2018 |
|
|
Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2018). Near InfraRed Imagery Colorization. In 25 th IEEE International Conference on Image Processing, ICIP 2018 (pp. 2237–2241).
Abstract: This paper proposes a stacked conditional Generative
Adversarial Network-based method for Near InfraRed
(NIR) imagery colorization. We propose a variant architecture
of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model.
We show that this new architecture/loss-function yields better
generalization and representation of the generated colored IR
images. The proposed approach is evaluated on a large test
dataset and compared to recent state of the art methods using
standard metrics.1
Index Terms—Convolutional Neural Networks (CNN), Generative
Adversarial Network (GAN), Infrared Imagery colorization.
|
|
|
|
Wilton Agila, Gomer Rubio, L. Miranda, & L. Vázquez. (2018). Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell. In 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. (pp. 1221–1226).
Abstract: This work describes an approximate reasoning
technique to deal with the non-linearity that occurs in the
stabilization of the pressure of anodic and cathodic gases of a
proton exchange membrane fuel cell (PEM). The implementation
of a supervisory element in the stabilization of the pressure of the
PEM cell is described. The fuzzy supervisor is a reference
control, it varies the value of the reference given to the classic
low-level controller, Proportional – Integral – Derivative (PID),
according to the speed of change of the measured pressure and
the change in the error of the pressure. The objective of the fuzzy
supervisor is to achieve a rapid response over time of the variable
pressure, avoiding unwanted overruns with respect to the
reference value. A comparative analysis is detailed with the
classic PID control to evaluate the operation of the "fuzzy
supervisor", with different flow values and different sizes of
active area of the PEM cell (electric power generated).
|
|
|
|
Xavier Soria, & Angel D. Sappa. (2018). Improving Edge Detection in RGB Images by Adding NIR Channel. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 266–273).
|
|
2017 |
|
|
Angel J. Valencia, Roger M. Idrovo, Angel D. Sappa, Douglas Plaza G., & Daniel Ochoa. (2017). A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers. In 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) (pp. 1–6).
|
|
