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Use este identificador para citar ou linkar para este item: http://repositorio.unb.br/handle/10482/29002
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Título: An inverse model for locating skin tumours in 3D using the genetic algorithm with the dual reciprocity boundary element method
Autor(es): Bueno, Fabrício Ribeiro
Partridge, Paul William
Assunto: Método inverso
Algoritmos genéticos
Métodos de elementos de contorno
Tumores
Equação biotérmica de Pennes
Data de publicação: Nov-2013
Editora: Associação Brasileira de Ciências Mecânicas
Referência: BUENO, Fabrício Ribeiro; PARTRIDGE, Paul William. An inverse model for locating skin tumours in 3D using the genetic algorithm with the dual reciprocity boundary element method. Latin American Journal of Solids and Structures, Rio de Janeiro, v. 10, n. 6, p. 1061-1079, nov. 2013. DOI: http://dx.doi.org/10.1590/S1679-78252013000600001. Disponível em: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252013000600001&lng=en&tlng=en. Acesso em: 04 ago. 2020.
Abstract: Here, the Dual Reciprocity Boundary Element Method is used to solve the 3D Pennes Bioheat Equation, which together with a Genetic Algorithm, produces an inverse model capable of obtaining the location and the size of a tumour, having as data input the temperature distribution measured on the skin surface. Given that the objective function, which is solved inversely, involves the DRBEM (Dual Reciprocity Boundary Element Method) the Genetic Algorithm in its usual form becomes slower, in such a way that it was necessary to develop functions based the solution history in order that the process becomes quicker and more accurate. Results for 8 examples are presented including cases with convection and radiation boundary conditions. Cases involving noise in the readings of the equipment are also considered. This technique is intended to assist health workers in the diagnosis of tumours.
Licença: Latin American Journal of Solids and Structures - (CC BY) - All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License. Fonte: https://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252013000600001&lng=en&tlng=en. Acesso em: 04 ago. 2020.
DOI: https://dx.doi.org/10.1590/S1679-78252013000600001
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