The application of the artificial neural network and hot probe method in thermal parameters determination of heat insulation materials Part 2 - application of the neural network

2009 ◽  
Author(s):  
S. Chudzik ◽  
W. Minkina ◽  
S. Grys
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Insulation ◽  
Probe Method ◽  
Thermal Parameters ◽  
Insulation Materials ◽  
The Neural Network ◽  
Hot Probe ◽  
Artificial Neural

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation Materials

2011 ◽  
Vol 18 (2) ◽  
pp. 261-274 ◽  
Author(s):  
Stanisław Chudzik ◽  
Waldemar Minkina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Conduction ◽  
Measurement System ◽  
Heat Insulation ◽  
Heat Conduction Problem ◽  
Thermal Parameters ◽  
Insulation Materials ◽  
Hot Probe ◽  
Artificial Neural

An Idea of a Measurement System for Determining Thermal Parameters of Heat Insulation MaterialsThe article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.

scholarly journals AN ARTIFICIAL NEURAL NETWORK DESIGN FOR DETERMINATION OF HASHIMOTO’S THYROIDITIS SUB-GROUPS

2016 ◽  
Vol 4 ◽  
pp. 756-762 ◽  
Author(s):  
Mehmet Emin Aktan ◽  
Erhan Akdoğan ◽  
Namık Zengin ◽  
Ömer Faruk Güney ◽  
Rabia Edibe Parlar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hashimoto’S Thyroiditis ◽  
Thyroid Stimulating Hormone ◽  
Hashimoto's Thyroiditis ◽  
The Neural Network ◽  
Right Lobe ◽  
Artificial Neural ◽  
Medical Analysis

In this study, an artificial neural network was developed for estimating Hashimoto’s Thyroiditis sub-groups. Medical analysis and measurements from 75 patients were used to determine the parameters most effective on disease sub-groups. The study used statistical analyses and an artificial neural network that was trained by the determined parameters. The neural network had four inputs: thyroid stimulating hormone, free thyroxine (fT4), right lobe size (RLS), and RLS2 – fT44, and two outputs for three groups: euthyroid, subclinical, and clinical. After training, the network was tested with data collected from 30 patients. Results show that, overall, the neural network estimated the sub-groups with 90% accuracy. Hence, the study showed that determination of Hashimoto’s Thyroiditis sub-groups can be made via designed artificial neural network.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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