Numerical Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation

Plasma nitriding is a powerful process for surface modification of different materials. In this study, plasma nitriding is applied on a Nickel-Aluminum composite, coated on ST37 steel. Ni+Al composites were fabricated by electrodeposition process in watts bath containing Al particles. For prediction of electrodeposited Al% during the electroplating and microhardness of coatings after plasma nitriding process artificial neural network (ANN) was used. The numerical results obtained via a neural network model were compared with the experimental results. Agreement between the experimental and numerical results was reasonably good.

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Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network

International Journal of Numerical Methods for Heat &amp Fluid Flow ◽

10.1108/hff-07-2021-0498 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hamid Reza Tamaddon Jahromi ◽

Igor Sazonov ◽

Jason Jones ◽

Alberto Coccarelli ◽

Samuel Rolland ◽

...

Keyword(s):

Neural Network ◽

Indoor Environment ◽

Numerical Results ◽

Human Breath ◽

Content Type ◽

Operation Conditions ◽

Gated Recurrent Units ◽

Enclosed Space ◽

The Impact ◽

Ventilation Conditions

Purpose The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets. Design/methodology/approach A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart–Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training. Findings In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space. Originality/value This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.

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A parallel Numerical Algorithm For Solving Some Fractional Integral Equations

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/32.1.1918 ◽

2019 ◽

Vol 32 (1) ◽

pp. 184

Author(s):

Khalid Mindeel Mohammed

Keyword(s):

Neural Network ◽

Integral Equations ◽

Numerical Algorithm ◽

Fractional Integral ◽

High Accuracy ◽

Numerical Results ◽

New Method ◽

Training Algorithm ◽

Numerical Examples ◽

Fractional Equations

In this study, He's parallel numerical algorithm by neural network is applied to type of integration of fractional equations is Abel’s integral equations of the 1st and 2nd kinds. Using a Levenberge – Marquaradt training algorithm as a tool to train the network. To show the efficiency of the method, some type of Abel’s integral equations is solved as numerical examples. Numerical results show that the new method is very efficient problems with high accuracy.

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An Investigation of Interpolation Scheme Based on Indirect Radial Basis Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.226-228.2181 ◽

2012 ◽

Vol 226-228 ◽

pp. 2181-2188

Author(s):

Hai Tao Sun

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Accuracy ◽

Numerical Results ◽

Interpolation Scheme ◽

Radial Basis Neural Network ◽

Radial Basis Neural Networks ◽

Radial Basis ◽

Different Order ◽

Methods Numerical

An indirect radial basis neural network (IRBNN) is proposed for improving the accuracy of the approximated functions. The IRBNN is constructed by new prompted functions generated from the Nth order derivative of the approximated function. In this way, high accuracy derivatives in different order can be obtained, so that more accuracy of the numerical results would be given while the IRBNN is employed for creating approximated functions in numerical methods. Numerical results through applications in elasticity show the effectiveness and accuracy of the IRBNN method.

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Chebyshev neural network model with linear and nonlinear active functions

International Journal of Basic and Applied Sciences ◽

10.14419/ijbas.v5i3.6382 ◽

2016 ◽

Vol 5 (3) ◽

pp. 182

Author(s):

Sarkhosh Seddighi Chaharborj ◽

Yaghoub Mahmoudi

Keyword(s):

Neural Network ◽

Neural Network Model ◽

Initial Value Problems ◽

High Accuracy ◽

Numerical Results ◽

Initial Value ◽

Linear Ordinary Differential Equations ◽

Chebyshev Neural Network ◽

Active Function ◽

Linear And Nonlinear

In this paper the second order non-linear ordinary differential equations of Lane-Emden type as singular initial value problems using Chebyshev Neural Network (ChNN) with linear and nonlinear active functions has been studied. Active functions as, \(\texttt{F(z)=z}, \texttt{sinh(x)}, \texttt{tanh(z)}\) are considered to find the numerical results with high accuracy. Numerical results from Chebyshev Neural Network shows that linear active function has more accuracy and is more convenient compare to other functions.

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Study of mixed convection in a caterpillar wavy lid-driven triangular cavity filled with nanofluid using artificial neural network

Canadian Journal of Physics ◽

10.1139/cjp-2017-0282 ◽

2018 ◽

Vol 96 (5) ◽

pp. 476-493 ◽

Cited By ~ 5

Author(s):

Manoj Kr. Triveni ◽

Rajsekhar Panua

Keyword(s):

Neural Network ◽

Heat Transfer ◽

Artificial Neural Network ◽

Mixed Convection ◽

Richardson Number ◽

Volume Fraction ◽

Numerical Study ◽

Numerical Results ◽

Grashof Number ◽

Artificial Neural

The present numerical study is carried out for mixed convection in a nanofluid-filled lid-driven triangular cavity. The base wall of the cavity is in a caterpillar shape, which is assumed as a hot wall while the side and inclined walls are considered as cold walls. The finite volume method along with the SIMPLE algorithm is used to discretize the governing equations. The study is evaluated for constrained parameters, such as volume fraction of the nanoparticles, sliding direction of the side wall, Richardson number, and Grashof number. Fluid flow and heat transfer are presented in terms of streamlines and isotherms and rate of enhancement has been shown by local and average Nusselt number. It is observed from the study that the heat transfer rate is enhanced for each volume fraction of nanoparticles, for both directions of sliding wall, Richardson number, and Grashof number. The obtained numerical results are validated with the predicted results of artificial neural network (ANN). Good agreement is reported between the numerical results and the predicted results.

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Secret Communication Systems Using Chaotic Wave Equations with Neural Network Boundary Conditions

Entropy ◽

10.3390/e23070904 ◽

2021 ◽

Vol 23 (7) ◽

pp. 904

Author(s):

Yuhan Chen ◽

Hideki Sano ◽

Masashi Wakaiki ◽

Takaharu Yaguchi

Keyword(s):

Neural Network ◽

Boundary Condition ◽

Wave Equation ◽

Nonlinear Boundary ◽

Nonlinear Boundary Condition ◽

Numerical Results ◽

Original Image ◽

Secret Communication ◽

The Neural Network ◽

Encrypted Images

In a secret communication system using chaotic synchronization, the communication information is embedded in a signal that behaves as chaos and is sent to the receiver to retrieve the information. In a previous study, a chaotic synchronous system was developed by integrating the wave equation with the van der Pol boundary condition, of which the number of the parameters are only three, which is not enough for security. In this study, we replace the nonlinear boundary condition with an artificial neural network, thereby making the transmitted information difficult to leak. The neural network is divided into two parts; the first half is used as the left boundary condition of the wave equation and the second half is used as that on the right boundary, thus replacing the original nonlinear boundary condition. We also show the results for both monochrome and color images and evaluate the security performance. In particular, it is shown that the encrypted images are almost identical regardless of the input images. The learning performance of the neural network is also investigated. The calculated Lyapunov exponent shows that the learned neural network causes some chaotic vibration effect. The information in the original image is completely invisible when viewed through the image obtained after being concealed by the proposed system. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method.

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Bubble Trajectory in a Bubble Column Reactor using Combined Image Processing and Artificial Neural Network

International Journal of Chemical Reactor Engineering ◽

10.1515/ijcre-2015-0186 ◽

2017 ◽

Vol 15 (1) ◽

Author(s):

Balachandar Chidambaram ◽

Arunkumar Seshadri ◽

Venkatesan Muniyandi

Keyword(s):

Neural Network ◽

Image Processing ◽

Artificial Neural Network ◽

Bubble Column ◽

Numerical Results ◽

Newtonian Fluids ◽

Bubble Column Reactor ◽

Column Reactor ◽

Bubble Column Reactors ◽

Artificial Neural

Abstract The applications of Bubble column reactors in gas-liquid multiphase reactions are widely observed in process industries. Biochemical reactions such as wet oxidation and algae bio-reactions are carried out in bubble column reactors. In this article, an image processing based comprehensive algorithm is developed to identify the trajectory of bubbles in a bubble column reactor. Photographs of bubbles moving up in a bubble column reactor are recorded for different velocities using a high speed camera. An algorithm is developed to plot the trajectory of the bubble. The developed algorithm can be used with experimental and numerical results to trace the trajectory of bubbles. The algorithm is applied to the results of volume of fluids (VOF) simulation to identify the bubble path in Newtonian and non-Newtonian fluids. Based on the algorithm, numerical results obtained on Newtonian fluids are used to train an Artificial Neural Network (ANN) to find the temporal position of the bubble. Superficial fluid velocities, nozzle diameter and time are the input parameters. The trained Levenberg-Marquardt based neural network can find the position of the bubble at any instant of time. The designed algorithm can study the dynamics and position of a bubble in process applications carried out in a bubble column reactor.

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