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Computational-based approach for predicting porosity of electrospun nanofiber mats using response surface methodology and artificial neural network methods
, Article Journal of Macromolecular Science, Part B: Physics ; Volume 54, Issue 11 , 2015 , Pages 1404-1425 ; 00222348 (ISSN) ; Khodaparast Haghi, A ; Kasaei, S ; Hasanzadeh, M ; Sharif University of Technology
Taylor and Francis Inc
2015
Abstract
Comparative studies between response surface methodology (RSM) and artificial neural network (ANN) methods to find the effects of electrospinning parameters on the porosity of nanofiber mats is described. The four important electrospinning parameters studied included solution concentration (wt.%), applied voltage (kV), spinning distance (cm) and volume flow rate (mL/h). It was found that the applied voltage and solution concentration are the two critical parameters affecting the porosity of the nanofiber mats. The two approaches were compared for their modeling and optimization capabilities with the modeling capability of RSM showing superiority over ANN, having comparatively lower values of...
Simulation and optimization of pulsating heat pipe flat-plate solar collectors using neural networks and genetic algorithm: a semi-experimental investigation
, Article Clean Technologies and Environmental Policy ; Volume 18, Issue 7 , 2016 , Pages 2251-2264 ; 1618954X (ISSN) ; Kargarsharifabad, H ; Abbasi Godarzi, A ; Ghofrani, A ; Shafii, M. B ; Sharif University of Technology
Springer Verlag
2016
Abstract
This research study presents an investigation on the behavior of a Pulsating Heat Pipe Flat-Plate Solar Collector (PHPFPSC) by artificial neural network method and an optimization of the parameters of the collector by genetic algorithm. In this study, several experiments were performed to study the effects of various evaporator lengths, filling ratios, inclination angles, solar radiation, and input chilled water temperature between 9:00 A.M. to 5:00 P.M., and the output temperature of the water tank, which was the output of the system, was also measured. According to the input and output information, multilayer perceptron neural network was trained and used to predict the behavior of the...
Simultaneous alpha and gamma discrimination with a phoswich detector using a rise time method and an artificial neural network method
, Article Applied Radiation and Isotopes ; Volume 154 , 2019 ; 09698043 (ISSN) ; Feghhi, S. A. H ; Moghadam, S. R ; Zamzamian, S. M ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
We used two digital methods—rise time discrimination (RTD) and an artificial neural network (ANN)—to simultaneously discriminate alpha particles and gamma rays detected by a phoswich detector (50 μm BC-400 coupled to 3 mm CsI(Tl)). The results for 10,000 pulses discriminated by the RTD method showed that the rise time distribution of the pulses is rather vast (between 200 and 800 ns for gamma rays and less than 40 ns for alpha particles). The same result was also observed in the dual-parameter diagram (pulse rise time versus area under the pulse) for an 241Am source. Then, as another approach, three pulse features—rise time, pulse height ratio, and charge ratio—were extracted from 2000...