Inverse Estimation of Surface Temperature in Nanoscale Using the Artificial Neural Network

2005 ◽  
Author(s):  
Bup Sung Jung ◽  
Sun K. Kim ◽  
Woo Il Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Inverse Heat Conduction Problem ◽  
Inverse Heat Conduction ◽  
Nanoscale Structures ◽  
Temperature Observation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

An inverse heat conduction problem (IHCP) for nanoscale structures was studied. The conduction phenomenon is modeled using the Boltzmann transfer equation. Phonon-mediated heat conduction in one dimension is considered. One boundary, where temperature observation takes place, is subjected to a known boundary condition and the other boundary is exposed to an unknown temperature. The artificial neural network (ANN) is employed to solve the described inverse problem. Sample results are presented and discussed.

Simulation and Experimental Study of Inverse Heat Conduction Problem

2011 ◽  
Vol 233-235 ◽  
pp. 2820-2823 ◽  
Author(s):  
Ley Chen ◽  
S Askarian ◽  
M Mohammadzaheri ◽  
F Samadi
Keyword(s):  
Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Experimental Tests ◽  
Inverse Heat Conduction Problem ◽  
Inverse Heat Conduction ◽  
Input Output ◽  
Output Data ◽  
Neural Network Method ◽  
Network Method

In this paper, a neural network method is proposed to solve a one dimensional inverse heat conduction problem (IHCP). The method relies on input/output data of an unknown system to create an intelligent neural network model. Multi layer perceptrons with recurrent properties are utilised in the model. Prepared input/output data are used to train the neural network. Reliable checking processes are also offered to justify the robustness of the method. A numerical sequential function specification (SFS) method is used as another technique to solve the IHCP. The numerical result is compared with that of the proposed method and good agreement is shown between the two methods. However, the numerical method can be only used to solve the IHCP off-line due to the high computation requirement. The proposed neural network method can be used in real-time situations as shown in the experimental tests.

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.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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