Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Robinson Jimenez Moreno
Universidad Militar Nueva Granada
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

 1 
Embedded fuzzy controller for water level control Javier Eduardo Martinez Baquero; Jairo Cuero Ortega; Robinson Jimenez Moreno
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp277-284

Abstract

This article presents the design of a fuzzy controller embedded in a microcontroller aimed at implementing a low-cost, modular process control system. The fuzzy system's construction is based on a classical proportional and derivative controller, where inputs of error and its derivate depend on the difference between the desired setpoint and the actual level; the goal is to control the water level of coupled tanks. The process is oriented to control based on the knowledge that facilitates the adjustment of the output variable without complex mathematical modeling. In different response tests of the fuzzy controller, a maximum over-impulse greater than 8% or a steady-state error greater than 2.1% was not evidenced when varying the setpoint.
Automatic food bio-hazard detection system Robinson Jimenez Moreno; Javier Eduardo Martinez Baquero
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2652-2659

Abstract

This paper presents the design of a convolutional neural network architecture oriented to the detection of food waste, to generate a low, medium, or critical-level alarm. An architecture based on four convolution layers is used, for which a database of 100 samples is prepared. The database is used with the different hyperparameters that make up the final architecture, after the training process. By means of confusion matrix analysis, a 100% performance of the network is obtained, which delivers its output to a fuzzy system that, depending on the duration of the detection time, generates the different alarm levels associated with the risk.
Co-Authors Daiam Camilo Parrado Nieto Jairo Cuero Ortega Jairo David Cuero Ortega Javier Eduardo Martinez Baquero Oscar Ivan Vargas Mora