Xavier Soria, Edgar Riba, & Angel D. Sappa. (2020). Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1912–1921).
Abstract: This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.
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Xavier Soria, Angel D. Sappa, & Riad Hammoud. (2018). Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059.Vol. 18(Issue 7).
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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Xavier Soria, Angel D. Sappa, & Arash Akbarinia. (2017). Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities. In The 7th International Conference on Image Processing Theory, Tools and Application (pp. 1–6).
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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).
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Xavier Soria, Y. L., Mohammad Rouhani & Angel D. Sappa. (2023). Tiny and Efficient Model for the Edge Detection Generalization. In Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 (pp. 1356–1365).
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Xavier Soria, A. S., Patricio Humanante, Arash Akbarinia. (2023). Dense extreme inception network for edge detection. Pattern Recognition, Vol. 139.
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Xavier Soria, G. P. - J. & A. S. (2022). LDC: Lightweight Dense CNN for Edge Detection. IEEE Access journal, Vol. 10, pp. 68281–68290.
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Wilton Agila, & Victor M. Huilcapi. (2014). Lógica borrosa para la estimación de estados críticos de una pila de combustible PEM. In Reconocimientos de Patrones, Control Inteligente y Comunicaciones (MACH 2014) (Vol. 5). Universidad de Cuenca.
Abstract: La determinación en tiempo real de los estados críticos de operación de la pila de combustible de membrana intercambio protónico (siglas en ingles, PEM) es uno de los principales retos para los sistemas de control de pilas de combustible PEM. En este trabajo, se presenta el desarrollo e implementación de un método no invasivo de bajo coste basado en técnicas de decisión borrosa que permite estimar los estados críticos de operación de la pila de combustible PEM. La estimación se realiza mediante perturbaciones al estado de operación de la pila y el análisis posterior de la evolución temporal del voltaje generado por la pila. La implementación de esta técnica de estimulación-percepción de estado de la pila de combustible para la detección de estados críticos constituye una novedad y un paso hacia el control autónomo en óptimas condiciones de la operación de las pilas de combustible PEM.
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Wilton Agila, Ricardo Cajo, & Douglas Plaza. (2015). Experts Agents in PEM Fuel Cell Control. In 4ta International Conference on Renewable Energy Research and Applications (pp. 896–900). Palermo, Italy: IEEE.
Abstract: In the control of the PEM (Proton Exchange Membrane) fuel cell, the existence of both deliberative and reactive processes that facilitate the tasks of control resulting from a wide range of operating scenarios and range of conditions it is required. The latter is essential to adjust its parameters to the multiplicity of circumstances that may occur in the operation of the PEM stack. In this context, the design and development of an expert-agents based architecture for autonomous control of the PEM stack in top working conditions is presented. The architecture integrates perception and control algorithms using sensory and context information. It is structured in a hierarchy of levels with different time window and level of abstraction. The monitoring model and autonomic control of PEM stack has been validated with different types of PEM stacks and operating conditions demonstrating high reliability in achieving the objective of the proposed energy efficiency. Dynamic control of the wetting of the membrane is a clear example.
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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).
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