Sharif Digital Repository

Autoregressive models are among the most successful neural network methods for estimating a distribution from a set of samples. However, these models, such as other neural methods, need large data sets to provide good estimations. We believe that knowing structural information about the data can improve their performance on small data sets. Masked autoencoder for distribution estimation (MADE) is a well-structured density estimator, which alters a simple autoencoder by setting a set of masks on its connections to satisfy the autoregressive condition. Nevertheless, this model does not benefit from extra information that we might know about the structure of the data. This information can... 

Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),... 

Determination of different facies in an underground reservoir with the aid of various applicable neural network methods can improve the reservoir modeling. Accordingly facies identification from well logs and cores data information is considered as the most prominent recent tasks of geological engineering. The aim of this study is to analyze and compare the five artificial neural networks (ANN) approaches with identification of various structures in a rock facies and evaluate their capability in contrast to the labor intensive conventional method. The selected networks considered are Backpropagation Neural Networks (BPNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN),... 

Autoregressive models are among the most successful neural network methods for estimating a distribution from a set of samples. However, these models, such as other neural methods, need large data sets to provide good estimations. We believe that knowing structural information about the data can improve their performance on small data sets. Masked autoencoder for distribution estimation (MADE) is a well-structured density estimator, which alters a simple autoencoder by setting a set of masks on its connections to satisfy the autoregressive condition. Nevertheless, this model does not benefit from extra information that we might know about the structure of the data. This information can... 

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... 

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... 

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...