References of "Apolinário Jr., José A."
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailRobust Passive Coherent Location via Nonlinearly Constrained Least Squares
Nicolalde-Rodríguez, Daniel P.; Apolinário Jr., José A.; Alves Martins, Wallace UL

in 12th IEEE Latin America Symposium on Circuits and System (LASCAS), Arequipa 21-24 February 2021 (2021)

This paper addresses the problem of target location by means of a passive radar. Existing approaches based on time difference-of-arrival (TDOA) measurements, namely spherical interpolation and spherical ... [more ▼]

This paper addresses the problem of target location by means of a passive radar. Existing approaches based on time difference-of-arrival (TDOA) measurements, namely spherical interpolation and spherical intersection, are revisited for the case of single transmitter and multiple receivers. The mathematical formulations of these state-of-the-art approaches do not take into account possible TDOA estimation errors, which degrade the target location performance. We extend those formulations by incorporating a nonlinear constraint into the underlying least squares problem, thus conferring robustness to the location technique against TDOA estimation errors, as corroborated by extensive numerical experiments. [less ▲]

Detailed reference viewed: 53 (1 UL)
Full Text
Peer Reviewed
See detailA fault detector/classifier for closed-ring power generators using machine learning
Quintanilha, Igor M.; Elias, Vitor R. M.; Silva, Felipe B. et al

in Reliability Engineering and System Safety (2021)

Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and ... [more ▼]

Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and reducing operational costs. Most such systems employ expertise-reliant rule-based methods. This work proposes a different framework, in which machine-learning algorithms are used for detecting and classifying several fault types in a power-generation system of dynamically positioned vessels. First, principal component analysis is used to extract relevant information from labeled data. A random-forest algorithm then learns hidden patterns from faulty behavior in order to infer fault detection from unlabeled data. Results on fault detection and classification for the proposed approach show significant improvement on accuracy and speed when compared to results from rule-based methods over a comprehensive database. [less ▲]

Detailed reference viewed: 36 (0 UL)