离散小波变换
人工神经网络
特征(语言学)
标准差
计算机科学
均方误差
探测器
体积热力学
蒙特卡罗方法
多层感知器
小波
小波变换
算法
人工智能
数学
统计
物理
量子力学
电信
语言学
哲学
作者
Mohammed Balubaid,Mohammad Amir Sattari,Osman Taylan,Ahmed A. Bakhsh,Ehsan Nazemi
出处
期刊:Mathematics
[MDPI AG]
日期:2021-12-13
卷期号:9 (24): 3215-3215
被引量:10
摘要
This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction.
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