• International Journal of Contents
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• 제15권4호
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• 한국항공운항학회지
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• 제28권2호
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• pp.29-35
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• 2020

In this paper, we established a prediction model of fuel consumption at the aircraft's taxi operation. To look for countermeasures to reduce fuel consumption and carbon emissions, Airbus A380's actual ground taxi data was used. As a result, the number of stops or turnings during the taxi operation was not related to fuel consumption. It was confirmed that the amount of fuel consumption in the taxi operation was the taxi time and the thrust change. It can be confirmed that ground control optimization, which is the result of close cooperation between the control organization and the airline, is absolutely necessary to reduce taxi time and minimize the occurrence of thrust change events.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.175-181
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• 2021

본 논문은 단기 에너지 사용량 예측을 위해 인공지능 기반의 접근법에 대해 분석한다. 본 논문에서는 단기 에너지 사용량 예측 기술에 자주 활용되는 지도학습 알고리즘의 한계를 개선하기 위해 강화학습 알고리즘을 활용한다. 지도학습 알고리즘 기반의 접근법은 충분한 성능을 위해 에너지 사용량 데이터뿐만 아니라 contextual information이 필요하여 높은 복잡성을 가진다. 데이터와 학습모델의 복잡성을 개선하기 위해 다중 에이전트 기반의 심층 강화학습 알고리즘을 제안하여 에너지 사용량 데이터로만 에너지 사용량을 예측한다. 공개된 에너지 사용량 데이터를 통해 시뮬레이션을 진행하여 제안한 에너지 사용량 예측 기법의 성능을 확인한다. 제안한 기법은 이상점의 특징을 가지는 데이터를 제외하고 실제값과 유사한 값을 예측하는 것을 보여준다.

• 정보통신설비학회논문지
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• 제10권2호
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• pp.74-79
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• 2011

The goal of this research is to reduce dynamic and static power consumption for a low power cache system. The proposed cache can achieve a low power consumption by using a drowsy and a way prediction mechanism. For reducing the static power, the drowsy technique is used at 4-way set associative cache. And for reducing the dynamic energy, one among four ways is selectively accessed on the basis of information in the Way-Line Prediction Unit (WLPU). This prediction mechanism does not introduce any additional delay though prediction misses are occurred. The WLPU can effectively reduce the performance overhead of the conventional drowsy caching by waking only a drowsy cache line and one way in advance. Our results show that the proposed cache can reduce the power consumption by about 40% compared with the 4-way drowsy cache.

• 산업경영시스템학회지
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• 제39권1호
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• 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.

• 한국정보통신학회논문지
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• 제26권4호
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• 반도체디스플레이기술학회지
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• 제18권1호
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• pp.123-128
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• 2019

Currently mobile services are on the verge of full commercialization ahead of 5G mobile communication (5G). The first goal could be to preempt the 5G market through realistic media services utilizing VR (Virtual Reality) and AR (Augmented Reality) technologies that users can most easily experience. Basically this movement is based on the advanced development of smart devices and high quality graphics processing computing power of mobile application processors. Accordingly, the importance of mobile GPUs is emerging and the most concern issue becomes a model for predicting the power and performance for smooth operation of high quality mobile contents. In many cases, the performance of mobile GPUs has been introduced in terms of power consumption of mobile GPUs using dynamic voltage and frequency scaling and throttling functions for power consumption and heat management. This paper introduces several studies of mobile GPU performance prediction model with user-friendly methods not like conventional power centric performance prediction models.

• 한국정밀공학회지
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• 제30권5호
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• pp.461-466
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• 2013

In this paper, main mechanism and measurement method of energy consumption for machine tools are investigated by experiment and simulation. To evaluate total energy consumption of the machine tools, standard test workpiece and measuring method and test procedures are suggested. And, improvement of energy consumption evaluation by the motion kinematics theory is used. In addition, to estimate energy consumption of machine tools in design process, mass distribution of the structure and 5 axis motions are investigated and simulated by numerical analysis.