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Wilton Agila, Gomer Rubio, Francisco Vidal, & B. Lima. (2019). Real time Qualitative Model for estimate Water content in PEM Fuel Cell. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 455–459).
Abstract: To maintain optimum performance of the electrical
response of a fuel cell, a real time identification of the
malfunction situations is required. Critical fuel cell states depend,
among others, on the variable demand of electric load and are
directly related to the membrane hydration level. The real time
perception of relevant states in the PEM fuel cell states space, is
still a challenge for the PEM fuel cell control systems. Current
work presents the design and implementation of a methodology
based upon fuzzy decision techniques that allows real time
characterization of the dehydration and flooding states of a PEM
fuel cell. Real time state estimation is accomplished through a
perturbation-perception process on the PEM fuel cell and further
on voltage oscillation analysis. The real time implementation of
the perturbation-perception algorithm to detect PEM fuel cell
critical states is a novelty and a step forwards the control of the
PEM fuel cell to reach and maintain optimal performance.
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Shendry Rosero Vásquez. (2019). Reconocimiento facial: técnicas tradicionales y técnicas de aprendizaje profundo, un análisis. (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). M.Sc. thesis. In Ediciones FIEC-ESPOL.
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Angel Morera, Angel Sánchez, Angel D. Sappa, & José F. Vélez. (2019). Robust Detection of Outdoor Urban Advertising Panels in Static Images. In 17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science (Vol. 1047, pp. 246–256).
Abstract: One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising
panels. For such a purpose, a previous stage is to accurately detect and
locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based
on a deep neural network architecture that minimizes the number of
false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection
over Union (IoU) accuracy metric make this proposal applicable in real
complex urban images.
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Santos V., Angel D. Sappa., & Oliveira M. & de la Escalera A. (2019). Special Issue on Autonomous Driving and Driver Assistance Systems. In Robotics and Autonomous Systems, 121.
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G.A. Rubio, & Wilton Agila. (2019). Sustainable Energy: A Strategic View of Fuel Cells. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 239–243).
Abstract: Based on the model of the proton exchange fuel cell in a strategic context,
this document develops the issue of energy as one of the pillars to achieve the
sustainability of our planet, considering the future scenarios up to the year 2060 of the
situation energy, hydrogen as a strategic vector and the contribution of the fuel cell in
solving the serious problems of environmental pollution and economic inequity that
humanity faces; for its application in the energy generation, telecommunications and
vehicle manufacturing industries.
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Rafael E. Rivadeneira, Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Thermal Image SuperResolution through Deep Convolutional Neural Network. In 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá (pp. 417–426).
Abstract: Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset
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Marjorie Chalen, & Boris X. Vintimilla. (2019). Towards Action Prediction Applying Deep Learning. Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019, , pp. 1–3.
Abstract: Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.
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G.A. Rubio, & Wilton Agila. (2019). Transients analysis in Proton Exchange Membrane Fuel Cells: A critical review. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 249–252).
Abstract: When a proton exchange fuel cell operates it produces in addition to electrical
energy, heat and water as sub products, which impact on the performance of the cell. This
paper analyzes the issue of transients and proposes a model that describes the dynamic
operation of the fuel cell. The model considers the transients produced by electrochemical
reactions, by flow water and by heat transfer. Two-phase flow transients result in
increased the parasitic power losses and thermal transients may result in flooding or dryout of the GDL and membrane, understanding transient behavior is critical for reliable
and predictable performance from the cell.
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Stalin Francis Quinde. (2019). Un nuevo modelo BM3D-RNCA para mejorar la estimación de la imagen libre de ruido producida por el método BM3D. (Ph.D. Angel Sappa, Director.). M.Sc. thesis. In Ediciones FIEC-ESPOL.
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José Reyes, Axel Godoy, & Miguel Realpe. (2019). Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. In International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica (Vol. 2019-July).
Abstract: Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI.
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