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Author  |
Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez | ||||
| Title | Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell | Type | Conference Article | ||
| Year | 2018 | Publication | 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. | Abbreviated Journal | |
| Volume | Issue | Pages | 1221-1226 | ||
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| 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). |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 88 | ||
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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) | Abbreviated Journal | |
| Volume | Issue | Pages | 266-273 | ||
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| Call Number | gtsi @ user @ | Serial | 95 | ||
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| Author |
Xavier Soria; Angel D. Sappa; Riad Hammoud | ||||
| Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. | Type | Journal Article | ||
| Year | 2018 | Publication | Abbreviated Journal | ||
| Volume | Vol. 18 | Issue | Issue 7 | Pages | |
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| Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 96 | ||
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