• 한국산학기술학회논문지
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• 제16권10호
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• pp.6799-6806
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• 2015

최근 물류센터는 대형화 첨단화에 따른 다양한 설비 및 장비의 도입으로 전기에너지 소비가 급격히 증가하고 있다. 본 연구는 물류센터의 전기에너지 사용 현황 및 소비 특성을 정량적으로 분석하고, 효율을 평가하기 위한 전기에너지 표준소비량을 추정하는 모형을 구축하는 것을 목적으로 한다. 제시된 모형은 물류센터의 온도요인이 전기에너지 소비에 큰 영향을 미치는 특성을 효과적으로 반영하기 위하여 열역학 이론을 도입하였다. 모형은 물류센터 벽면의 열전도, 출입문 열대류 및 취급물품의 열 손실로 구성된 냉동기 운용에너지 부문과 물류활동을 위한 기계설비의 전력소모 부문으로 구성된다. 모형은 또한 물류센터 운영자가 에너지 소비 효율을 평가하고 개선전략을 수립하는 것을 지원할 수 있도록 다양한 설명변수들을 포함한다. 실제 물류센터의 에너지 소비량을 기반으로 본 연구에서 개발된 모형의 적용성이 평가된다.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2005년도 추계학술발표회
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• pp.699-700
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• 2005

• Journal of Radiation Protection and Research
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• 제41권2호
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• pp.93-99
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• 2016

Background: Industrial X-ray CT system is normally applied to non-destructive testing (NDT) for industrial product made from metal. Furthermore there are some special CT systems, which have an ability to inspect nuclear fuel assemblies or rocket motors, using high power and high energy (more than 6 MeV) pulsed X-ray source. In these case, pulsed X-ray are produced by the electron linear accelerator, and a huge number of photons with a wide energy spectrum are produced within a very short period. Consequently, it is difficult to measure the X-ray energy spectrum for such accelerator-based X-ray sources using simple spectrometry. Due to this difficulty, unexpected images and artifacts which lead to incorrect density information and dimensions of specimens cannot be avoided in CT images. For getting highly precise CT images, it is important to know the precise energy spectrum of emitted X-rays. Materials and Methods: In order to realize it we investigated a new approach utilizing the Bayesian estimation method combined with an attenuation curve measurement using step shaped attenuation material. This method was validated by precise measurement of energy spectrum from a 1 MeV electron accelerator. In this study, to extend the applicable X-ray energy range we tried to measure energy spectra of X-ray sources from 6 and 9 MeV linear accelerators by using the recently developed method. Results and Discussion: In this study, an attenuation curves are measured by using a step-shaped attenuation materials of aluminum and steel individually, and the each X-ray spectrum is reconstructed from the measured attenuation curve by the spectrum type Bayesian estimation method. Conclusion: The obtained result shows good agreement with simulated spectra, and the presently developed technique is adaptable for high energy X-ray source more than 6 MeV.

• 전기학회논문지
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• 제61권8호
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• pp.1193-1198
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• 2012

An instantaneous amplitude (IA) estimator using the symmetric higher order differential energy operator is proposed. The amplitude estimator and the instantaneous frequency (IF) estimator based on the symmetric higher order differential energy operator coincide with the analyzed signal in time, and they show better estimation results than the IA and IF based on the higher order differential energy operator. Various IF and IA estimators are applied to AM-FM signals for the performance comparison. Among the IF and IA estimators, the IF and IA estimators based on the symmetric higher order energy operator show the best estimation accuracy. Then, the IA and IF estimators are applied to the distorted power line signal to show their usefulness as power disturbance detectors.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2017년도 추계학술논문요약집
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• pp.323-324
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• 2017

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2017년도 춘계학술논문요약집
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• pp.263-264
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• 2017

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2018년도 춘계학술논문요약집
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• pp.303-304
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• 2018

• 정보처리학회논문지A
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• 제19A권3호
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• pp.139-146
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• 2012

서버 클러스터 환경에서 에너지 절약을 위한 방법 중 하나는 서버의 전원을 트래픽 상황에 맞게 제어하는 전원 제어 기술이다. 이는 현재 데이터 센터의 전체 에너지 사용량과 각 서버의 에너지 사용량을 파악하여 적절하게 ON/OFF 상태로 관리하는 기술이다. 이를 위해서 각 서버의 전력을 효과적으로 추정하는 방식이 필요한데, 본 논문에서는 비용 면과 에너지 면에서 효율적인 소프트웨어 방식의 추정 모델을 사용하여 전력을 추정한다. 또한 기존의 전력 추정 모델은 CPU의 유휴(idle) 사용량만을 사용함으로써 현재 서버의 세부적인 CPU 상태나 I/O 장치의 사용량을 정확히 파악하지 못하고, 이는 해당 서버의 전력을 효과적으로 추정하지 못하는 단점으로 이어진다. 본 논문에서는 CPU의 다양한 상태 필드를 활용하여 서버의 CPU 및 시스템의 전반적인 상태를 보다 정확히 파악하고, 이에 따라 서버의 전력을 기존의 두 소비전력 추정 모델(CPU/디스크/메모리 기반의 전력 소비 추정 모델 및 CPU 유휴값 기반의 전력 소비 추정 모델)보다 정확히 측정하는 CPU 필드(field) 기반의 전력 추정 모델을 제안한다. 2대의 서버를 사용하여 실험을 수행하였으며, 전력계를 통해 측정한 실제 전력과 각 추정 모델의 추정 값을 비교하여 평균 오차율을 계산하였다. 실험 결과 기존 소비전력 추정 모델이 평균 8-15%대의 오차율을 보이는 반면, 본 논문에서 제안하는 서버 전력 추정 모델은 2%대의 오차율을 보여 주었다.

• Earthquakes and Structures
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• 제11권1호
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• pp.165-178
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• 2016

The goal of energy-based seismic design is to obtain a structural design with a higher energy dissipation capacity than the energy dissipation demands incurred under earthquake motions. Accurate estimation of the story hysteretic energy demand of a multi-story structure is the key to meeting this goal. Based on the assumption of a mode-equivalent single-degree-of-freedom system, the energy equilibrium relationship of a multi-story structure under seismic action is transformed into that of a multi-mode analysis of several single degree-of-freedom systems. A simplified equation for the estimation of the story seismic hysteretic energy demand was then derived according to the story shear force and deformation of multi-story buildings, and the deformation and energy relationships between the mode-equivalent single-degree-of-freedom system and the original structure. Sites were categorized into three types based on soil hardness, namely, hard soil, intermediate hard (soft) soil, and soft soil. For each site type, a 5-story and 10-story reinforced concrete frame structure were designed and employed as calculation examples. Fifty-six earthquake acceleration records were used as horizontal excitations to validate the accuracy of the proposed method. The results verify the following. (1) The distribution of seismic hysteretic energy along the stories demonstrate a degree of regularity. (2) For the low rise buildings, use of only the first mode shape provides reasonably accurate results, whereas, for the medium or high rise buildings, several mode shapes should be included and superposed to achieve high precision. (3) The estimated hysteretic energy distribution of bottom stories tends to be underestimated, which should be modified in actual applications.

• 전력전자학회논문지
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• 제26권3호
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.