Identification of the spatial distribution of conductivity by eddy current defectoscopy with application of artificial neural networks

2012 ◽  
Author(s):  
T. Chady ◽  
I. Spychalski ◽  
R. Sikora
Keyword(s):  
Neural Networks ◽  
Spatial Distribution ◽  
Artificial Neural Networks ◽  
Eddy Current ◽  
Artificial Neural

Identification of Conductivity Distribution Using Eddy Current Tomography System and Artificial Neural Networks

2010 ◽  
Vol 670 ◽  
pp. 336-344
Author(s):  
Tomasz Chady ◽  
Ireneusz Spychalski ◽  
Takashi Todaka
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Base ◽  
Eddy Current ◽  
Current System ◽  
Identification Problem ◽  
Conductivity Distribution ◽  
Tomography System ◽  
Dimensional Distribution ◽  
Artificial Neural

In certain applications (security, biomedical, food and wood testing etc.) it is necessary to detect and identify position of small metal particles with high precision. This paper presents an eddy current system designated for evaluation of conductivity distribution. The system was modeled using the finite element method as well as it was constructed and the measurements were carried out. Using these results a data base of the signals achieved for various configurations of the test objects were created. The data base was utilized to solve the identification problem. Artificial neural networks were utilized as the inverse models in order to reconstruct two-dimensional distribution of conductivity. Selected results achieved for simulated signals were presented.

scholarly journals LITHOLOGICAL INTERPRETATION ACCORDING TO WELL-LOGGING DATA FOR THE BAZHENOV FORMATION WITH USAGE OF ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 2 (3) ◽  
pp. 3-9
Author(s):  
Elena U. Temnikova ◽  
Serafim I. Grubas ◽  
Arsenii A. Fedoseev
Keyword(s):  
Neural Networks ◽  
Spatial Distribution ◽  
Artificial Neural Networks ◽  
Well Logging ◽  
Data Models ◽  
Relative Content ◽  
Material Composition ◽  
Bazhenov Formation ◽  
Modern Classification ◽  
Artificial Neural

Using artificial neural networks for lithological interpretation according to well logging data, models of the relative content of rock-forming components of the Bazhenov Formation were constructed and its main types of rocks were identified in accordance with a modern classification. Results of lithological interpretation were used for building correlation schemes, which made it possible to trace the spatial distribution of the material composition and main types of rocks of the Bazhenov Formation for the Salym field.

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