scholarly journals Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

Designs ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 78
Author(s):  
Kareem Othman
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Activation Function ◽  
Dry Density ◽  
Activation Functions ◽  
Neural Network Models ◽  
Maximum Dry Density ◽  
Base Course ◽  
Aggregate Base ◽  
The Impact

Laboratory tests for the estimation of the compaction parameters, namely the maximum dry density (MDD) and optimum moisture content (OMC) are time-consuming and costly. Thus, this paper employs the artificial neural network technique for the prediction of the OMC and MDD for the aggregate base course from relatively easier index properties tests. The grain size distribution, plastic limit, and liquid limits are used as the inputs for the development of the ANNs. In this study, multiple ANNs (240 ANNs) are tested to choose the optimum ANN that produces the best predictions. This paper focuses on studying the impact of three different activation functions: number of hidden layers, number of neurons per hidden layer on the predictions, and heatmaps are generated to compare the performance of every ANN with different settings. Results show that the optimum ANN hyperparameters change depending on the predicted parameter. Additionally, the hyperbolic tangent activation is the most efficient activation function as it outperforms the other two activation functions. Additionally, the simplest ANN architectures results in the best predictions, as the performance of the ANNs deteriorates with the increase in the number of hidden layers or the number of neurons per hidden layers.

Evaluating the Impact of Coarse Aggregate Content on Properties of Cement Treated Base

2011 ◽  
Vol 52-54 ◽  
pp. 729-733 ◽  
Author(s):  
Li Qun Hu ◽  
Ai Min Sha
Keyword(s):  
Coarse Aggregate ◽  
Thermal Shrinkage ◽  
Dry Density ◽  
Maximum Dry Density ◽  
Coarse Aggregates ◽  
Aggregate Content ◽  
Base Course ◽  
Aggregate Base ◽  
The Impact ◽  
Shrinkage Coefficient

This paper mainly presents the study on the properties of cement treated aggregate with different coarse aggregate content. The test specimens which contain 75%, 70%, 65%, 60% and 55% of coarse aggregates were made and the 7d, 28d, and 90d unconfined compressive strengths (UCS), 28d thremal shrinkage coefficient, as well as 90d anti-erosion performance were tested. Results show that with the increase of coarse aggregate, the maximum dry density (MDD)of the cement treated aggregate mixture increased slowly at first to reached the peak value and then decreased rapidly; The optimum moisture content (OMC) declined with the increase of coarse aggregate content; In order to enhance the UCS of cement treated aggregate, coarse aggregate of mixture can be increased to some extent, but too much coarse aggregate will increase the void of the specimen and lead to lower UCS; Increasing the content of coarse aggregate is able to decrease the thermal shrinkage coefficient and erosion quantity of 30 min of the specimens. This is favourable to enhance the cracking resistance and anti-erosion performance of cement treated aggregate base course.

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.

Analysis of Fin-Tube Evaporator Performance With Limited Experimental Data Using Artificial Neural Networks

2000 ◽  
Author(s):  
Arturo Pacheco-Vega ◽  
Mihir Sen ◽  
Rodney L. McClain
Keyword(s):  
Neural Network ◽  
Heat Rate ◽  
Network Models ◽  
Activation Function ◽  
Operating Conditions ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Artificial Neural ◽  
Fin Tube

Abstract In the current study we consider the problem of accuracy in heat rate estimations from artificial neural network models of heat exchangers used for refrigeration applications. The network configuration is of the feedforward type with a sigmoid activation function and a backpropagation algorithm. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer in these systems. Results from this exercise show that a well-trained network correlates the data with errors of the same order as the uncertainty of the measurements. It is also shown that the number and distribution of the training data are linked to the performance of the network when estimating the heat rates under different operating conditions, and that networks trained from few tests may give large errors. A methodology based on the cross-validation technique is presented to find regions where not enough data are available to construct a reliable neural network. The results from three tests show that the proposed methodology gives an upper bound of the estimated error in the heat rates.

Mathematics anxiety and cognition: an integrated neural network model

2020 ◽  
Vol 31 (3) ◽  
pp. 287-296
Author(s):  
Ahmed A. Moustafa ◽  
Angela Porter ◽  
Ahmed M. Megreya
Keyword(s):  
Neural Network ◽  
Educational Outcomes ◽  
Mathematics Anxiety ◽  
Network Models ◽  
Neural Mechanism ◽  
Neural Network Models ◽  
Cognitive Studies ◽  
Behavioral Studies ◽  
Negative Effect ◽  
The Impact

AbstractMany students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.

scholarly journals Optimized Activation for Quantum-Inspired Self-supervised Neural Network based Fully Automated Brain MR Image Segmentation

2020 ◽  
Author(s):  
Debanjan Konar ◽  
Siddhartha Bhattacharyya ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Models ◽  
Activation Function ◽  
Medical Image Segmentation ◽  
Data Repository ◽  
Dynamic Susceptibility ◽  
Neural Network Models ◽  
Promising Alternative ◽  
Multi Level

<div>The slow-convergence problem degrades the segmentation performance of the recently proposed Quantum-Inspired Self-supervised Neural Network models owing to lack of suitable tailoring of the inter-connection weights. Hence, incorporation of quantum-inspired meta-heuristics in the Quantum-Inspired Self-supervised Neural Network models optimizes their hyper-parameters and inter-connection weights. This paper is aimed at proposing an optimized version of a Quantum-Inspired Self-supervised Neural Network (QIS-Net) model for optimal</div><div>segmentation of brain Magnetic Resonance (MR) Imaging. The suggested Optimized Quantum-Inspired Self-supervised Neural Network (Opti-QISNet) model resembles the architecture of QIS-Net and its operations are leveraged to obtain optimal segmentation outcome. The optimized activation function employed in the presented model is referred to as Quantum-Inspired Optimized Multi-Level Sigmoidal (Opti-QSig) activation. The Opti-QSig activation function is optimized by three quantum-inspired meta-heuristics with fifitness evaluation using Otsu’s multi-level thresholding. Rigorous experiments have been conducted on Dynamic Susceptibility Contrast (DSC) brain MR images from Nature data repository. The experimental outcomes show that the proposed self-supervised Opti-QISNet model offffers a promising alternative to the deeply supervised neural network based architectures (UNet and FCNNs) in medical image segmentation and outperforms our recently developed models QIBDS Net and QIS-Net.</div>

scholarly journals The Impact of an Urban Growth Boundary on Land Development in Knox County, Tennessee: A Comparison of Two-Stage Probit Least Squares and Multilayer Neural Network Models

2007 ◽  
Vol 39 (3) ◽  
pp. 701-717 ◽  
Author(s):  
Seong-Hoon Cho ◽  
Olufemi A. Omitaomu ◽  
Neelam C. Poudyal ◽  
David B. Eastwood
Keyword(s):  
Neural Network ◽  
Urban Growth ◽  
Land Development ◽  
Network Models ◽  
Probit Model ◽  
Neural Network Models ◽  
Two Stage ◽  
Urban Growth Boundary ◽  
Knox County ◽  
The Impact

The impact of an urban growth boundary (UGB) on land development in Knox County, TN is estimated via two-stage probit and neural-network models. The insignificance of UGB variable in the two-stage probit model and more visible development patterns in the western part of Knoxville and the neighboring town of Farragut during the post-UGB period in both models suggest that the UGB has not curtailed urban sprawl. Although the network model is found to be a viable alternative to more conventional discrete choice approach for improving the predictability of land development, it is at the cost of evaluating marginal effects.

scholarly journals Artificial Neural Network Models for Material Classification by Photon Scattering Analysis

2020 ◽  
Vol 9 (03) ◽  
pp. 01-04
Author(s):  
Dr. Naveen Jain
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Network Models ◽  
Photon Scattering ◽  
Neural Network Models ◽  
Material Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
The Impact

This article explains the risk factors involved in a business. In each type of business, there are certain risk factors for the implementation of anything in the business. The type of risks involved can depend upon many factors. It also depends on the type of business an organisation is doing. But it is very important that the risk analyst does all the analysis of the risks that might arise in future and must take necessary actions in order to avoid those risks. The risk analyst can also try to reduce the impact of the risks on the business. Therefore, it is very important that the risk analyst should have the knowledge of how to analyse risk and then can act upon them.

Sign in / Sign up

Export Citation Format

Share Document