• 한국정밀공학회지
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• 제32권4호
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• pp.329-337
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• 2015

In this paper, a simulation based estimation method of energy consumption of the spindle and feed drives for the NC machine tool during the cutting process is proposed. To predict energy consumption of the feed drive system, position, velocity, acceleration and jerk of the table are analyzed based on NC data and then the power and energy are calculated considering friction force and mass of the stages. Energy consumption of the spindle is estimated based on models from acceleration motion of rotating parts, friction torque and power loss of motors. Moreover, simulation models of cutting power and energy for the material removal along the NC tool paths are proposed.

• 응용통계연구
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• 제37권1호
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• pp.13-38
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• 2024

최근 코로나19 등으로 경제 불확실성이 확대됨에 따라 민간 경제주체의 경제상황을 직접적으로 반영하는 민간소비 동향을 신속히 파악할 필요성이 높아지고 있다. 이에 본 연구는 기존 거시경제지표 뿐만 아니라 빅데이터를 종합적으로 활용하여 민간소비를 실시간으로 추정(nowcasting)하는 방법을 제안하였다. 특히 초고차원 빅데이터의 적합을 위해 활용 가능한 다양한 기계학습 방법론을 비교분석하여 민간소비 추정의 정확도를 향상시키고자 하였다. 실증 분석 결과, 빅데이터를 비롯한 가용 공변량의 수가 많은 경우에는 변수를 미리 선별하여 모형적합에 활용하는 것이 민간소비 예측 성능을 향상시킬 수 있음을 확인하였다. 또한 코로나19 이후 빅데이터의 반영이 민간소비 예측 성능을 더욱 크게 향상시킴에 따라 경제 불확실성이 높은 상황일수록 새로운 정보를 적시에 반영할 수 있는 고빈도 빅데이터의 활용가치가 높은 것으로 판단된다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제4권11호
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• pp.369-382
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• 2015

에너지 절감형 서버 클러스터에서는 에너지 절감을 고려하지 않는 기존 서버 클러스터에 비해 서비스 품질을 보장하면서 전력소비를 절감하는 것을 목표로 하며, 현재의 부하를 처리하는 데 필요한 최소수의 서버들만 ON 하도록 고정 주기 또는 가변 주기로 서버들의 전원모드를 조정한다. 이에 대한 기존 연구들은 전력을 절감하거나 열을 낮추는데 노력해왔지만 에너지 효율성을 잘 고려하지 못했다. 본 논문에서는 기존 자율학습기반의 서버 전원 모드 제어 방법의 단위전력당 성능과 QoS를 높이기 위한 에너지 효율적인 클러스터 관리기법을 제안한다. 제안 방법은 다중임계기반의 자율학습 방법과 전력소모 예측 방법을 결합한 서버 전원 모드 제어이다. 일반적인 부하 상황에서는 다중임계 학습기반의 서버 전원 모드 제어를 적용하고, 급변하는 부하 상황에서는 예측기반의 서버 전원 모드 제어가 적용된다. 일반적 상황과 급변하는 상황의 구별은 현재의 사용자 요청과 관찰된 과거 몇 분의 사용자 요청의 비율에 따라 이루어진다. 또한, 동적종료 기법을 추가로 적용해 서버가 OFF 하는 데 소요되는 시간을 단축한다. 제안 방법은 16대 서버로 구성된 클러스터 환경에서 3가지 부하 패턴을 이용하여 실험을 수행한다. 다중임계 학습, 예측, 동적종료를 함께 이용한 실험에서 단위전력당 성능(유효응답 수)과 표준화된 QoS 측면에서 가장 우수한 결과를 보여준다. 제안하는 방법과 파라미터 로드된 단일임계 학습을 비교할 때 뱅킹 부하패턴, 실제 부하패턴, 가상 부하패턴에서 단위전력당 유효응답 수가 각각 1.66%, 2.9%, 3.84% 향상되고, QoS 관점에서는 각각 0.45%, 1.33%, 8.82% 향상되었다.

• 한국지열·수열에너지학회논문집
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• 제7권1호
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• pp.65-71
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• 2011

Recently, central and local governments of Korea have established and implemented various energy policies such as making energy map of city level and establishment of environment friendly city plan to materialize low carbon green city. To implement effectively these policies, however, conditions of energy consumption by each administrative district and each usage have to be verified exactly. This study is aimed to suggest a basic data for planing energy policy and energy demand prediction of city level by analyzing energy consumption unit and conditions of city gas by borough and usage in Busan.

• 대한건축학회논문집:구조계
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• 제34권5호
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• pp.51-58
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• 2018

The purpose of this study was to analyze the effect of seasonal change in characteristics of hygiene activity on domestic hot water energy consumption. With 16 residents of 4 households, the data about frequency of hygiene activity and water temperature was collected from February to August, 2017. The results of collected data discovered that the frequency of hygiene activity was higher especially in summer, whereas the consumption of warm water they used was higher in winter. The seasonal change in characteristics of hygiene activity was analyzed to be changed and strongly influenced by outdoor temperature. The influence of characteristics of hygiene activity on hot water consumption was analyzed. There was 13% of difference between consumption that was calculated taking characteristics of hygiene activity into account and consumption that was not. Therefore, this study suggested hygiene activity schedule, hot water profile and hot water consumption pattern, which can be utilized for improving simulation as well.

• 한국컴퓨터정보학회논문지
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• 제23권10호
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• pp.65-72
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• 2018

In this paper, we used LSTM as a method to detect abnormal behavior of motors. We fixed the high layout size to 1 and changed the range of the input values and the neural network structure to see what change in power consumption prediction. Now, as the fourth industrial revolution era, smart factories are attracting attention. All the physical actions of smart factories are done using motors. Continuous monitoring of motor malfunctions helps to detect malfunctions and efficient operation. However, it is difficult to acquire the power consumption constantly due to the influence of the noise. We have experimented with a simple experimental environment, a method of predicting similarity to input data by adjusting the range of the input data or by changing the neural network structure.

• 한국콘텐츠학회논문지
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• 제19권7호
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• pp.436-443
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• 2019

본 논문의 목적은 탑재비행시험 간 무인헬기 연료 소모량에 영향을 미치는 인자들의 영향정도와 상관관계를 분석하여 예측모형을 수립하는 것이다. 본 연구에서는 실험계획법을 활용하여 4인자 2수준 완전요인실험을 설계하여 실험을 수행하였고, 결과 값을 분석하여 인자들의 주 효과와 교호작용을 도출하고 회귀분석을 통해 예측모형을 수립하였다. 본 연구에서 도출한 결과를 활용하여 효율적인 탑재비행시험 및 전자시험장 시험 능력 향상에 기여할 것으로 기대된다.

• Environmental Engineering Research
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• 제21권4호
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• pp.355-364
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• 2016

Beijing, as a cradle of modern industry and the third largest metropolitan area in China, faces more responsibilities to adjust industrial structure and mitigate carbon emissions. The purpose of this study is aimed at predicting and comparing industrial carbon emissions of Beijing in ten scenarios under different policy focus, and then providing emission-cutting recommendations. In views of various scenarios issues, system dynamics has been applied to predict and simulate. To begin with, the model has been established following the step of causal loop diagram and stock flow diagram. This paper decomposes scenarios factors into energy structure, high energy consumption enterprises and growth rate of industrial output. The prediction and scenario simulation results shows that energy structure, carbon intensity and heavy energy consumption enterprises are key factors, and multiple factors has more significant impact on industrial carbon emissions. Hence, some recommendations about low-carbon mode of Beijing industrial carbon emission have been proposed according to simulation results.

• 설비공학논문집
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• 제12권12호
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• pp.1090-1097
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• 2000

Predicting building energy use can be useful to evaluate its energy performance. This study proposed empirical approach for predicting building energy use with regression analysis. For the empirical analysis, simple regression models were developed based on the historical energy consumption data as a function of daily outside temperature, the predicting equations were derived for different operational modes and day types, then the equations were applied for predicting energy use in a building. BY selecting a real building as a case study, the feasibilities of the empirical approach for predicting building energy use were examined. The results showed that empirical approach with regression analysis was fairly reliable by demonstrating prediction accuracy of $pm10%$ compared with the actual energy consumption data. It was also verified that the prediction by regression models could be simple and fairly accurate. Thus, it is anticipated that the empirical approach will be useful and reliable tool for many purposes: retrofit savings analysis by estimating energy usage in an existing building or the diagnosis of the building operational problems with real time analysis.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.723-727
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• 2015

Building energy use estimation relies on building characteristics, its energy systems, occupants, and weather. Energy estimation of new buildings is considerably an easy task when compared to modeling existing buildings as they require calibration with actual data. Particularly, when energy estimation of existing building stock is warranted at a city-scale, the problem is exacerbated owing to lack of construction drawings and other engineering specifications. However, as collection of buildings and other infrastructure constitute cities, such predictions are a necessary component of developing and maintaining sustainable cities. This paper uses Artificial Neural Network techniques to predict electricity consumption for residential buildings situated in the City of Gainesville, Florida. With the use of 32,813 samples of data vectors that comprise of building floor area, built year, number of stories, and range of monthly energy consumption, this paper extends the prediction to environmental impact assessment of electricity usage at the urban-scale. Among others, one of the applications of the proposed model discussed in this paper is the study of urban scale Life Cycle Assessment, and other decisions related to creating sustainable cities.