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G.A. Rubio; Wilton Agila |
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Transients analysis in Proton Exchange Membrane Fuel Cells: A critical review |
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2019 |
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8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania |
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249-252 |
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When a proton exchange fuel cell operates it produces in addition to electrical
energy, heat and water as sub products, which impact on the performance of the cell. This
paper analyzes the issue of transients and proposes a model that describes the dynamic
operation of the fuel cell. The model considers the transients produced by electrochemical
reactions, by flow water and by heat transfer. Two-phase flow transients result in
increased the parasitic power losses and thermal transients may result in flooding or dryout of the GDL and membrane, understanding transient behavior is critical for reliable
and predictable performance from the cell. |
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gtsi @ user @ |
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111 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel D. Sappa and Henry O. Velesaca |
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Transformer based Image Dehazing. |
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Conference Article |
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2022 |
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16TH International Conference On Signal Image Technology & Internet Based Systems SITIS 2022. |
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148-154 |
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cidis @ cidis @ |
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195 |
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Author |
Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca |
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Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem |
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2020 |
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The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 |
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4 |
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498-505 |
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Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. |
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This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model
a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The
transfer learning consist of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose
estimation accuracy using the proposed model, as well as further improvements when the transfer learning
strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on
the training due to the reduced number of pairs of real-images on most of the public data sets. |
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978-989758402-2 |
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120 |
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Silva Steven, Paillacho Dennys, Verdezoto Nervo, Hernandez Juan David |
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TOWARDS ONLINE SOCIALLY ACCEPTABLE ROBOT NAVIGATION |
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2022 |
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IEEE INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, |
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2022-August |
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707-714 |
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cidis @ cidis @ |
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199 |
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