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Author (up) Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez pdf  openurl
  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  
  Keywords  
  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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  Call Number gtsi @ user @ Serial 88  
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Author (up) Xavier Soria; Angel D. Sappa openurl 
  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 (up) Xavier Soria; Angel D. Sappa; Riad Hammoud pdf  openurl
  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  
  Keywords  
  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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