학회 논문지
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• 제44권9호
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• pp.954-965
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• 2017

안정적인 전력 공급은 전력 인프라의 유지 보수 및 작동에 매우 중요하며, 이를 위해 정확한 전력 사용량 예측이 요구된다. 대학 캠퍼스는 전력 사용량이 많은 곳이며, 시간과 환경에 따른 전력 사용량 변화폭이 다양하다. 이러한 이유로, 전력계통의 효율적인 운영을 위해서는 전력 사용량을 정확하게 예측할 수 있는 모델이 요구된다. 기존의 시계열 예측 기법은 학습 시점과 예측 시점 간의 차이가 클수록 예측 구간이 넓어짐으로 예측 성능이 크게 떨어진다는 단점이 있다. 본 논문은 이를 보완하려는 방안으로, 먼저 의사결정나무를 이용해 날짜, 요일, 공휴일 여부, 학기 등을 고려하여 시계열 형태가 유사한 전력 데이터를 분류한다. 다음으로 분류된 데이터 셋에 각각의 자기회귀누적이동평균모형을 구성하여, 예측 시점에서 시계열 교차검증을 적용해 대학 캠퍼스의 일간 전력 사용량 예측 기법을 제안한다. 예측의 정확성을 평가하기 위해, 성능 평가 지표를 이용하여 제안한 기법의 타당성을 검증하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 통신소사이어티 추계학술대회논문집
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• pp.135-139
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• 2003

Sensor networks consist of sensor nodes with small-size, low-cost, low-power, and multi-functions to sense, to process and to communicate. Minimizing power consumption of sensors is an important issue in sensor networks due to limited power in sensor networks. Clustering is an efficient way to reduce data flow in sensor networks and to maintain less routing information. In this paper, we propose a multi-hop clustering mechanism using global and local ID to reduce transmission power consumption and an efficient routing method for improved data fusion and transmission.

• 산업경영시스템학회지
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• 제38권1호
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• pp.143-151
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• 2015

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

• Journal of Information Technology Applications and Management
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• 제14권2호
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• pp.1-10
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• 2007

The ubiquitous computing system is expected to be widely utilized in digital home, logistics control, environment/disaster management, medical/health-care services and other applications. The ubiquitous sensor network (USN) is a key infra-structure of this system. Nodes in the USN are exposed to adverse environments and required to perform their missions with very limited power supply only. Also the sensor network is composed of much more nodes. In case some node consumes up its power capacity under a certain required level, the network topology should change and re-routing/ re-transmission of data is necessitated. Resultantly communication protocols studied for conventional wireless networks or ad-hoc networks are not suitable for the sensor network. Schemes should be devised to control the efficient usage of node power in the sensor network. This paper proposes a routing algorithm to enhance the efficiency of power consumption for USN node and analyzes its performance by simulation.

• JSTS:Journal of Semiconductor Technology and Science
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• 제13권5호
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• pp.443-450
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• 2013

This paper propose a new ROM data encoding method that takes into account of a sequential access pattern to reduce the power consumption in ROMs used in applications such as FIR filters that access the ROM sequentially. In the proposed encoding method, the number of 1's, of which the increment leads to the increase of the power consumption, is reduced by applying an exclusive-or (XOR) operation to a bit pair composed of two consecutive bits in a bit line. The encoded data can be decoded by using XOR gates and D flip-flops, which are usually used in digital systems for synchronization and glitch suppression. By applying the proposed encoding method to coefficient ROMs of FIR filters designed by using various design methods, we can achieve average reduction of 43.7% over the unencoded original data in the power consumption, which is larger reduction than those achieved by previous methods.

• International journal of advanced smart convergence
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• 제4권1호
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.