• Solar Energy
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• v.11 no.2
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• pp.9-19
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• 1991

Lighting is one of the largest energy consumption in commercial building. For saving such lighting energy, integrated lighting system with daylight and artificial lighting has been suggested. In such system, perimeter zone can be illuminated by daylighting and the deep area of room by artificial lighting. So, the study aimed to develope of lighting energy prediction nomograph by turnning-off depth and lighting control systems during daytime. For the purpose, energy nomo-graph has been developed to apply to side-lit office building and the use and limitation of the nomograph has been discussed.

• Defense and Technology
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• no.5 s.291
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• pp.30-41
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• 2003

최근 컴퓨터기술의 발전과 더불어 등장한 컴퓨터모델링기법을 이용하여 현재는 존재하지 않으나 합성에 의해 만들어낼수 있는 새로운 분자들을 예측할 수 있게되었다. 이러한 기술을 에너지물질의 설계에 적용하려는 시도가 1890년대부터 시작되어, 새로운 고밀도 고에너지물질 분자들이 이론적으로 존재 가능하다는 연구결과가 속속 보고되고 있다. 에너지물질 분자들의 주요 구성원소는 C, H, N, O 등인데, 이들 원자들로부터 에너지효율을 극대화하기 위하여 선진국을 위시한 세계 여러 나라의 이론화학자들이 양자역학 이론에 바탕을 둔 ab initio 계산이 주가 되는 분자모델링 기법을 이용하여 새로운 분자들의 존재가능성을 예측하려는 연구가 활발하게 이루어지고 있다. 이러한 새로운 고에너지 물질을 찾으려는 노력의 일환으로 순수한 질소 원자들로만 이루어진 분자들, 일명 질소클러스터(Nitrogen Clusters)에 대한 연구가 진행되고 있는데, N4에서 N60까지 다양한 개수의 질소원자로 이루어진 질소크러스터의 존재가능성이 이론적으로 확인되고 있고, $N5^+$가 최근 합성되는 등 이들 새로운 초 고에너지의 분자들의 출현이 기대된다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1042-1043
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• 2015

미니트램(Mini-Tram)은 승하차 시간 동안 정거장에서의 무선급전 방식에 의한 급속충전(약, 2분 30초 이내)을 통해 에너지를 공급받으며, 운행 시 차상에 설치된 에너지저장 매체(LIC: Lithium-Ion Capacitor)로부터 전기에너지를 공급받아서 운행된다. 차량에 요구되는 에너지 소모량을 분석하고 이에 맞춰서 저장 매체의 직-병렬 구성 및 전력 변환 장치(Regulator 및 DC-DC 컨버터 등)를 설계하는 것은 전기 구동 차량인 미니트램의 설계에서 매우 중요한 부분이다. 본 논문에서는 미니트램 시스템을 개발하는 과정에서 수행된 에너지공급시스템의 사양 선정을 위한 에너지 소모량 예측과 더불어 이와 관련된 주행저항 및 주행성능 분석에 대해서 다룬다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Composites Research
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• v.26 no.3
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• pp.175-181
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• 2013

The evaluation and prediction for the absorbed energy, residual velocity, and impact damage are the key things to characterize the impact behavior of composite laminated panel subjected to high-velocity impact. In this paper, the method to predict the residual velocity and the absorbed energy of Carbon/Epoxy laminated panel subjected to high velocity impact are proposed and examined by using quasi-static perforation test and high-velocity impact test. Total absorbed energy of specimen due to the high-velocity impact can be grouped with static energy and kinetic energy. The static energy are consisted of energy due to the failure of the fiber and matrix and static elastic energy, which are related to the quasi-static perforation energy. The kinetic energy are consisted of kinetic energy of moving part of specimen, which are modelled by three modified kinetic model. The high-velocity impact test were conducted by using air gun impact facility and compared with the predicted values. The damage area of specimen were examined by C-scan image. In the high initial impact velocity above the ballistic limit, both the static energy and the kinetic energy are known to be the major contribution of the total absorbed energy.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1994.05a
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• pp.78-85
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• 1994

ASPEN(Advanced System Process ENgineering) 코드를 이용하여 오리멀젼(orimulsion)을 이용한 가스화기의 성능을 예측하고, 발전 연료로서의 적합성 등을 살펴보았다. 오리멀젼은 역청(bitumen)에 물을 섞은 연료로 중유와 석탄의 중간 정도의 성질을 가지고 있다. 본 연구에서는 오리멀젼 가스화기의 운전 특성을 파악하고 주입되는 산화제 양을 변화시켜 산화제의 가스화기 운전온도에 미치는 영향과 생성되는 주요 합성가스 성분에 미치는 영향 등을 예측하고 새로운 연료로서의 타당성 여부를 검토하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.457-459
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• 2018

Solar power generation system is a energy generation technology that produces electricity from solar power, and it is growing fastest among renewable energy technologies. It is of utmost importance that the solar power system supply energy to the load stably. However, due to unstable energy production due to weather and weather conditions, accurate prediction of energy production is needed. In this paper, an Artificial Neural Network(ANN) that predicts solar energy using 15 kinds of meteorological data such as precipitation, long and short wave radiation averages and temperature is implemented and its performance is evaluated. The ANN is constructed by adjusting hidden parameters and parameters such as penalty for preventing overfitting. In order to verify the accuracy and validity of the prediction model, we use Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) as performance indices. The experimental results show that MAPE = 19.54 and MAE = 2155345.10776 when Hidden Layer $Sizes=^{\prime}16{\times}10^{\prime}$.

• The Journal of the Acoustical Society of Korea
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• v.15 no.5
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• pp.44-52
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• 1996

Acoustical characteristics of internal pipe structures and a loudspeaker of the thermoacoustic refrigerator are analyzed by using the transfer matrix method. The resonator system is dismantled into verious basic acoustic elements, and then linearized transfer matrices are serially combined with the dynamical system of linearized loudspeaker model, that the total system of thermoacoustic refrigerator can be analyzed in terms of frequency characteristics and acoustic field shape. Additionally, by using equations for energy flow through the capillary stack, the temperature distribution over the stack is numerically estimated. After expressing the acoustic work flow, thermoacoustic flow, and energy loss per unit length in a single capillary duct by using the transverse functional variations, overall energy flow rate and energy balance are obtained for the whole capillary stack. The final expression for energy flow through the stack is numerically evaluated by varying physical parameters obtained from the sound field analysis. After confirming good agreements between predicted and experimental results for the interior sound field of a refrigerator model, the thermoacoustic characteristics of Hofler's apparatus is analyzed by the proposed method and it is observed that the results agree well with Hofler's experimental results.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Korean Journal of Applied Biomechanics
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• v.19 no.1
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• pp.179-186
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• 2009

This works studied to compare gas analyzer with accelerometer and the estimation of energy expenditure based on different attaching position of tri-axial accelerometer such as waist and top of the foot Based on the fact that oxygen intake increases more radically linearly during walking more than 8.0km/hr. 9 male subjects performed walking and running on the treadmill with speed of $1.5{\sim}8.5km$/hr and $4.5{\sim}13.0km$/hr, respectively. Commercially available Nike + iPod Sports kit was used to compare energy expenditure with sensor module attached to their foot. Actual energy expenditure was determined by a continuous direct gas analyzer and two multiple regression equations of walking and running mode for different attaching position were developed. Results showed that estimation accuracy of energy expenditure using waist mounted accelerometer was higher than that of the top of the foot and Nike + iPod Sports kit. Results of energy expenditure based on waist and top of the foot showed that the crossover state of energy expenditure occurred at 7.5km/hr. But Nike + iPod Sports kit could not find intersection of energy expenditure in all nine subjects. Therefore the sensor module attached to the waist and separate multi regression equation by walking and running mode was the best to estimate more accurate prediction.