Um comparativo entre a utilização de redes neurais perceptron e redes neurais profundas na identificação de radionuclídeos em espectrometria gama
DOI:
https://doi.org/10.15392/bjrs.v8i1.1132Keywords:
redes neurais, redes neurais profundas, caracterização, espectrometria gamaAbstract
Apresentamos os resultados da comparação entre uma Rede Neural Profunda e uma Rede Neural Perceptron na classificação de espectros gama obtidos utilizando um detector de germânio hiper-puro. Utilizando dados de diversas fontes seladas (Am-241, Ba-133, Cd-109, Co-57, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210) foram gerados uma lista extensa de espectros para treino e validação contendo, respectivamente, 500 e 160 espectros, onde foram mesclados até três radionuclídeos em um único espectro. Depois de 250 épocas de treino foram validadas a acurácia de cada um dos modelos utilizando o conjunto de validação. O modelo de rede neural profunda obteve uma acurácia de classificação de 96,25% enquanto a rede neural perceptron obteve uma acurácia de 80,62%. Os resultados mostram um desempenho robusto e consistentemente melhor das redes neurais profundas, frente as redes neurais perceptron.
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