scholarly journals Application of Feature Extraction and Artificial Intelligence Techniques for Increasing the Accuracy of X-ray Radiation Based Two Phase Flow Meter

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1227
Author(s):  
Abdulrahman Basahel ◽  
Mohammad Amir Sattari ◽  
Osman Taylan ◽  
Ehsan Nazemi
Keyword(s):  
Neural Network ◽  
Volume Fraction ◽  
Detection System ◽  
Absolute Error ◽  
Percentage Error ◽  
Two Phase ◽  
Flow Measurements ◽  
X Ray ◽  
The Time Domain ◽  
Neural Network Input

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.

scholarly journals Optimization of X-ray Tube Voltage to Improve the Precision of Two Phase Flow Meters Used in Petroleum Industry

2021 ◽  
Vol 13 (24) ◽  
pp. 13622
Author(s):  
Abdullah K. Alanazi ◽  
Seyed Mehdi Alizadeh ◽  
Karina Shamilyevna Nurgalieva ◽  
John William Grimaldo Guerrero ◽  
Hala M. Abo-Dief ◽  
...  
Keyword(s):  
Two Phase Flow ◽  
Detection System ◽  
Petroleum Industry ◽  
Flow Characteristics ◽  
Absolute Error ◽  
Phase Flow ◽  
Tube Voltage ◽  
Two Phase ◽  
X Ray ◽  
Feature Extraction Method

To the best knowledge of the authors, in all the former studies, a fixed value of X-ray tube voltage has been used for investigating gas–liquid two-phase flow characteristics, while the energy of emitted X-ray radiations that depends on the tube voltage can significantly affect the measurement precision of the system. The purpose of present study is to find the optimum tube voltage to increase the accuracy and efficiency of an intelligent X-ray radiation-based two-phase flow meter. The detection system consists of an industrial X-ray tube and one detector located on either side of a steel pipe. Tube voltages in the range of 125–300 kV with a step of 25 kV were investigated. For each tube voltage, different gas volume percentages (GVPs) in the range of 10–90% with a step of 5% were modeled. A feature extraction method was performed on the output signals of the detector in every case, and the obtained matrixes were applied to the designed radial basis function neural networks (RBFNNs). The desired output of the networks was GVP. The precision of the networks in every voltage and every number of neurons in the hidden layer were obtained. The results showed that 225 kV tube voltage is the optimum voltage for this purpose. The obtained mean absolute error (MAE) for this case is less than 0.05, which demonstrates the very high precision of the metering system with an optimum X-ray tube voltage.

scholarly journals A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3653
Author(s):  
Uddin ◽  
Zeb ◽  
Zeb ◽  
Ishfaq ◽  
Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Power System ◽  
Performance Indicator ◽  
Stable Condition ◽  
External Perturbation ◽  
Absolute Error ◽  
Unified Power Flow Controller ◽  
Two Phase ◽  
Model Reference

In this paper, a model reference controller (MRC) based on a neural network (NN) is proposed for damping oscillations in electric power systems. Variation in reactive load, internal or external perturbation/faults, and asynchronization of the connected machine cause oscillations in power systems. If the oscillation is not damped properly, it will lead to a complete collapse of the power system. An MRC base unified power flow controller (UPFC) is proposed to mitigate the oscillations in 2-area, 4-machine interconnected power systems. The MRC controller is using the NN for training, as well as for plant identification. The proposed NN-based MRC controller is capable of damping power oscillations; hence, the system acquires a stable condition. The response of the proposed MRC is compared with the traditionally used proportional integral (PI) controller to validate its performance. The key performance indicator integral square error (ISE) and integral absolute error (IAE) of both controllers is calculated for single phase, two phase, and three phase faults. MATLAB/Simulink is used to implement and simulate the 2-area, 4-machine power system.

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

A method for predicting the capacity of a lithium-ion battery based on deep neural network

2021 ◽  
Author(s):  
JamesChan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Lithium Ion ◽  
Chemical Conversion ◽  
Absolute Error ◽  
Production Costs ◽  
Percentage Error ◽  
Average Percentage ◽  
True Value ◽  
Battery Capacity

This paper proposes a solution to predict the capacity of the lithium-ion battery's capacity division process using deep learning methods. This solution extracts the physical observation records of part of the process steps from the chemical conversion and volumetric processes as features, and trains a Deep Neural Network (DNN) to achieve accurate prediction of battery capacity. According to the test, the average percentage absolute error (Mean Absolute Percentage Error, MAPE) of the battery capacity predicted by this model is only 0.78% compared with the true value. Combining this model with the production line can greatly reduce production time and energy consumption, and reduce battery production costs.

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Ali M. Al-Salihi ◽  
Zahraa A. AL-Ramahy
Keyword(s):  
Neural Network ◽  
Soil Temperature ◽  
Hydrological Cycle ◽  
Absolute Error ◽  
Training Data ◽  
Percentage Error ◽  
Ann Model ◽  
Temperature Estimation ◽  
Data Set ◽  
Daily Data

Soil temperature is an important meteorological variable which plays a significant role in hydrological cycle. In present study, artificial intelligence technique employed for estimating for 3 daysa head soil temperature estimation at 10 and 20 cm depth. Soil temperature daily data for the period 1 January 2012 to 31 December 2013 measured in three stations namely (Mosul, Baghdad and Muthanna) in Iraq. The training data set includes 616 days and the testing data includes 109 days. The Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian regularization algorithms. To evaluate the ANN models, Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE) and Correlation Coefficient (r) were determined. According to the four statistical indices were calculated of the optimum ANN model, it was ANN model (3) in Muthanaa station for the depth 10 cm and ANN model (3) in Baghdad station for the depth 20 were (RMSE=0.959oC, MAE=0.725, MAPE=4.293, R=0.988) and (RMSE=0.887OC, MAE=0.704, MAPE=4.239, R=0.993) respectively, theses statistical criteria shown the efficiency of artificial neural network for soil temperature estimation.

scholarly journals Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

2020 ◽  
Vol 10 (15) ◽  
pp. 5160
Author(s):  
Saad Sh. Sammen ◽  
Mohammad Ali Ghorbani ◽  
Anurag Malik ◽  
Yazid Tikhamarine ◽  
Mohammad AmirRahmani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Metrics ◽  
Absolute Error ◽  
Hybrid Models ◽  
Percentage Error ◽  
Scour Depth ◽  
Box Plots ◽  
Taylor Diagram ◽  
Artificial Neural

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

Estimation of structural response using convolutional neural network: application to the Suramadu bridge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arya Panji Pamuncak ◽  
Mohammad Reza Salami ◽  
Augusta Adha ◽  
Bambang Budiono ◽  
Irwanda Laory
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Structural Response ◽  
Force Sensor ◽  
Absolute Error ◽  
Percentage Error ◽  
Learning Approaches ◽  
Content Type ◽  
Cable Force

PurposeStructural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.Design/methodology/approachThe CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.FindingsThe CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.Originality/valueThis work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

scholarly journals Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

2021 ◽  
Vol 56 (2) ◽  
pp. 235-248
Author(s):  
Fatchul Arifin ◽  
Herjuna Artanto ◽  
Nurhasanah ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Network Models ◽  
Single Shot ◽  
Neural Network Models ◽  
X Ray ◽  
Detection Model ◽  
Shot Detection ◽  
Average Accuracy ◽  
Chest X Ray

COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

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