Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Francklin Rivas-Echeverria"'
Autor:
Edmundo Casas, Leo Ramos, Cristian Romero, Francklin Rivas-Echeverria, Dunetchka Cerpa, Pablo Hernandez, Gonzalo Orellana, Jose Luis Ibarra, Carlos Rosas Albrecht, Natalia Cuevas, Juan Carlos Gallardo Hurtado
Publikováno v:
IEEE Access, Vol 12, Pp 77831-77851 (2024)
Ensuring the safe and reliable operation of underground oil pipelines is crucial to prevent environmental disasters and maintain uninterrupted energy supply. Yet, this vast network faces threats from third-party activities, natural disasters, and agi
Externí odkaz:
https://doaj.org/article/452d9581cee14774a9642a20837493dc
Autor:
Leo Ramos, Edmundo Casas, Cristian Romero, Francklin Rivas-Echeverria, Manuel Eugenio Morocho-Cayamcela
Publikováno v:
IEEE Access, Vol 12, Pp 13711-13728 (2024)
This study explores the effectiveness of the ConvNeXt model, an advanced computer vision architecture, in the task of image captioning. We integrated ConvNeXt with a Long Short-Term Memory network that includes a visual attention module, focusing on
Externí odkaz:
https://doaj.org/article/98f9f35886c346faaa431208d3718d55
Publikováno v:
IEEE Access, Vol 11, Pp 126155-126171 (2023)
This paper introduces EngineFaultDB, a novel dataset capturing the intricacies of automotive engine diagnostics. Centered around the widely represented C14NE spark ignition engine, data was collected under controlled laboratory conditions, simulating
Externí odkaz:
https://doaj.org/article/a394fda4f9e24daf8ae24577231e7b40
Publikováno v:
IEEE Access, Vol 11, Pp 96554-96583 (2023)
This paper presents a comprehensive evaluation of YOLO architectures for smoke and wildfire detection, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLO-NAS. We aim to assess their effectiveness in early detection of wildfires. The Foggia dataset is
Externí odkaz:
https://doaj.org/article/696b3fe88be74f4883c88eeb14d3d69d
Autor:
Cristian García García, Javier Cárcel Carrasco, Mary Vergara Paredes, Francklin Rivas Echeverria, Franklin Camacho
Publikováno v:
Ingeniare. Revista chilena de ingeniería. 30:57-68
Autor:
Rita M. Ávila de Hernández, Francklin Rivas-Echeverria, Edwin A. Hernández-Caraballo, Tarcisio Capote-Luna
Publikováno v:
Talanta. 74:871-878
Radial basis neural networks (RBNNs) were developed and evaluated for discrimination of specimens of ‘aguardiente de Cocuy’, a spirituous beverage produced in the northwestern region of Venezuela. The beverage is distilled from the must of Agave
Publikováno v:
International Journal of Control. 73:678-685
A sliding mode control strategy is proposed for the synthesis of an adaptive learning algorithm in a neuron whose weights are constituted by first-order dynamical filters with adjustable parameters, which in turn allows the representation of dynamica
Publikováno v:
IFAC Proceedings Volumes. 32:5543-5546
In this work it is presented a fault defection scheme using neural networks with a fuzzy preprocessing done to the input signals. The use of the fuzzy preprocessing enriches the input set given to the fault diagnosis neural network. We present an exa
Publikováno v:
IFAC Proceedings Volumes. 32:5480-5484
A Variable Structure Control (VSC) based algorithm is used for adjusting a set of time varying parameters of a linear dynamic neuron, that serves as a model tor an uncertain nonlinear system with measurable state vector. This model allows the impleme
Publikováno v:
Scopus-Elsevier
A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. Th