Explorando a correlação espaço-temporal no agrupamento de sensores de cidades inteligentes

  • Morgana Gabi Gomes UFMG
  • Pedro H. Barros UFMG
  • Heitor S. Ramos UFMG

Resumo


Neste trabalho, propusemos uma função de similaridade chamada de SMELL-TS, baseada em aprendizagem de métrica profunda, para classificação de séries temporais no contexto de Zero-shot Learning, i.e., nosso método é apto a classificar objetos que pertecem a classes que ainda não foram usadas no conjunto de treinamento. Os dados são pré-processados pela Transformada de Fourier de Curto Termo, e posteriormente, são mapeados em dois novos espaços de representação, chamados de espaço latente e Espaço-S. Testamos nosso modelo num conjunto de dados reais de sensores distribuídos em um edifício inteligente, buscando agrupar sensores co-localizados no mesmo ambiente. Nosso método apresentou melhores resultados quando comparado com outras técnicas encontradas na literatura, com um ganho de 15 % na métrica de Room Accuracy – porcentagem de sensores co-localizados corretamente agrupados pelo SMELL-TS.

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Publicado
23/05/2022
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GOMES, Morgana Gabi; BARROS, Pedro H.; RAMOS, Heitor S.. Explorando a correlação espaço-temporal no agrupamento de sensores de cidades inteligentes. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 43-55. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2022.221955.

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