1,721,010 research outputs found
Efficient Implementation of Spontaneous Calcium Oscillations in the Central Nervous System on Reconfigurable Digital Boards
No full textThis work was supported by the Deanship of Scientific Research at King Khalid University under Grant RGP.1/243/42. This article was recommended by Associate Editor P. K. Meher. (Corresponding author: Yisu Ge.
Investigation of Time-Domain Feature Selection and GMDH Neural Network Application for Determination of Volume Percentages in X-Ray-Based Two-Phase Flow Meters
No full textThis work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (RG-25-135-42). The authors, therefore, gratefully acknowledge the DSR technical and financial support
Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter
No full textFrequency Domain Feature Extraction Investigation to Increase the Accuracy of an Intelligent Nondestructive System for Volume Fraction and Regime Determination of Gas-Water-Oil Three-Phase Flows
Get PDFIn this research, a methodology consisting of an X-ray tube, one Pyrex-glass pipe, and two NaI detectors was investigated to determine the type of flow regimes and volume fractions of gas-oil-water three-phase flows. Three prevalent flow patterns—namely annular, stratified, and homogenous—in various volume percentages—10% to 80% with the step of 10%—were simulated by MCNP-X code. After simulating all the states and collecting the signals, the Fast Fourier Transform (FFT) was used to convert the data to the frequency domain. The first and second dominant frequency amplitudes were extracted to be used as the inputs of neural networks. Three Radial Basis Function Neural Networks (RBFNN) were trained for determining the type of flow regimes and predicting gas and water volume fractions. The correct detection of all flow regimes and the determination of volume percentages with a Mean Relative Error (MRE) of less than 2.02% shows that the use of frequency characteristics in determining these important parameters can be very effective. Although X-ray radiation-based two-phase flowmeters have a lot of advantages over the radioisotope-based ones, they suffer from lower measurement accuracy. One reason might be that the X-ray multi-energy spectrum recorded in the detector has been analyzed in a simple way. It is worth mentioning that the X-ray sources generate multi-energy photons despite radioisotopes that generate single energy photons, therefore data analyzing of radioisotope sources would be easier than X-ray ones. As mentioned, one of the problems researchers have encountered is the lower measurement accuracy of the X-ray, radiation-based three-phase flowmeters. The aim of the present work is to resolve this problem by improving the precision of the X-ray, radiation-based three-phase flowmeter using artificial neural network (ANN) and feature extraction techniques
Design and construction of Zana robot for modeling human player in rock-paper-scissors game using multilayer perceptron, radial basis functions and Markov algorithms
Get PDFAbstract: In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies
Application of feature extraction and artificial intelligence techniques for increasing the accuracy of X-ray radiation based two phase flow meter
Get PDFAbstract: The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow's components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works
An Experimental and Simulation Study for Comparison of the Sensitivity of Different Non-Destructive Capacitive Sensors in a Stratified Two-Phase Flow Regime
Get PDFMeasuring the volume fraction of each phase in multi-phase flows is an essential problem in petrochemical industries. One of the standard flow regimes is stratified two-phase flow, which occurs when two immiscible fluids are present in a pipeline. In this paper, we performed several experiments on vertical concave, horizontal concave, and double-ring sensors to benchmark obtained simulation results from modeling these sensors in stratified two-phase flow using COMSOL Multiphysics software. The simulation data was confirmed by experimental data. Due to the low number of data in the experimental method in order to extract more data, the mentioned software was used to extract more data and then compare the sensitivity of different directions of concave and double ring sensors. The simulation results show that the overall sensitivity of the concave is higher than the double-ring and the momentary sensitivity of the horizontal concave is higher in higher void fractions, and the vertical one has higher sensitivity in lower void fractions
Proposing an Intelligent Dual-Energy Radiation-Based System for Metering Scale Layer Thickness in Oil Pipelines Containing an Annular Regime of Three-Phase Flow
Get PDFDeposition of scale layers inside pipelines leads to many problems, e.g., reducing the internal diameter of pipelines, damage to drilling equipment because of corrosion, increasing energy consumption because of decreased efficiency of equipment, and shortened life, etc., in the petroleum industry. Gamma attenuation could be implemented as a non-invasive approach suitable for determining the mineral scale layer. In this paper, an intelligent system for metering the scale layer thickness independently of each phase’s volume fraction in an annular three-phase flow is presented. The approach is based on the use of a combination of an RBF neural network and a dual-energy radiation detection system. Photo peaks of 241Am and 133Ba registered in the two transmitted detectors, and scale-layer thickness of the pipe were considered as the network’s input and output, respectively. The architecture of the presented network was optimized using a trial-and-error method. The regression diagrams for the testing set were plotted, which demonstrate the precision of the system as well as correction. The MAE and RMSE of the presented system were 0.07 and 0.09, respectively. This novel metering system in three-phase flows could be a promising and practical tool in the oil, chemical, and petrochemical industries
Correction: Wang et al. An Optimization on the Neuronal Networks Based on the ADEX Biological Model in Terms of LUT-State Behaviors: Digital Design and Realization on FPGA Platforms. Biology 2022, 11, 1125
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Extraction of time-domain characteristics and selection of effective features using correlation analysis to increase the accuracy of petroleum fluid monitoring systems
Get PDFAbstract: In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry
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