http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : R. Gholipour Peyvandi (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
R. Gholipour Peyvandi,S.Z. Islami rad 한국원자력학회 2018 Nuclear Engineering and Technology Vol.50 No.7
Precise prediction of the radiation interaction position in scintillators plays an important role in medicaland industrial imaging systems. In this research, the incident position of the gamma rays was predictedprecisely in a plastic rod scintillator by using attenuation technique and multilayer perceptron (MLP)neural network, for the first time. Also, this procedure was performed using nonlinear regression (NLR)method. The experimental setup is comprised of a plastic rod scintillator (BC400) coupled with two PMTsat two sides, a 60Co gamma source and two counters that record count rates. Using two proposedtechniques (ANN and NLR), the radiation interaction position was predicted in a plastic rod scintillatorwith a mean relative error percentage less than 4.6% and 14.6%, respectively. The mean absolute errorwas measured less than 2.5 and 5.5. The correlation coefficient was calculated 0.998 and 0.984,respectively. Also, the ANN technique was confirmed by leave-one-out (LOO) method with 1% error. These results presented the superiority of the ANN method in comparison with NLR and the othermethods. The technique and set up used are simpler and faster than other the previous position sensitivedetectors. Thus, the time, cost and shielding and electronics requirements are minimized and optimized.
-
E. Nazemi,S.A.H. FEGHHI,G.H. Roshani,R. Gholipour Peyvandi,S. Setayeshi 한국원자력학회 2016 Nuclear Engineering and Technology Vol.48 No.1
Void fraction is an important parameter in the oil industry. This quantity is necessary forvolume rate measurement in multiphase flows. In this study, the void fraction percentagewas estimated precisely, independent of the flow regime in gaseliquid two-phase flows byusing g-ray attenuation and a multilayer perceptron neural network. In all previous studiesthat implemented a multibeam g-ray attenuation technique to determine void fractionindependent of the flow regime in two-phase flows, three or more detectors were usedwhile in this study just two NaI detectors were used. Using fewer detectors is of advantagein industrial nuclear gauges because of reduced expense and improved simplicity. In thiswork, an artificial neural network is also implemented to predict the void fraction percentageindependent of the flow regime. To do this, a multilayer perceptron neuralnetwork is used for developing the artificial neural network model in MATLAB. Therequired data for training and testing the network in three different regimes (annular,stratified, and bubbly) were obtained using an experimental setup. Using the techniquedeveloped in this work, void fraction percentages were predicted with mean relative errorof <1.4%.
-
Nazemi, E.,Feghhi, S.A.H.,Roshani, G.H.,Gholipour Peyvandi, R.,Setayeshi, S. Korean Nuclear Society 2016 Nuclear Engineering and Technology Vol.48 No.1
Void fraction is an important parameter in the oil industry. This quantity is necessary for volume rate measurement in multiphase flows. In this study, the void fraction percentage was estimated precisely, independent of the flow regime in gas-liquid two-phase flows by using ${\gamma}-ray$ attenuation and a multilayer perceptron neural network. In all previous studies that implemented a multibeam ${\gamma}-ray$ attenuation technique to determine void fraction independent of the flow regime in two-phase flows, three or more detectors were used while in this study just two NaI detectors were used. Using fewer detectors is of advantage in industrial nuclear gauges because of reduced expense and improved simplicity. In this work, an artificial neural network is also implemented to predict the void fraction percentage independent of the flow regime. To do this, a multilayer perceptron neural network is used for developing the artificial neural network model in MATLAB. The required data for training and testing the network in three different regimes (annular, stratified, and bubbly) were obtained using an experimental setup. Using the technique developed in this work, void fraction percentages were predicted with mean relative error of <1.4%.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
