| Records |
| Author |
Rubio Abel; Agila Wilton; González Leandro; Aviles Jonathan |
| Title |
A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells |
Type |
Conference Article |
| Year |
2023 |
Publication  |
12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 |
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273 - 278 |
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979-835033793-8 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
230 |
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| Author |
Wilton Agila, Gomer Rubio, Raul M. del Toro, Livington Miranda |
| Title |
Qualitative model for an oxygen therapy system based on Renewable Energy |
Type |
Conference Article |
| Year |
2023 |
Publication |
12th International Conference on Renewable Energy Research and Applications (ICRERA 2023) Oshawa 29 August – 1 September 2023 |
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Pages |
365–371 |
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979-835033793-8 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
219 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture. |
Type |
Conference Article |
| Year |
2017 |
Publication |
13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) |
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2017-July |
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Pages |
212-217 |
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no |
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cidis @ cidis @ |
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62 |
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| Author |
Henry O. Velesaca, Juan Antonio Holgado-Terriza, Doménica Carrasco, José Miguel Gutiérrez Guerrero, Tonny Toscano, Darío Carpio & Angel Sappa |
| Title |
Anomaly Detection in Industrial Production Products using OPC-UA and Deep Learning |
Type |
Conference Article |
| Year |
2024 |
Publication |
13th International Conference on Data Science, Technology and Applications, DATA 2024 Dijon 9-11 July 2024 |
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Issue |
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Pages |
505 - 512 |
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978-989758707-8 |
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no |
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cidis @ cidis @ |
Serial |
232 |
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| Author |
Eduardo Benavides, Abel Rubio Farías, Wilton Agila Roldán & Luis Muñoz Zurita |
| Title |
Implementation of Proton Exchange Membrane Fuel Cells in Agricultural Areas: Technical and Economic Benefits |
Type |
Conference Article |
| Year |
2024 |
Publication |
13th International Conference on Renewable Energy Research and Applications |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
258 |
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| Author |
Patricia Pasmay Bohorquez, Abel Rubio Roldán, Homero Ojeda Guevara, Sixifo Falcones |
| Title |
Transmission Expansion Planning with Photovoltaic Generation Penetration |
Type |
Conference Article |
| Year |
2024 |
Publication |
13th International Conference on Renewable Energy Research and Applications |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
259 |
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| Author |
Xavier Soria; Angel D. Sappa |
| Title |
Improving Edge Detection in RGB Images by Adding NIR Channel. |
Type |
Conference Article |
| Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
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Issue |
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Pages |
266-273 |
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Approved |
no |
| Call Number |
gtsi @ user @ |
Serial |
95 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach. |
Type |
Conference Article |
| Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
Abbreviated Journal |
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Issue |
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Pages |
358-364 |
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| Abstract |
This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
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no |
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gtsi @ user @ |
Serial |
92 |
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| Author |
Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa |
| Title |
Deep learning based camera pose estimation in multi-view environment. |
Type |
Conference Article |
| Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
224-228 |
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| Abstract |
This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
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Approved |
no |
| Call Number |
gtsi @ user @ |
Serial |
93 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
| Title |
Deep Learning based Single Image Dehazing |
Type |
Conference Article |
| Year |
2018 |
Publication |
14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
This paper proposes a novel approach to remove haze
degradations in RGB images using a stacked conditional
Generative Adversarial Network (GAN). It employs a triplet
of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a
conditional probabilistic model, is proposed. The proposed
GAN architecture learns to remove the haze, using as conditioned
entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast
model training convergence and a homogeneous model generalization.
Experiments showed that the proposed method
generates high-quality clear images. |
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Approved |
no |
| Call Number |
gtsi @ user @ |
Serial |
83 |
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