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Application of bayesian deep learning for Smart City monitoring and chemical industry processes operation

Grant number: 19/08280-9
Support Opportunities:Scholarships in Brazil - Master
Start date: June 01, 2019
End date: May 31, 2021
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:Song Won Park
Grantee:Gustavo Ryuji Taira
Host Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:17/50343-2 - Institutional development plan in the area of digital transformation: advanced manufacturing and smart and sustainable cities (PDIp), AP.PDIP

Abstract

Project consists on the development of Bayesian Deep Learning based systems applicable for the Smart Cities monitoring and chemical industry process operations. The elaborate systems will be applied to databases obtained by air quality sensors and/or sound noise sensors, and, lastly, they will be applied to industrial chemical process data (FCC) (Fluid Catalytic Cracking). One of the goals of this work is the development of Big Data Analytics systems to be incorporated as applications of Machine Learning with methods that are easily applicable for different contexts, in order to create self-agent systems such as Smart City and Smart Factory. (AU)

News published in Agência FAPESP Newsletter about the scholarship:
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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
TAIRA, GUSTAVO R.; PARK, SONG W.; ZANIN, ANTONIO C.; PORFIRIO, CARLOS R.. Fault Detection in a Fluid Catalytic Cracking Process using Bayesian Recurrent Neural Network. IFAC PAPERSONLINE, v. 55, n. 7, p. 6-pg., . (19/08280-9)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
TAIRA, Gustavo Ryuji. Bayesian neural networks for calibration of air pollution sensors and fault detection in chemical processes.. 2022. Master's Dissertation - Universidade de São Paulo (USP). Escola Politécnica (EP/BC) São Paulo.