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Electric capacitance tomography for nondestructive testing of standing trees
Author(s) -
Carcangiu Sara,
Fanni Alessandra,
Montisci Augusto
Publication year - 2017
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2252
Subject(s) - capacitance , electrical capacitance tomography , position (finance) , set (abstract data type) , tomography , computer science , artificial neural network , inverse , inverse problem , nondestructive testing , artificial intelligence , mathematics , physics , mathematical analysis , geometry , optics , electrode , finance , quantum mechanics , economics , programming language
In this paper, an innovative Artificial Neural Network (ANN)‐based approach to solve the bi‐dimensional Electric Capacitance Tomography inverse problem is proposed in order to assess the health state of standing trees. Using a set of examples, an ANN is trained to solve the direct problem, ie, to associate the properties of the tree trunk affected by defects to a set of capacitance values. Then, the trained ANN is inverted in order to obtain the defect position and dimensions on the basis of measured capacitance values. The potentiality of the method is presented making reference to several target examples.