Design and construction of automated equipment for separating mixtures

There are various forms of environmental pollution, one of which is caused by oily mixtures which are particularly discharged into water sources from economic activities such as washing vehicles, service stations, and others. This article research shows a way to mitigate these polluting effects through the design and construction of an automated equipment that performs the separation of a mixture of water-oil. The separation of the oil mixture is carried out in a corrugated plate interceptor (CPI), and accompanied by a Programmable Logic Controller (PLC) S7 1200, through which the user is allowed to perform the process efficiently, thanks to high percentage of separation has been achieved with respect to the initial mixture. Then, was divided into three methodological steps , first the CPI separator built, setting turn the variables of interest, second was designed and implemented the Automation and third results were validated by testing. Three variables were defined; level, the most important in the system, also temperature and flow, for each of them has been implemented appropriate instrumentation for data collection subsequently entering the PLC. The main result is that the process is efficient as it manages to recover more than 90% of the water present in the initial oily fluid, this evidenced by the implemented instrumentation , allowing reuse and subsequent saving precious liquid

Automatic food bio-hazard detection system

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

Comparison of convolutional neural network models for user’s facial recognition

This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time

Embedded fuzzy controller for water level control

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

Virtual environment for assistant mobile robot

This paper shows the development of a virtual environment for a mobile robotic system with the ability to recognize basic voice commands, which are oriented to the recognition of a valid command of bring or take an object from a specific destination in residential spaces. The recognition of the voice command and the objects with which the robot will assist the user, is performed by a machine vision system based on the capture of the scene, where the robot is located. In relation to each captured image, a convolutional network based on regions is used with transfer learning, to identify the objects of interest. For human-robot interaction through voice, a convolutional neural network (CNN) of 6 convolution layers is used, oriented to recognize the commands to carry and bring specific objects inside the residential virtual environment. The use of convolutional networks allowed the adequate recognition of words and objects, which by means of the associated robot kinematics give rise to the execution of carry/bring commands, obtaining a navigation algorithm that operates successfully, where the manipulation of the objects exceeded 90%. Allowing the robot to move in the virtual environment even with the obstruction of objects in the navigation path.&lt

Paper biological risk detection through deep learning and fuzzy system

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%

A Review of Energy Management of Renewable Multisources in Industrial Microgrids

This review aims to consolidate recent advancements in power control within microgrids and multi-microgrids. It specifically focuses on analyzing the comparative benefits of various architectures concerning energy sharing and demand cost management. The paper provides a comprehensive technical analysis of different architectures found in existing literature, which are designed for energy management and demand cost optimization. In summary, this review paper provides a thorough examination of power control in microgrids and multi-microgrids and compares different architectural approaches for energy management and demand cost optimization

Ambulance detection for smart traffic light applications with fuzzy controller

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

Uso de aplicaciones móviles como herramienta de apoyo tecnológico para la enseñanza con metodología steam

This document demonstrates in a practical way how mobile applications can be considered as a great tool in the teaching of various areas in elementary school such as: mathematics, English, social studies, Spanish, among others. It is important to note how teachers can support their teaching processes through the use of mobile devices, in which simple to use applications are installed for students, easy to understand and manage. Particularly applications such as SAIA v1.0, GROBOT v1.0, STEAM MATH v1.0 and STUDENT STEAM v 1.0 that have been custom designed and made available to primary school institutions, in order to support their processes, obtaining excellent results, representing for them practical tools, light portability and easy handling, but above all are applications that allow to strengthen the knowledge acquired without the presence of the teacher.En este documento se demuestra de forma práctica como las aplicaciones móviles pueden ser consideradas como una gran herramienta, en la enseñanza de diversas áreas en básica primaria como son: matemáticas, inglés, sociales, español, entre otras. Es importante anotar cómo los docentes pueden apoyar sus procesos de enseñanza, a través del uso de dispositivos móviles, en los cuales se instalan aplicaciones sencillas de usar para los estudiantes, de fácil entendimiento y manejo. Particularmente aplicaciones como SAIA v1.0, GROBOT v1.0, STEAM MATH v1.0 y STUDENT STEAM v 1.0 que han sido diseñadas a la medida y puestas a disposición de instituciones de básica primaria, con la finalidad de apoyar sus procesos, obteniendo excelentes resultados, representando para ellos herramientas prácticas, de liviana portabilidad y de fácil manipulación, pero sobre todo son aplicaciones que permiten afianzar los conocimientos adquiridos sin la presencia del docente. Educational mobile applications have gained strength in the teaching and learning processes, for this reason, the objective of this paper is to demonstrate how these mobile applications can be considered a great tool in teaching using steam methodology. The paper has a descriptive character, considering the way in which the different forms of learning are supported by mobile devices and are becoming new teaching models. The topics addressed, the services received, the content exposed, among others, are analyzed. The results show a positive impact after the use of the applications on the performance, motivation, attitude and interest in learning of the students in the topics developed, as well as the satisfaction of the teachers for the support of the mobile applications in their work, and how the applications allow reinforcing the knowledge acquired without the presence of the teacher

Design of the electric propulsion system for dumper trucks

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