Latin American Journal of Computing
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Single-Phase Variable Reluctance Motor Design using Particle Swarm Optimization
Get PDFElectrical engines are built under electromagnetism concepts to create mechanical power, those can be seen as simple machines, as it depends on reluctance, even being called as “reluctance motor”, what gives this engine the possibility of being widely used for many purposes. The main objective of this research is to minimize copper losses of a single-phase 6x6 variable reluctance synchronous motor. For that, a Particle Swarm Optimization (PSO) algorithm will be used to obtain the optimum configuration through the Finite Elements Method (FEM). In this context, electric motor design equations were dimensioned based on similar machines. The next procedure was to use FEMM (Finite Element Method Magnetics) software, that allows the magnetic flow density analysis inside the motor air gap. Finally, it is noteworthy that the copper losses results were analyzed before and after the variable reluctance motor optimization with computational tools.Electrical engines are built under electromagnetism concepts to create mechanical power, those can be seen as simple machines, as it depends on reluctance, even being called as “reluctance motor”, what gives this engine the possibility of being widely used for many purposes. The main objective of this research is to minimize copper losses of a single-phase 6x6 variable reluctance synchronous motor. For that, a Particle Swarm Optimization (PSO) algorithm will be used to obtain the optimum configuration through the Finite Elements Method (FEM). In this context, electric motor design equations were dimensioned based on similar machines. The next procedure was to use FEMM (Finite Element Method Magnetics) software, that allows the magnetic flow density analysis inside the motor air gap. Finally, it is noteworthy that the copper losses results were analyzed before and after the variable reluctance motor optimization with computational tools
Esquemas esencialmente no oscilatorios aplicados a la ecuación de Buckley-Leverett con término difusivo
Get PDFThe purpose of this work was to investigate the flow of two-phase fluids via the Buckley-Leverett equation, corresponding to three types of scenarios applied in oil extraction, including a diffusive term. For this, a weighted essentially non-oscillatory scheme, a Runge-Kutta method and a central finite difference were computationally implemented. In addition, a numerical study related to the precision order and stability was performed. The use of these methods made it possible to obtain numerical solutions without oscillations and without excessive numerical dissipation, sufficient to assist in the understanding of the mixing profiles of saturated water and petroleum fluids, inside pipelines filled with porous material, in addition to allowing the investigation of the impact of adding the diffusive term in the original equation.El propósito de este trabajo fue investigar el flujo de fluidos bifásicos a través de la ecuación de Buckley-Leverett, correspondiente a tres tipos de escenarios aplicados en la extracción de petróleo, incluyendo un término difusivo. Para ello se implementaron computacionalmente un esquema esencialmente no oscilatorio ponderado , un método de Runge-Kutta y un esquema de diferencias finitas centrado. Además, se realizó un estudio numérico relacionado con el orden de precisión y estabilidad. El uso de estos métodos permitió obtener soluciones numéricas sin oscilaciones y sin disipación numérica excesiva, suficientes para auxiliar en la comprensión de los perfiles de mezcla de agua saturada y fluidos derivados del petróleo, en el interior de tuberías llenas de material poroso, además de permitir la investigación del impacto de sumar el término difusivo en la ecuación original
Pappus-Guldin theorems applied the study of solid modeling with GeoGebra software
Get PDFIn this work, we use Geogebra software to simulate the shape of objects (solids) in three dimensions from their photo and real dimensions using spline interpolation. With the reconstructed object, we analyze its volume and surface area using the Pappus-Guldin Theorems (PGT), the theorems that use mathematical analysis ideas to describe the volume and surface area by the sectional area and by the contour curve of the object. In the simulations, we tested the verification of the modeling for known solids (sphere and torus) and then analyzed some objects used in the industry, such as the packaging of products, pet bottles, yogurt containers, coffee powder packaging, aluminum soda cans, and the packaging of chocolate powder. We also analyzed some objects created by rotating bodies, such as the shape of a jar and an aluminum barrel, and also shapes found in nature, such as the shape of a pear and an egg. Modeling allows us to better understand the packaging used in the industry to minimize manufacturing costs and maximize its utility. Thus, we can modify these packages to obtain the best development of how these products are presented to the public, optimizing its format by analyzing its surface and its volume.In this work, we use Geogebra software to simulate the shape of objects (solids) in three dimensions from their photo and real dimensions using spline interpolation. With the reconstructed object, we analyze its volume and surface area using the Pappus-Guldin Theorems (PGT), the theorems that use mathematical analysis ideas to describe the volume and surface area by the sectional area and by the contour curve of the object. In the simulations, we tested the verification of the modeling for known solids (sphere and torus) and then analyzed some objects used in the industry, such as the packaging of products, pet bottles, yogurt containers, coffee powder packaging, aluminum soda cans, and the packaging of chocolate powder. We also analyzed some objects created by rotating bodies, such as the shape of a jar and an aluminum barrel, and also shapes found in nature, such as the shape of a pear and an egg. Modeling allows us to better understand the packaging used in the industry to minimize manufacturing costs and maximize its utility. Thus, we can modify these packages to obtain the best development of how these products are presented to the public, optimizing its format by analyzing its surface and its volume
An Approach for Optimizing Resource Allocation and Usage in Cloud Computing Systems by Predicting Traffic Flow
Get PDFThe cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA\u27s prediction of 79.8% and XGBoost\u27s prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA\u27s prediction of 79.8% and XGBoost\u27s prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation
Editorial
Get PDFThe constant evolution of Computer Science challenges researchers to push the boundaries of innovation within a multidisciplinary landscape. From the Editorial of the Latin-American Journal of Computing, we are pleased to present to our readership this number, which showcases cutting-edge research in different applications of this field. The first article explores the application of Pappus-Guldin Theorems in solid modeling using spline interpolation. Here, researchers demonstrate the potential of mathematical analysis in order to deliver more cost-effective computing-based solutions to possibly optimize industrial packaging design. Similarly, in the second article, numerical modeling is used to overcome the limitations of traditional approaches for analyzing linear elastic fracture mechanics by comparing the results obtained using commercial and open- source platforms.
Conversely, the work featured in the third article presents essentially non-oscillatory schemes for understanding the flow of two-phase fluids in oil extraction scenarios. Numerical methods are successfully employed to analyze mixing profiles of saturated water and petroleum fluids, demonstrating their importance for understanding fluid dynamics in porous materials. Likewise, the fourth article explores the usage of particle swarm optimization for enhancing the efficiency of a single-phase variable reluctance motor design. The authors demonstrate that minimizing copper losses is possible through finite element method analysis.
Addressing the evolving landscape of cybersecurity is the focus of the fifth article. The authors introduce a methodology for categorizing and updating attacks on web services, which contributes to a better understanding of vulnerabilities for preventing web-based attacks. In addition, the sixth article discusses the optimization of resource allocation on Cloud Computing by predicting traffic flow. The researchers employ machine learning models like ARIMA, Monte Carlo, and XGBoost for such predictive analysis.
Finally, the seventh and eight articles cover medical diagnosis and educational needs, respectively. In the former, an early-diagnosis method for Alzheimer\u27s is featured using magnetic resonance imaging and the VGG16 Algorithm. The authors justify the effectiveness of employing AI to aid the diagnosis of such disease with a capacity exceeding 82 per cent. In the latter, machine learning and text mining techniques are used to explore open educational resources (OER) for automatically identifying topics, enhancing their description and categorization.
In conclusion, the articles brought to you in this number provides a unique perspective to the different applications of Computer Science, and the dynamic nature of the research carried out in this contemporary discipline. Thanks to the authors who contributed to the ever-growing body of knowledge in this field, wishing them, and all our readers, a successful year 2024.
“Let science be the vessel to carry our dreams beyond the limits of our imagination”La constante evolución de las Ciencias de la Computación desafía a los investigadores a ampliar los límites de la innovación dentro de un panorama multidisciplinario. Desde la Editorial de la Revista Latin-American Journal of Computing, tenemos el agrado de presentar a nuestros lectores este número, el cual muestra investigaciones de vanguardia en diferentes aplicaciones de este campo. El primer artículo explora la aplicación de los teoremas de Pappus-Guldin en el modelado de sólidos mediante interpolación spline. Aquí, los investigadores demuestran el potencial del análisis matemático para ofrecer soluciones informáticas más rentables para posiblemente optimizar el diseño de envases industriales. De manera similar, en el segundo artículo, se utiliza el modelado numérico para superar las limitaciones de los enfoques tradicionales en el análisis de la mecánica de fracturas elásticas lineales, comparando los resultados obtenidos utilizando plataformas comerciales y de código abierto.
Por el contrario, el trabajo presentado en el tercer artículo presenta esquemas esencialmente no oscilatorios para comprender el flujo de fluidos de dos fases en escenarios de extracción de petróleo. Los métodos numéricos se emplean con éxito para analizar los perfiles de mezcla de agua saturada y fluidos derivados del petróleo, lo que demuestra su importancia para comprender la dinámica de fluidos en materiales porosos. Así mismo, el cuarto artículo explora el uso de la optimización por enjambre de partículas para mejorar la eficiencia de un diseño de motor monofásico de reluctancia variable. Los autores demuestran que es posible minimizar las pérdidas de cobre mediante el análisis del método de elementos finitos.
El enfoque del quinto artículo aborda el panorama cambiante de la ciberseguridad. Los autores presentan una metodología para categorizar y actualizar ataques a servicios web, lo que contribuye a una mejor comprensión de las vulnerabilidades para prevenir estos ataques. Adicionalmente, el sexto artículo analiza la optimización de la asignación de recursos en Cloud Computing mediante la predicción del flujo de tráfico. Los investigadores emplean modelos de aprendizaje automático como ARIMA, Monte Carlo y XGBoost para dicho análisis predictivo.
Finalmente, los artículos séptimo y octavo cubren el diagnóstico médico y las necesidades educativas, respectivamente. En el primero, se presenta un método de diagnóstico temprano del Alzheimer mediante resonancia magnética y el algoritmo VGG16. Los autores justifican la eficacia del empleo de la IA para ayudar al diagnóstico de dicha enfermedad con una capacidad superior al 82 por ciento. En el último artículo, se utilizan técnicas de aprendizaje automático y minería de textos para explorar recursos educativos abiertos (REA) para identificar tópicos automáticamente, mejorando su descripción y categorización.
En conclusión, los artículos presentados en este número brindan una perspectiva única de las diferentes aplicaciones de las Ciencias de la Computación y la naturaleza dinámica de la investigación llevada a cabo en esta disciplina contemporánea. Gracias a los autores que contribuyeron al creciente cuerpo de conocimiento de este campo, deseándoles a ellos y a todos nuestros lectores un exitoso año 2024.
“Dejemos que la ciencia sea el vehículo para llevar nuestros sueños más allá de los límites de nuestra imaginación”
Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks
Get PDFDeep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images
Identification of Nano-Beams Rigidity Coefficient: A Numerical Analysis Using the Landweber Method
Get PDFDue to their supporting function, beams are one of the main elements in structural projects. With the intense technological development in the field of nanotechnology, beams at micro- and nanoscales have become objects of intense study and research interest, see for example [8]. In this approach, we analyze numerically the inverse problem of identifying the stiffness coefficient in micro-nano-beams as a function that implicitly depends on the fractal media map for the continuum from strain measurements. Such a problem is unstable with respect to noise in strain measurements, which is inherent in practical problems. We introduce the equations that compose Landweber\u27s iterative regularization method as a strategy to obtain a stable and convergent approximate solution with respect to the noise level in the measurements. We show some scenarios with simulated data for identifying the stiffness coefficient for different noise levels in measurements and for different coefficient of transformation of fractal medium. The results found numerically show that Landweber\u27s method is a regularization strategy for the problem of identifying the stiffness coefficient in micro/nano-beams
Gamificación en Matemáticas
Get PDFThe application of gamification in education is scarce; therefore, the purpose of this study is to consider the contribution of Gamification in the learning of Mathematics in eighth grade students of General Basic Education. The methodology used has a quali-quantitative approach, of exploratory experimental type, based on the survey technique with the questionnaire instrument, instrument validated with Cronbach\u27s alpha with a result of 0.846, on a Likert scale of 5 points, applied to a sample of 30 students with a pretest and a posttest. Thus, it was determined the non-use of gamification resources for the teaching of mathematics, so that an intervention was performed with the developed author resources of the web 3. 0 by means of the A.D.D.I.E. methodology in the Canva, Liveworksheet and Nearpood applications. To measure the results and test the hypothesis, chi-square statistics and the Kolmogorov-Smirnov test were used, finding that gamification contributes to the learning of mathematics and generates interactive classes awakening the students\u27 attention.La aplicación de la gamificación en educación es escaza; por consiguiente, el propósito de este estudio es considerar el aporte de la Gamificación en el aprendizaje de Matemática en los estudiantes de octavo año de Educación General Básica. La metodología utilizada tiene un enfoque cuali - cuantitativo, de tipo experimental exploratorio, basado en la técnica de la encuesta con el instrumento cuestionario, instrumento validado con el alfa de Cronbach con un resultado de 0,846, en una escala de Likert de 5 puntos, aplicada a una muestra de 30 estudiantes con un pretest y un postest. De ahí, se determinó la falta de uso de recursos de gamificación para la enseñanza de la matemática, por lo que se realizó una intervención con el desarrollo de recursos de autor de la web 3.0 mediante la metodología A.D.D.I.E en las aplicaciones Canva, Liveworksheet y Nearpood. Para medir los resultados y comprobación de la hipótesis se utilizaron los estadísticos de chi–cuadrado y la prueba de Kolmogorov-Smirnov, encontrándose que la gamificación aporta en el aprendizaje de la Matemática y genera clases interactivas lo cual despierta la atención del estudiantado
Deflection Analysis of Beams from Vehicle Velocity
Get PDFIn this work, the modeling and calculations referring to the deflection of special artworks are presented. The type train is modeled as two degrees of freedom and mobile base, with the bridge deck being considered the mobile base. The base is treated as an elastic beam, according to the Euler-Bernoulli theory. The fundamental assumption made is that the relative displacements between the vehicle and the bridges are synchronous. This allows the calculation of natural frequencies, eigenvalues and normal modes of vibration of the beam. The temporal response of the beam deflection is obtained, assuming that the vehicle employs, at each instant of time, an impulse load on the beam. Numerical simulations are performed to improve the understanding of the dynamic behavior of the structure
Modelos de Aprendizaje Automático basados en CRISP-DM para el Análisis de los niveles de Depresión en los estudiantes de la Escuela Politécnica Nacional
Get PDFThis project analyzes the depression rates among students from Escuela Politécnica Nacional (EPN). A total of 302 students from different EPN careers, voluntarily and anonymously completed an online survey of the Beck Depression Inventory-II (BDI-II). In addition, they were asked to answer 19 questions related to the lifestyle of an EPN student; These questions were reviewed and endorsed about their possible relationship with depressive disorders by a professional in the field of psychology. The CRISP-DM methodology was used for the project phases, which involved the analysis of the current situation, objectives setting, data collection, data preparation, and construction of ML models that allows predicting the degree of depression based on the BDI-II metrics and evaluation of the models. The model obtained has 0.59 accuracy score and shows that variables of gender, age and relationships are significant to determine severity depression.El presente proyecto analiza las variables de depresión que puede tener un estudiante universitario de la Escuela Politécnica Nacional (EPN) mediante modelos de aprendizaje automático (ML). Participaron un total de 302 estudiantes de distintas carreras quienes completaron de manera voluntaria y anónima una encuesta en línea constituida por el Inventario de Depresión de Beck II (BDI-II). Las 19 preguntas de la encuesta están relacionadas al estilo de vida promedio de un estudiante de la EPN y fueron revisadas y avaladas sobre su relación con trastornos depresivos por una profesional en el campo de la psicología. Se utilizó la metodología CRISP-DM para las fases del proyecto que consistieron en el análisis de la situación actual, planteamiento de objetivos, recolección, análisis y preparación de datos, construcción de modelos de ML para predecir la severidad de depresión con base en las métricas de BDI-II y evaluación de modelos. Se obtuvo un modelo con 0.59 de exactitud y se verificó que las variables de género, edad y relaciones interpersonales son las más significativas al determinar la severidad de depresión