1 code implementation • 26 Feb 2024 • Daniel Leite, Alisson Silva, Gabriella Casalino, Arnab Sharma, Danielle Fortunato, Axel-Cyrille Ngomo
As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals.
no code implementations • 5 Mar 2021 • Daniel Leite, Volnei Frigeri Jr., Rodrigo Medeiros
We analyze the effect of individual electrodes, time window lengths, and frequency bands on the accuracy of user-independent eGFCs.
no code implementations • 18 Feb 2021 • Daniel Leite, Pedro Coutinho, Iury Bessa, Murilo Camargos, Luiz Cordovil Junior, Reinaldo Palhares
We present a method for incremental modeling and time-varying control of unknown nonlinear systems.
no code implementations • 25 Apr 2020 • Leticia Decker, Daniel Leite, Luca Giommi, Daniele Bonacorsi
Log records in the data center is a stochastic and non-stationary phenomenon in nature.
no code implementations • 17 Apr 2020 • Daniel Leite, Leticia Decker, Marcio Santana, Paulo Souza
Power-quality disturbances lead to several drawbacks such as limitation of the production capacity, increased line and equipment currents, and consequent ohmic losses; higher operating temperatures, premature faults, reduction of life expectancy of machines, malfunction of equipment, and unplanned outages.
no code implementations • 8 Apr 2020 • Leticia Decker, Daniel Leite, Fabio Viola, Daniele Bonacorsi
Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classification of the severity of anomalies.
no code implementations • 25 Mar 2020 • Charles Aguiar, Daniel Leite
Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream.