Accurate Artificial Neural Network Research Articles

Predicting hydrogen segregation energy distributions in strained regions of tungsten using artificial neural network

Employing the smooth overlap of atomic position (SOAP) descriptors, we established an artificial neural network (ANN) model with the ability to effectively and accurately predict the segregation energy Eseg distributions of hydrogen (H) atoms in various strained regions of tungsten (W). The model is verified to have comparable accuracy with the molecular statics (MS) relaxations, as quantitively evidenced by the root mean square error (RMSE) values of 0.018 eV for the homogeneously strained regions, averagely 0.025 eV for regions near different dislocations, and 0.032 eV for regions surrounding an interstitial H cluster. Besides, the predictions yield no systematic bias at both high and low Eseg extremes, which is a common issue in the elastic theory (ET) calculations. Moreover, Eseg values are precisely predicted with the RMSE value of 0.041 eV surrounding an interstitial H cluster that is not encountered during the learning phase of the model, demonstrating the model’s robust generalization capability. Considering its markedly enhanced computation speed, this accurate ANN model holds promising prospects in circumstances that require massive Eseg calculations.

Machine-Learning-Based Ground-Level Mobile Network Coverage Prediction Using UAV Measurements

Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous in hard-to-reach areas. An alternative safe method involves using drones or unmanned aerial vehicles (UAVs). The objective of this study was to use a drone to measure signal strength at discrete points a few meters above the ground and an artificial neural network (ANN) for processing the measured data and predicting signal strength at ground level. The drone was equipped with low-cost data logging equipment. The ANN was also used to classify specific ground locations in terms of signal coverage into poor, fair, good, and excellent. The data used in training and testing the ANN were collected by a measurement unit attached to a drone in different areas of Sultan Qaboos University campus in Muscat, Oman. A total of 12 locations with different topologies were scanned. The proposed method achieved an accuracy of 97% in predicting the ground level coverage based on measurements taken at higher altitudes. In addition, the performance of the ANN in predicting signal strength at ground level was evaluated using several test scenarios, achieving less than 3% mean square error (MSE). Additionally, data taken at different angles with respect to the vertical were also tested, and the prediction MSE was found to be less than approximately 3% for an angle of 68 degrees. Additionally, outdoor measurements were used to predict indoor coverage with an MSE of less than approximately 6%. Furthermore, in an attempt to find a globally accurate ANN module for the targeted area, all zones’ measurements were cross-tested on ANN modules trained for different zones. It was evaluated that, within the tested scenarios, an MSE of less than approximately 10% can be achieved with an ANN module trained on data from only one zone.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼103) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

Effective plasma transport modeling of magnetically confined fusion devices relies on having an accurate understanding of the ion composition and radiative power losses of the plasma. Generally, these quantities can be obtained from solutions of a collisional-radiative (CR) model at each time step within a plasma transport simulation. However, even compact, approximate CR models can be computationally onerous to evaluate, and in-situ evaluation of these models within a larger plasma transport code can lead to a rigid bottleneck. As a way to bypass this bottleneck, we propose deploying artificial neural network (ANN) surrogates to allow rapid evaluation of the necessary plasma quantities. However, one issue with training an accurate ANN surrogate is the reliance on a sufficiently large and representative training and validation data set, which can be time-consuming to generate. In this work we explore a data-driven active learning and training routine to allow autonomous adaptive sampling of the problem parameter space to ensure a sufficiently large and meaningful set of training data is assembled for the network training. As a result, we can demonstrate approximately order-of-magnitude savings in required training data samples to produce an accurate surrogate.

Upscaling Shear Strength of Heterogeneous Oil Sands with Interbedded Shales Using Artificial Neural Network

SummaryUnderstanding the shear strength of caprock shale and oil sands is important in risk assessment of slope stability in open-pit mining, caprock integrity of in-situ thermal recovery, and optimization of bitumen production from oil sands. A robust and efficient upscaling technique is essential to model the impact of heterogeneity on the deformation and failure of oil sands and caprock shale. Although conventional analytical and numerical upscaling techniques are available, many of these methods consider oversimplified assumptions and have high computational costs, especially when considering the impact of spatially correlated interbedded shales on the shear strength. A machine learning enhanced upscaling (MLEU) technique that leverages the accuracy of local numerical upscaling and the efficiency of artificial neural network (ANN) is proposed here. MLEU uses a fast and accurate ANN proxy model to predict the anisotropic shear strength of heterogeneous oil sands with interbedded shales. The R2 values of the trained ANN models exceed 0.94 for estimating shear strengths in horizontal and vertical directions. The deviation of upscaled shear strength from numerical upscaled results is improved by 12–76% compared with multivariate regression methods like response surface methodology (RSM) and polynomial chaos expansion (PCE). In terms of computational efficiency, the proposed MLEU method can save computational effort by two orders of magnitude compared with numerical upscaling. MLEU provides a reasonable estimate of anisotropic shear strength while considering uncertainties caused by different distributions of shale beddings. With the increasing demand for regional scale modeling of geomechanical problems, the proposed MLEU technique can be extended to other geological settings, where weak beddings play a significant role and the impact of heterogeneity on shear strength is important.

Statistical and Artificial Neural Network Analysis for Corrosion of Mild Steel in Hydrochloric Acid in Presence of Eco-Friendly Inhibitor

In present work, the inhibition performance of the Portulaca Grandiflora Leaf Extract (PGL), as environmental-friendly corrosion inhibitor, for low-carbon steel in 0.5 M hydrochloric acid solution at variable inhibitor concentrations and temperatures is evaluated by mass loss technique. The dependent variable was corrosion rate, while the independent variables were inhibitor concentration and temperatures. Several mathematical and artificial neural network (ANN) models have been suggested. A computer aided program is used during regression and estimation processes. Several models were used. Polynomial – individual effect, polynomial – interaction effect, linear effect, exponential growth, and piecewise regression models were estimated. Results show that the Piecewise regression model was the best one with high correlation coefficient (R2) equal to 0.9994. For ANN studies, Linear Model (LM), Radial Basis Function (RBF), and Multi-Layer Perceptron (MLP) were evaluated. The data were divided into training and testing. MLP of two inputs, multi-hidden layers, and one output (2:2-8-1:1) was the highly accurate artificial neural network model ANN with a high correlation coefficient (R2=0.9826). The effect of temperature was lower than the effect of PGL concentration as shown by mathematical and ANN analysis.

Fast design and optimization method for an ultra-wideband perfect absorber based on artificial neural network acceleration

In recent years, ultra-wideband perfect absorbers (UWPAs) based on metamaterial nanostructures have been widely studied due to their excellent performance. However, designing and optimizing the absorber quickly and accurately remains challenging in the broad-band range. In this work, by adopting the genetic algorithm combined with artificial neural network (ANN) acceleration, an effective method with the global search ability and extremely fast optimization speed is developed, which only requires an accurate forward ANN model and offers tremendous controls to the designer. In the context of the optimization of the nano-multilayers absorber, the most of mean absolute errors (MAEs) of ANN between the actual spectral absorptance and the predicted spectral absorptance are less than 1E-2, demonstrating the outstanding consistency. What's more, the optimization speed is four orders of magnitude faster than conventional electromagnetic simulation software, 1.2 × 105 search calculations take only 573 s. Meanwhile, we also show that the hybridization between multilayer cavity plasmonic resonant modes is the main cause of the ultra-wideband absorptance. This method significantly increases the viability of more complex ultra-wideband perfect absorber designs and provides opportunities to manipulate light-matter interactions in the future.

Predicting compressive strength of manufactured-sand concrete using conventional and metaheuristic-tuned artificial neural network

Compressive strength (CS) is the maximum resistance of concrete against axial compressive loading in standard conditions. Estimation of this parameter is essential for the proper design of concrete mixture. Considering the complexity of this task as a burden for traditional approaches, machine learning models like artificial neural network (ANN) have been successfully used for analyzing the nonlinear relationship between the CS and concrete ingredients. This study implements two ANN-based scenarios to approximate the uniaxial CS of manufactured-sand concrete. First, the ANN is trained by nine regular algorithms, and the best one is selected to represent the conventional ANN (CNN). For the second scenario, two improved ANNs are created with metaheuristic algorithms, namely biogeography-based optimization (BBO) and multi-tracker optimization algorithm (MTOA). The first scenario revealed that Levenberg-Marquardt is the strongest regular trainer. Comparing the performance of the CNN with hybrid models showed that both BBO and MTOA can construct a more accurate ANN. In this sense, root mean square error of the CNN experienced 8.77 and 8.84% reduction in the training phase, and more effectively, 13.05 and 11.46% in the testing phase by applying the BBO and MTOA, respectively. Hence, the suggested hybrids can act as promising alternatives to traditional models for predicting the CS of concrete. Two explicit formulas optimized by the MTOA and BBO are derived for practical applications. Also, importance analysis revealed the high contribution of curing age and water to binder ratio to the compressive behavior of the concrete.

Finite element analysis, prediction, and optimization of residual stresses in multi-pass arc welding with experimental evaluation

Prediction and reduction of unwanted tensile Residual Stress of welded stainless-steel plates is presented in this paper. Validated finite element analysis and Artificial Neural Network (ANN) is employed to simulate and mathematically model the process, respectively. Taguchi design of experiments tool is utilized to generate input data for finite element analyses and also to choose the most accurate ANN structures. RSs are minimized using three methods: Taguchi suggestion, Comprehensive factorial search, and Particle Swarm Optimization, whose accuracy and response pace increases and decreases respectively in this order. Furthermore, adding and removing extra weld lines was proposed to reduce unwanted residual stresses by up to 50%. Finally, the shapes and amounts of results are experimentally verified using contour method and proposed novel application of roughness testing. Micro-grain structures of the welded samples were also investigated, and RSs were discussed considering metallography images.

Computational analysis of axially loaded thin-walled rectangular concrete-filled stainless steel tubular short columns incorporating local buckling effects

This paper investigates the structural performance of concrete-filled stainless steel tubular (CFSST) columns composed of rectangular and square sections. A fiber-based mathematical model is developed to simulate the nonlinear performance of such columns loaded concentrically accounting for the local buckling of steel tube. An accurate lateral pressure model, as well as a strength degradation parameter are proposed based on the existing test results and incorporated in the mathematical modeling developed. A large test dataset is used to validate the accuracy of the numerical prediction. The mathematical model is employed to study the sensitivities of important column parameters on their axial behavior. The accuracy of the existing confinement model of rectangular CFST columns is evaluated in predicting the performance of CFSST short columns. Furthermore, the accuracy of the existing codified design models as well as and the simplified design model proposed in this study to quantify the ultimate compressive strength of such columns is investigated. An accurate artificial neural network (ANN) model along with the GUI feature is developed for the design engineers as a tool to predict their ultimate axial strengths.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

Role of Grain Size and Oxide Dispersion Nanoparticles on the Hot Deformation Behavior of AA6063: Experimental and Artificial Neural Network Modeling Investigations

The hot deformation behavior of coarse-grained (CG), ultrafine-grained (UFG), and oxide dispersion-strengthened (ODS) AA6063 is experimentally recognized though carrying out compression tests at different temperatures (300–450 °C) and strain rates (0.01–1 s−1). Microstructural studies conducted by TEM and EBSD indicate that dynamic softening mechanisms including dynamic recovery and dynamic recrystallization become operative in all the investigated materials depending on the regime of deformation. Moreover, the high temperature flow behavior is considerably influenced by the initial grain structure and the presence of reinforcement particles. The constitutive and artificial neural network (ANN) models were used to study the high-temperature flow behavior of the investigated alloys. To establish an accurate ANN model, material characteristics along with the processing parameters are deliberated. An Arrhenius type constitutive model with a strain-compensation term is employed to predict the flow stress of AA6063 alloys. The relative error associated with the constitutive and ANN models in the prediction of the flow stress is obtained 9.56% and 2.02%, respectively. The analysis indicates that the developed ANN model is more accurate in the prediction of flow stress with at least 78% less error in comparison to the constitutive model.

Estimation of (n,p) reaction cross sections at 14.5 [formula omitted] MeV neutron energy by using artificial neural network

The aim of this study is to develop an accurate artificial neural network algorithm for the cross-section of (n,p) reactions at 14.5 ∓0.5 MeV neutron energy which is important to developing materials for fusion reactor design. The experimental data used at artificial Neural network calculations have been taken from the Experimental Nuclear Reaction Data (EXFOR) database. Bayesian algorithm has been used at training section of artificial neural network. Regression (R) values of artificial neural network calculations have been found as 0.99363, 0.98574 and 0.99257 for training, testing and all process respectively. In addition to artificial neural network calculations, TALYS 1.95 nuclear reaction code has been used to reproduce (n,p) reactions at 14.5 ∓0.5 MeV. Two-component exciton model and Constant Temperature Fermi Gas Model have been used as pre-equilibrium and level density models respectively. Mean square errors of our calculations have been found 48.51 and 495.06 for artificial neural network and TALYS 1.95 respectively. Artificial Neural network estimations have been compared and analyzed with the TALYS 1.95 calculations and the experimental data taken from EXFOR database.

Estimations of level density parameters by using artificial neural network for phenomenological level density models

The main aim of this study is to develop accurate artificial neural network (ANN) algorithms to estimate level density parameters. An efficient Bayesian-based algorithm is presented for classification algorithms. Unknown model parameters are estimated using the observed data, from which the Bayesian-based algorithm is predicted. This paper focuses on the Bayesian method for parameter estimations of Gilbert Cameron Model (GCM), Back Shifted Fermi Gas Model (BSFGM) and Generalised Super Fluid Model (GSM), which are known as the phonemological level density models. Obtained level density parameters have been compared with the Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations (RIPL) data. R values of the Bayesian method have been found as 0.9946, 0.9981 and 0.9824 for BSFGM, GCM and GSM, respectively. In order to validate our results, default level density parameters of TALYS 1.95 code have been changed with our newly obtained results and photo-neutron cross-section calculations of the 117Sn(γ,n)116Sn, 118Sn(γ,n)117Sn, 119Sn(γ,n)118Sn and 120Sn(γ,n)119Sn reactions have been calculated by using these newly obtained level density parameters.

Reliability prediction analysis of aspect-oriented application using soft computing techniques

Abstract Accurate estimation is the foremost goal of any forecasting model. Software reliability is one of the leading research issues of the software organization. Recently, various applications of soft computing technology have attempted. Software reliability is one of the quantitative indicators of software quality. The Software Reliability Growth Model (SRGM) is used to evaluate the reliability obtained in different stages of testing. The reliability of the software depends on several factors (extensibility, sustainability, design stability, and configurability). Moreover, these factors are related to each other and directly or indirectly affect the software development process. Among various soft computing technologies, models based on artificial neural networks are well known, which extremely needs more research work and endeavors to discover the most reasonable model for software quality. The purpose of this paper is to examine the reliability of the software using soft computing techniques, which is the most efficiently used tool to evaluate its predictive power. It provides a new comparative analysis to find the most suitable and accurate artificial neural network based on the software reliability model. To answer this question, we need to evaluate the model. We use MMRE, SD, RSD, and PRED (N) to analyze the performance of the competitive model. The applicability of a particular soft computing technique is an open question because it depends mostly on the nature and characteristics of the problem. Every software project has its development behavior and complexity model. Therefore, more research is needed to predict and analysis of the results.

A Multi-Level World Comprehensive Neural Network Model for Maximum Annual Solar Irradiation on a Flat Surface

With the growing demand for clean and economically feasible renewable energy, solar photovoltaic (PV) system usage has increased. Among many factors, the tilt and azimuth angles are of great importance and influence in determining the photovoltaic panel’s efficiency to generate electricity. Although much research was conducted related to solar PV panels’ performance, this work critically determined the tilt and azimuth angles for PV panels in all countries worldwide. The optimum tilt and azimuth angles are estimated worldwide by the photovoltaic geographic information system (PVGIS). Also, annual and average daily solar irradiation incident on the tilted and oriented plate optimally (AR1 and DR1) are calculated. Besides, annual and average daily solar irradiation incident on plate tilt optimally and oriented because of the south in the northern hemisphere and because of the north in the southern hemisphere (AR2 and DR2) are estimated. PVGIS is also used to calculate the annual and average daily solar irradiation incident on the horizontal plate (AR3 and DR3). The data collected from PVGIS are used to develop an efficient and accurate artificial neural network model based on feed-forward neural network approach. This model is an essential subpart that can be used in an embedded system or an online system for further PV system analysis and optimization. The developed neural model reflected very high accuracy in predicting the PV panels’ optimal tilt and azimuth angles worldwide. The benefit of tilting is generally increased by increasing the latitude. As the latitude increases, the tilt factor (F) increases because of the increase in the optimum tilt angle by increasing the latitude. The optimal orientation is due to the north in the southern hemisphere and due to the south in the northern hemisphere for most cities worldwide. In sum, it can be concluded that the optimum tilt angle is equal to or greater than the latitude until the latitude 30°. The optimum tilt angle becomes less than the latitude, and the difference is increased until it reaches more than 20°. Hence in this study the aim is to develop a simple neural network model which can accurately predict the annual radiation and optimum tilt and azimuth angle in any region of the world and can be easily implemented in a low-cost microcontroller.

An ANN-Based Synthesis Method for Nonuniform Linear Arrays Including Mutual Coupling Effects

This paper proposes an artificial neural network (ANN)-based synthesis method for nonuniform linear arrays with mutual coupling effects. The proposed method can simultaneously optimize the location distributions and excitations of the elements. As is well known, for nonuniform linear arrays, the mutual coupling effects on the active element patterns (AEPs) and passive S parameters vary with the location distribution of the elements. However, few papers have focused on how to describe the relationship between the effects of mutual coupling and the location distribution of the elements. In this paper, we use several parallel and independent ANNs to characterize the effects of mutual coupling on the AEPs and passive S parameters in nonuniform linear arrays with variable location distributions. At the same time, to relieve the curse of dimensionality, we make use of the idea of subarrays, which makes it possible for the proposed method to model nonuniform linear arrays with a large number of elements. After building accurate ANN models, we make use of the adaptive differential evolution algorithm JADE to search for the optimal location distribution and the excitations of the elements, to satisfy the requirements for the radiation pattern and the active S parameters of the antenna array. In view of the fact that the mutual coupling effects are accounted for in the synthesis process, the proposed method probably exhibits more practical value in antenna array designs than in other synthesis methods. The validity and efficiency of the proposed method are confirmed based on three examples.

The Concept of Synapse against Popular Opinion

Emulation of the operation process in the human brain was performed by Artificial Neural Network (ANN). The new comments of this study with the concept of progressive tense like an action to new Comparison ANN with Biological Neuron Network were stated against popular opinions. However, their opinions just pointed out the role of Synapse as the weights in the framework of ANN. In this paper, another concept was better suggested. The role of Synapse should be treated as the weights of ANN, which connect two neurons of two hidden different layers. There was a new proposed opinion in this study when an accurate ANN model was built with optimal weights. The role of Synapse should be both the converting the action potential into electrical energy and chemical energy and synaptic strengthening corresponding to long-term potentiation (LTP) in Biological Neuron Network. From the concept of pharmacology, the action of updating weights with optimal values after training more data, was similar as keeping a normal converting for LTP just using medicaments for resisting some ageing brain diseases e.g. Dementia. The new proposed opinion by comparing both Neural Networks should be reasonable in this study.

Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights

Estimation of solar potential is vital for renewable energy applications. In several studies, artificial neural networks have been employed to model solar radiation using various meteorological parameters. The collection and availability of the most appropriate input parameters is important for getting an accurate artificial neural network model. The present study aims to estimate the global solar radiation using different meteorological parameters and identify the significant parameters based on the analysis of synaptic weights in an artificial neural network model using the connection weight approach. Initially, artificial neural network and empirical models is applied to estimate the solar radiation in Chamba region. The artificial neural network architecture 5-48-15-1 resulted in minimum mean absolute percentage error of 12.15%. The mean absolute percentage error values for linear models are found to be 18.95%, 15.39%, and 21.62%, respectively. Thereafter, connection weight approach is applied to find significant parameters. The efficacy of the approach has been shown through a case study related to estimation of solar radiations in the Hamirpur region situated in the state of Himachal Pradesh (India). Five input parameters, namely temperature (T), relative humidity (RH), clearness index (KT), precipitation (PT), and pressure (P), have been considered to estimate solar radiations using a feed-forward neural network. The proposed approach infers that temperature is the most significant parameter followed by humidity and pressure. The clearness index and precipitation has been found to have the least effect on the estimation of solar radiations. Results also indicate that artificial neural network based technique is more accurate compared to empirical model.