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Ulises Gildardo Quiroz Antúnez, Alejandro Ismael Monterroso Rivas, María Fernanda Calderón Vega, Adán Guillermo Ramírez García |
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APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE |
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2022 |
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Granja |
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Vol. 36 |
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Issue 2 |
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cidis @ cidis @ |
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200 |
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Author |
Abel Rubio, Wilton Agila, Leandro González & Jonathan Aviles-Cedeno |
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Title |
Distributed Intelligence in Autonomous PEM Fuel Cell Control. |
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Journal Article |
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2023 |
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Energies 2023 |
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Vol. 16 |
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Issue 12 |
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19961073 |
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cidis @ cidis @ |
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217 |
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Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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Morocho-Cayamcela, M.E. & W. Lim |
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Lateral confinement of high-impedance surface-waves through reinforcement learning |
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2020 |
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Electronics Letters |
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Vol. 56 |
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23, 12 November 2020 |
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pp. 1262-1264 |
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The authors present a model-free policy-based reinforcement learning
model that introduces perturbations on the pattern of a metasurface.
The objective is to learn a policy that changes the size of the
patches, and therefore the impedance in the sides of an artificially structured
material. The proposed iterative model assigns the highest reward
when the patch sizes allow the transmission along a constrained path
and penalties when the patch sizes make the surface wave radiate to
the sides of the metamaterial. After convergence, the proposed
model learns an optimal patch pattern that achieves lateral confinement
along the metasurface. Simulation results show that the proposed
learned-pattern can effectively guide the electromagnetic wave
through a metasurface, maintaining its instantaneous eigenstate when
the homogeneity is perturbed. Moreover, the pattern learned to
prevent reflections by changing the patch sizes adiabatically. The
reflection coefficient S1, 2 shows that most of the power gets transferred
from the source to the destination with the proposed design. |
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English |
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cidis @ cidis @ |
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139 |
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