Otimização da escolha de modelo de propagação por medição de campo e inteligência artificial
Tipo
Dissertação
Data de publicação
2019-02-05
Periódico
Citações (Scopus)
Autores
Botelho, Alberto Leonardo Penteado
Orientador
Akamine, Cristiano
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Omar, Nizam
Casella, Ivan Roberto Santana
Casella, Ivan Roberto Santana
Programa
Engenharia Elétrica
Resumo
The propagation model to be chosen in the design of a digital terrestrial
broadcast station is a critical point for predicting the coverage area. There are
several models, with specific characteristics that may be better than others in
certain situations. This dissertation presents a study of the choice of propagation
model, through the use of artificial intelligence (AI). A brief review of the SBTVD
(Brazilian System of Digital Television), the complexity operation in SFN (Single
Frequency Network) and the most widely used propagation models in the
literature. The comparison of propagation models was elaborated with the field
measurements and simulations by the Progira coverage prediction software,
which works on an ArcGis geoprocessing platform that considered the criterion
of smallest average error (absolute mean deviation, standard deviation and root
mean square error) between the field measurement and the software simulation.
The propagation model ITUR P. 1812-3 had the best average performance. To
optimize the analysis of choice of propagation models, an AI method was
developed by machine learning, classification learning, so that the computer can
formulate aspects of human intelligence and have the ability to choose the best
propagation model for each study area, not restricted to sites measured in the
field. The Support Vectors Machines and Nearest Neighbor Classifiers learning
models displayed a significant improvement of the average error in comparison
to the model of propagation of smallest average error
Descrição
Palavras-chave
televisão digital , SBTVD , modelo de propagação , rede de frequência única , predição de cobertura , medição de campo , inteligência artificial , aprendizagem de máquina , aprendizagem por classificação
Assuntos Scopus
Citação
BOTELHO, Alberto Leonardo Penteado. Otimização da escolha de modelo de propagação por medição de campo e inteligência artificial. 2019. 189 f. Dissertação (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2019.