PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA ELÉTRICA (PPGEE)
UNIVERSIDADE FEDERAL DA PARAÍBA
- Phone
-
(83)32167857
News
Banca de QUALIFICAÇÃO: FILIPE DO Ó CAVALCANTI
Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
DISCENTE: FILIPE DO Ó CAVALCANTI
DATA: 30/09/2022
HORA: 09:00
LOCAL: Videoconferência
TÍTULO: Real-time Pavement Classification Using an Embedded Artificial Neural Network
PALAVRAS-CHAVES: Embedded systems, pavement classification, accelerometer, RTOS
PÁGINAS: 42
RESUMO: The capacity to analyze pavement condition can be a very important feature for either
automobiles or general road quality surveys. It presents such data that can be used as
reference to decide when a road requires maintenance or as a means to select a different
route when travelling, based on a driver requirement for comfort. There are methods
available for general road analysis such as the Pavement Condition Index (PCI) that
requires extensive manual survey of the pavement. In this context, this work presents a
system that uses machine learning techniques for automatic pavement classification, based
on accelerometer readings that are used to classify roads between two categories: asphalt
or paving stone. A triaxial accelerometer and Global Positioning System (GPS) modules
are used as peripherals to a real-time operating system that can execute high precision
data acquisitions and also classify the road in real time. This operation requires that an
accelerometer module is mounted next to the vehicles center of gravity and is calibrated to
the vehicle mounting point. The data acquisition mode is used to obtain data in the city of
Campinas - Brazil, containing acceleration from longitudinal, lateral and vertical axis and
geolocation data from the GPS. This data is analyzed and an extensive feature selection
process is executed to filter the best metrics that can be used for training the Artificial
Neural Network (ANN) for pavement classification between the two types of road. The
model uses features extracted from acceleration data in both time and frequency domains
and achieved an accuracy of 94% in the test set. The model was added to the embedded
system, allowing classification of the pavement in real time.
MEMBROS DA BANCA:
Externo à Instituição - CARLOS DANILO MIRANDA REGIS
Interno - 1523920 - CLEONILSON PROTASIO DE SOUZA
Presidente - 1718473 - FABRICIO BRAGA SOARES DE CARVALHO
Interno - 1744179 - WASLON TERLLIZZIE ARAUJO LOPES