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스컬용융법에 의한 패각을 이용한 큐빅지르코니아 단결정 성장
정진화,연석주,석정원,Jung, Jin-Hwa,Yon, Seog-Joo,Seok, Jeong-Won 한국결정성장학회 2013 韓國結晶成長學會誌 Vol.23 No.3
In this research, cubic zirconia is synthesized with a refined CaO from shells as a stabilizer through Skull melting method. The proper process time and concentration are defined by Hydration reaction to produce the refined CaO after two different treatments using 0.1 mol% of HCl respectively with Cockle shell. The highest purity of CaO is reached when the shell is immersed in 1 mol% HCl. In Hydration reaction step, the pure $Ca(OH)_2$ is produced at $45^{\circ}C$ for 24 hours. The highest purity of CaO is measured when the $Ca(OH)_2$ is treated by heat at $1200^{\circ}C$ for 5 hours. The single crystals are grown through Skull melting method by adding the different contents of the refined CaO from 10 mol% to 30 mol% into $ZrO_2$. The frequency of High-frequency oscillator used for Skull melting method is 3.4 MHz. The descending speed of the single crystal is 3 mm/hour. The grown length of the single crystal is 4 cm. As a result of this study, 15 mol% of CaO has the best crystallinity. 본 연구에서는 안정화제로, 패각으로부터 정제한 CaO를 사용하여 Skull melting법으로 큐빅 지르코니아를 성장시켰다. 꼬막 패각을 HCl을 0.1~1 mol%로 1차 처리한 후 수화반응을 거쳐 최적의 처리시간과 농도를 검토하였다. HCl을 1 mol%의 농도로 처리했을 때 가장 순도가 높았으며 수화반응시 $45^{\circ}C$의 온도로 24시간 동안 반응시킬 때 완전한 $Ca(OH)_2$ 를 얻을 수 있었다. $Ca(OH)_2$를 $1200^{\circ}C$에서 5시간 처리하였을 때 CaO의 순도가 가장 높게 측정되었다. 패각으로부터 얻어진 CaO를 $ZrO_2$에 첨가하여 함량을 10~30 mol% 로 변화시켜 Skull melting법으로 단결정을 성장시켰다. Skull melting법에 사용된 고주파 발진기의 주파수는 약 3.4 MHz이며 단결정의 하강속도는 시간당 3 mm로 4 cm 길이로 성장시켰다. 실험결과 CaO의 함량이 15 mol% 일 때 결정성이 가장 우수했다.
기계학습을 활용한 기상예측자료 기반 태양광 발전량 예측 향상기법
정진화(Jin-Hwa Jeong),채영태(Young-Tae Chae) 한국생활환경학회 2018 한국생활환경학회지 Vol.25 No.1
This study investigated a selection of machine learning model to forecast electric power output from photovoltaic arrays based on forecasted weather data and historic solar radiation data. It tested two approaches to improve forecasting accuracy of power output with three typical machine learning algorithms such as Random Forest(RF), Artificial Neural Network(ANN), and Support Vector Machine(SVM). A forecasting power output was conducted with conventional weather forecasting data from national weather service which does not include solar radiation. The other approach has two steps, forecasting solar radiation with weather forecasting data and historic solar radiation data then it forecasts the electric power output of photovoltaic arrays. It has been studied the importance variables incorporated with the power output forecasting. The results show that the forecasting accuracy of the power output improves by using forecasted solar radiation data and Random Forest outperforms on this power output forecasting problem among other machine learning algorithms.
건물유형별 에너지소비 예측성능 향상을 위한 변수중요도 및 기계학습모델 평가
정진화(Jeong, Jin-Hwa),채영태(Chae, Young-Tae) 한국건축친환경설비학회 2017 한국건축친환경설비학회 논문집 Vol.11 No.6
The optimal machine learning model depends on building types was selected by comparing and analyzing short term load forecasting (STLF) performance of primary school and commercial reference building based on 4 machine learning models such as ANN, SVM, CHAID, and, RF. The research consists of data collection-storage, data analysis, meteorological variables extraction, energy consumption forecasting and analysis on typical primary school and commercial building energy model. TMY (Typical Meteorological Year) of Incheon, Korea was applied and based on weather forecasting data provided by the KMA (Korea Meteorological Agency). In case of building energy consumption data, primary school and medium commercial reference building energy consumption data by on EIA’s Commercial Buildings Energy Consumption Survey (CBECS) were used. Key weather variables were extracted for each machine learning model between the input variables and the output which is building energy consumption in 15 minutes interval. Finally, forecasting of energy consumption on different building types conducted a comparative analysis of the forecasting performance of building energy consumption based on 4 machine learning models using optimal input variables. The results shows ANN model outperforms other models with 5.44% of CV (RMSE) for 7 days school building energy forecasting trained 8 weeks prior data. Whereas, RF model performs better than the others with 10.96% of CV (RMSE). It may be concluded that the priority of variables which have impacts on energy consumption is important and the most suitable model for energy forecasting is different by the building types.
국내 상업용 에너지다소비건물 규모별 하절기 전력 소비량과 기상 및 시간변수 영향도 분석
정진화(Jin-Hwa Jeong),정유찬(Yu-Chan Jeong),채영태(Young-Tae Chae) 한국생활환경학회 2021 한국생활환경학회지 Vol.28 No.1
Building sector takes responsible for approximately 30% of the national energy consumption in Korea. For energy monitoring and management, highly energy usage buildings which have the large service area and the high energy-use-intensity have been regulated to report the energy consumption data to the public since 2008. Although the energy data has been applicable to provide various information on the energy performance of the building, it has necessarily to characterizes the correlation between the building energy consumption and parameters which affects on the energy usage. This research collected hourly electricity consumption data of ten large commercial buildings among the national highly energy usage building list and investigated the impact of each parameters on the electricity consumption under different time resolutions, hourly and daily in summer. The results shows that daily mean outdoor air temperature is the most correlated variable and feature compared with other variables to illustrate the daily energy consumption but the larger building area is, the less R² values between the temperature and electricity consumption. In addition, the coefficient of the time flag is comparable to hourly outdoor air temperature under the correlation analysis but the parameter is the most importance feature for hourly electricity energy consumption.