scholarly journals Predictions of Friction Coefficient in Hydrodynamic Journal Bearing Using Artificial Neural Networks

2021 ◽  
Vol 67 (9) ◽  
pp. 411-420
Author(s):  
Dragan Milčić ◽  
Amir Alsammarraie ◽  
Miloš Madić ◽  
Vladislav Krstić ◽  
Miodrag Milčić
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Friction Coefficient ◽  
Journal Bearing ◽  
Hydrodynamic Lubrication ◽  
Percentage Error ◽  
Radial Load ◽  
Neural Network Models ◽  
Artificial Neural

This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed.

Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models

2019 ◽  
Vol 89 (19-20) ◽  
pp. 4083-4094 ◽  
Author(s):  
Melkie Getnet Tadesse ◽  
Emil Loghin ◽  
Marius Pislaru ◽  
Lichuan Wang ◽  
Yan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Extreme Values ◽  
Subjective Evaluation ◽  
Fuzzy Model ◽  
Percentage Error ◽  
Ann Model ◽  
Neural Network Models ◽  
Artificial Neural

This paper aims to predict the hand values (HVs) and total hand values (THVs) of functional fabrics by applying the fuzzy logic model (FLM) and artificial neural network (ANN) model. Functional fabrics were evaluated by trained panels employing subjective evaluation scenarios. Firstly, the FLM was applied to predict the HV from finishing parameters; then, the FLM and ANN model were applied to predict the THV from the HV. The estimation of the FLM on the HV was efficient, as demonstrated by the root mean square error (RMSE) and relative mean percentage error (RMPE); low values were recorded, except those bipolar descriptors whose values are within the lowermost extreme values on the fuzzy model. However, the prediction performance of the FLM and ANN model on THV was effective, where RMSE values of ∼0.21 and ∼0.13 were obtained, respectively; both values were within the variations of the experiment. The RMPE values for both models were less than 10%, indicating that both models are robust, effective, and could be utilized in predicting the THVs of the functional fabrics with very good accuracy. These findings can be judiciously utilized for the selection of suitable engineering specifications and finishing parameters of functional fabrics to attain defined tactile comfort properties, as both models were validated using real data obtained by the subjective evaluation of functional fabrics.

scholarly journals Modeliranje i prognoziranje broja zaposlenih u turizmu i hotelskoj industriji u Republici Hrvatskoj primjenom modela umjetnih neuronskih mreža

2020 ◽  
Vol 10 (2) ◽  
pp. 3-20
Author(s):  
Tea Baldigara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Percentage Error ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Prognostic Power ◽  
Modelling And Forecasting

The paper investigates the performance and prognostic power of artificial neural network models in modelling and forecasting of time series of seasonal character. Models of artificial neural networks have been applied in modelling and forecasting the monthly total number of employees, the number of employed men and the number of employed women in the activity of providing accommodation services and preparing and serving food and beverages in the Republic of Croatia. The obtained modelling results have been compared with the results obtained by applying some of the traditionally used quantitative models in the analysis of seasonal time series, such as the Holt-Winters model of triple exponential smoothing and the seasonal multiplicative model of exponential trend. The evaluation of the performance and prognostic power of individual models was performed by comparing the average absolute and average absolute percentage error and the correlation coefficient between the actual and estimated values, and the predicted values were compared with the actual values. The evaluation of the obtained results showed that the selected model of acyclic multilayer perceptron is suitable for modelling and forecasting time series of seasonal character. The comparison of prognostic powers and actual and projected values of the number of employees suggests that the designed model of the artificial neural network is very reliable. This indicates that the models of artificial neural networks have great application potentials in the domain of modelling and forecasting of time series of a seasonal character.

Optimizing the input vectors of applied artificial neural network models for wind power production forecasting

2021 ◽  
pp. 0309524X2110463
Author(s):  
Konstantinos V Kolokythas ◽  
Athanassios A Argiriou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Plant ◽  
Wind Energy ◽  
Wind Power ◽  
Percentage Error ◽  
Mountainous Area ◽  
Forecast Horizon ◽  
Neural Network Models ◽  
Artificial Neural

The significant increase of wind energy production worldwide revealed the necessity of its accurate forecasting. However, this is a very complex and, despite the progress made, more accurate forecasting methods are still needed. Accurate forecasts will contribute to a better power plant and grid management by solving problems related to the distribution and storage of the produced electricity, maximizing thus the profits of wind energy investments, contributing ultimately to their further enhancement. Here we present the development and validation of selected artificial neural network (ANN) wind energy forecasting models that produce hourly forecasts for 24 hours forecast horizon. The models are developed and validated using wind speed, direction, and energy produced by a wind power plant located at a semi-mountainous area in Western Greece. The ANN forecasts are compared against those of the persistence method using the Root Means Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) statistics.

scholarly journals Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

2021 ◽  
Author(s):  
Ahmed Benyekhlef ◽  
Brahim Mohammedi ◽  
Djamel Hassani ◽  
Salah Hanini
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Exchanger ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inlet Temperature ◽  
Fouling Resistance ◽  
Data Points ◽  
Artificial Neural

Abstract In this work an artificial neural network model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. This data points contains 6 inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 230-231
Author(s):  
Sunday O Peters ◽  
Mahmut Sinecan ◽  
Kadir Kizilkaya ◽  
Milt Thomas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Normal Distribution ◽  
Genomic Prediction ◽  
Network Models ◽  
Bivariate Normal Distribution ◽  
Snp Markers ◽  
Neural Network Models ◽  
Bivariate Normal ◽  
Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

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