|
|
Rubio, G. A., Agila, W.E. (2021). A fuzzy model to manage water in polymer electrolyte membrane fuel cells. In Processes Journal. (Article number 904), Vol. 9(Issue 6).
Abstract: In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits.
|
|
|
|
Rubio Abel, Agila Wilton, González Leandro, & Aviles Jonathan. (2023). A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells. In 12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 (pp. 273–278).
|
|
|
|
Rosero Vasquez Shendry. (2020). Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020.615–625.
|
|
|
|
Roberto Jacome Galarza, Miguel-Andrés Realpe-Robalino, Chamba-Eras LuisAntonio, & Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco. (2019). Computer vision for image understanding. A comprehensive review. In International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador (pp. 248–259).
Abstract: Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
|
|
|
|
Roberto Jacome Galarza. (2022). Multimodal deep learning for crop yield prediction. In Doctoral Symposium on Information and Communication Technologies –DSICT 2022. Octubre 12-14. (Vol. 1647, pp. 106–117).
|
|
|
|
Rivadeneira, R. E., & Sappa, A. D. and V. B. X. (2022). Thermal Image Super-Resolution: A Novel Unsupervised Approach. In Communications in Computer and Information Science, 15th International Communications in Computer and Information Science Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 1474, pp. 495–506).
|
|
|
|
Rivadeneira R.E., S. A. D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. (2021). Thermal Image Super-Resolution Challenge – PBVS 2021. In In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 (pp. 4354–4362).
|
|
|
|
Ricaurte P, Chilán C, Cristhian A. Aguilera, Boris X. Vintimilla, & Angel D. Sappa. (2014). Feature Point Descriptors: Infrared and Visible Spectra. Sensors Journal, Vol. 14, pp. 3690–3701.
Abstract: This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
|
|
|
|
Raul A. Mira, Patricia L. Suarez, Rafael E. Rivadeneira, & Angel D. Sappa. (2019). PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization. In IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador (pp. 1–6).
Abstract: This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different
communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic.
|
|
|
|
Rangnekar, A., Mulhollan, Z., Vodacek, A., Hoffman, M., Sappa, A. D., & Yu, J. et al. (2022). Semi-Supervised Hyperspectral Object Detection Challenge Results-PBVS 2022. In Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 389–397).
|
|
