|
|
Pereira J., M. M. & W. A. (2021). Qualitative Model to Maximize Shrimp Growth at Low Cost. 5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15, .
|
|
|
|
Wilton Agila, Gomer Rubio, Francisco Vidal, & B. Lima. (2019). Real time Qualitative Model for estimate Water content in PEM Fuel Cell. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 455–459).
Abstract: To maintain optimum performance of the electrical
response of a fuel cell, a real time identification of the
malfunction situations is required. Critical fuel cell states depend,
among others, on the variable demand of electric load and are
directly related to the membrane hydration level. The real time
perception of relevant states in the PEM fuel cell states space, is
still a challenge for the PEM fuel cell control systems. Current
work presents the design and implementation of a methodology
based upon fuzzy decision techniques that allows real time
characterization of the dehydration and flooding states of a PEM
fuel cell. Real time state estimation is accomplished through a
perturbation-perception process on the PEM fuel cell and further
on voltage oscillation analysis. The real time implementation of
the perturbation-perception algorithm to detect PEM fuel cell
critical states is a novelty and a step forwards the control of the
PEM fuel cell to reach and maintain optimal performance.
|
|
|
|
Dennis G. Romero, A. F. N., & Teodiano Freire B. (2014). Reconocimiento en-línea de acciones humanas basado en patrones de RWE aplicado en ventanas dinámicas de momentos invariantes. Revista Iberoamericana de Automática e Informática industrial 00 (2014), Vol. 11, pp. 202–211.
|
|
|
|
Velesaca, H. O., Suárez, P. L., Sappa, A. D., Carpio, D., Rivadeneira, R. E., & Sanchez, A. (2022). Review on Common Techniques for Urban Environment Video Analytics. In WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI 2022) (pp. 107–118).
|
|
|
|
Cristhian A. Aguilera, Xaver Soria, Angel D. Sappa, & Ricardo Toledo. (2017). RGBN Multispectral Images: a Novel Color Restoration Approach. In 15th International Conference on Practical Applications of Agents and Multi-Agent Systems (Vol. 619, pp. 155–163).
|
|
|
|
Rangnekar, A., Mulhollan, Z., Vodacek, A., Hoffman, M., Sappa, A. D., & Yu, J. et al. (2022). Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 389–397).
|
|
|
|
Victor Santos, Angel D. Sappa, & Miguel Oliveira. (2017). Special Issue on Autonomous Driving an Driver Assistance Systems. In Robotics and Autonomous Systems Journal, Vol. 91, pp. 208–209.
|
|
|
|
Santos V., Angel D. Sappa., & Oliveira M. & de la Escalera A. (2019). Special Issue on Autonomous Driving and Driver Assistance Systems. In Robotics and Autonomous Systems, 121.
|
|
|
|
Armin Mehri, P. B., Dario Carpio, and Angel D. Sappa. (2023). SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution. IEEE access, Vol. 11, 121457–121469.
|
|
|
|
Ángel Morera, Á. S., A. Belén Moreno, Angel D. Sappa, & José F. Vélez. (2020). SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. In Sensors, Vol. 2020-August(16), pp. 1–23.
Abstract: This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)
deep neural networks for the outdoor advertisement panel detection problem by handling multiple
and combined variabilities in the scenes. Publicity panel detection in images o ers important
advantages both in the real world as well as in the virtual one. For example, applications like Google
Street View can be used for Internet publicity and when detecting these ads panels in images, it could
be possible to replace the publicity appearing inside the panels by another from a funding company.
In our experiments, both SSD and YOLO detectors have produced acceptable results under variable
sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex
background and multiple panels in scenes. Due to the diculty of finding annotated images for the
considered problem, we created our own dataset for conducting the experiments. The major strength
of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable
when the publicity contained inside the panel is analyzed after detecting them. On the other side,
YOLO produced better panel localization results detecting a higher number of True Positive (TP)
panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models
with di erent types of semantic segmentation networks and using the same evaluation metrics is
also included.
|
|
