Comparison of Artificial Neural Networks and the Transfer Function Method for Force Reconstruction in Curved Composite Plates

2014 ◽  
Vol 627 ◽  
pp. 301-304
Author(s):  
Marco Thiene ◽  
Z. Sharif-Khodaei ◽  
M.H. Aliabadi
Keyword(s):  
Transfer Functions ◽  
Impact Force ◽  
Composite Plates ◽  
Composite Panel ◽  
Radius Of Curvature ◽  
Reconstruction Problem ◽  
Advantages And Disadvantages ◽  
Transfer Function Method ◽  
Force Reconstruction ◽  
Artificial Neural

This paper presents two of the most recent approaches for impact force reconstruction, applied to a curved composite panel. The first one is based on the development of an artificial neural network, while the other on the evaluation of transfer functions in the frequency domain. Both methods provide advantages and disadvantages so that a detailed study should be conducted in order to determine which one can be considered more suitable for impact identification purposes. The aim of this paper is to present a comparison between these two methods, in particular when impacts on different surfaces of the plate are present. The main contribution is the application of the two approaches on a curved composite panel. The radius of curvature plays an important role in the contact force due to impacts on the inner or outer surface of the panel, introducing one more parameter in the reconstruction problem.

scholarly journals Impact force reconstruction and localization using nonconvex overlapping group sparsity

2022 ◽  
Vol 162 ◽  
pp. 107983
Author(s):  
Junjiang Liu ◽  
Baijie Qiao ◽  
Yuanchang Chen ◽  
Yuda Zhu ◽  
Weifeng He ◽  
...  
Keyword(s):  
Impact Force ◽  
Group Sparsity ◽  
Overlapping Group Sparsity ◽  
Force Reconstruction

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

IDENTIFICATION OF CURVATURE RADIUS FOR CURVED SECTIONS ON FOREST ROADS IN THE PROCESS OF UTILISATION

2021 ◽  
Vol 14(63) (1) ◽  
pp. 47-56
Author(s):  
Ihor RUDKO ◽  
Borys BAKAY ◽  
Abdullah AKAY ◽  
Vasyl BARYLIAK ◽  
Stanislav HORZOV
Keyword(s):  
Technical Information ◽  
Radius Of Curvature ◽  
Curvature Radius ◽  
Forest Roads ◽  
Forest Road ◽  
Advantages And Disadvantages ◽  
Natural Production ◽  
Angle Deviation ◽  
Road Projects ◽  
Selection Of

This article reviews the problem of measuring the actual radius of curvature for curved sections of existing forest roads, as forestry enterprises require reliable technical information about the current conditions of operated transport networks. It was identified that at this moment, a selection of methods are used for measuring the radii of horizontal curved sections of roads, which have certain advantages and disadvantages in specific natural production conditions. For calculating the radius of curvature for auto forest road projects it is recommended to apply the method of measured angles by chord angle deviation, which is sufficiently accurate for engineering purposes and does not require usage of special high-precision equipment and tools.

scholarly journals Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

2020 ◽  
Vol 48 (1) ◽  
pp. 366-377 ◽  
Author(s):  
Yeşim Benal ÖZTEKİN ◽  
Alper TANER ◽  
Hüseyin DURAN
Keyword(s):  
Neural Networks ◽  
Transfer Functions ◽  
Geometric Mean ◽  
Back Propagation ◽  
Castanea Sativa ◽  
Average Error ◽  
Ann Model ◽  
Neural Network Approach ◽  
Castanea Sativa Mill ◽  
Artificial Neural

The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R2 value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R2 value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.

Sign in / Sign up

Export Citation Format

Share Document