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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Javier Eduardo Martinez Baquero
Universidad de los Llanos
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.
Ambulance detection for smart traffic light applications with fuzzy controller Robinson Jimenez-Moreno; Javier Eduardo Martinez Baquero; Luis Alfredo Rodriguez Umaña
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4876-4882

Abstract

In the development of intelligent cities, the automation of vehicular mobility is one of the strong points of research, where intelligent traffic lights stand out. It is essential in this field to prioritize emergency vehicles that can help save lives, where every second counts in favor of the transfer of a patient or injured person. This paper presents an artificial intelligence algorithm based on two stages, one is the recognition of emergency vehicles through a ResNet-50 and the other is a fuzzy inference system for timing control of a traffic light, both lead to an intelligent traffic light. An application of traffic light vehicular flow control for automatic preemption when detecting emergency vehicles, specifically ambulances, is oriented. The training parameters of the network, which achieves 100% accuracy with confidence levels between 65% with vehicle occlusion and 99% in direct view, are presented. The traffic light cycles are able to extend the green time of the traffic light with almost 50% in favor of the road that must yield the priority, in relation to not using the fuzzy inference system.
Paper biological risk detection through deep learning and fuzzy system Juan Sebastian Sanabria; Robinson Jimenez-Moreno; Javier Eduardo Martinez Baquero
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp249-257

Abstract

Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.
Design of the electric propulsion system for dumper trucks Walter Naranjo Lourido; Luis Ariel Riaño Ocampo; Gustavo Andres Gallego Chipiaje; Javier Eduardo Martinez Baquero; Luis Alfredo Rodriguez Umaña
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.pp2546-2554

Abstract

This article designs a high-efficiency electric propulsion system for industrial trucks, such as dumper trucks. This design proposes using an alternative energy storage system of green H2 hydrogen to reduce emissions. This design determines the propulsion systems' technical and power requirements, starting with each vehicle's driving and duty cycles. For this analysis, a longitudinal dynamic model is created, with which the behavior of the energy conversion chain of the propulsion system is established. The evolutionary methodology analyzes the dynamic forces of vehicle interaction to size the propulsion system's components and the storage system. Using green H2 as fuel allows an energy yield three times higher than diesel. In addition, using this green hydrogen prevents the emission of 264,172 kg of CO₂, which the dumper emits when consuming 1,000 daily gallons of diesel within its working day.
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 Fredy A. Sanz Gustavo Andres Gallego Chipiaje Jairo Cuero Ortega Jairo David Cuero Ortega Juan Sebastian Sanabria Luis Alfredo Rodriguez Umaña Luis Ariel Riaño Ocampo Oscar Ivan Vargas Mora Robinson Jimenez Moreno Robinson Jimenez-Moreno Walter Naranjo Lourido Walter Naranjo Lourido