Navegando por Assunto "Python (Linguagem de programação de computador)"

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Análise de dados em radioastronomia como ferramenta de ensino-aprendizagem de física e matemática
(2022-06-18) Falcão, Sérgio Francisco; Silva, Antenor Jorge Parnaiba da; http://lattes.cnpq.br/0144039007577270; http://lattes.cnpq.br/5177728129241307
El presente trabajo refleje la inquietud de este y de otros autores cuanto a introducción importante de los conceptos de la astronomía radial en la educación básica. Seguro de su grand potencial educativo y del poco uso de tal concepto, es que surge la idea de um producto educativo que tiene como influencia las ideas construidas durante el curso de Especialización en la enseñanza de la Astronomía y Ciencias Afines de la Universidad Federal Rural de Pernambuco (UFRPE), los aspectos construccionistas de Seymour Papert (1994 y 1986), los enfoques modernos de la Teoría del Aprendizaje Significativo propuesta por David Ausubel (1918-2008), bien como la análisis de datos de radiotelescopios hcha con el lenguaje de programación Python. Según Papert (1986), El uso del ordenador mejora el proceso de aprendizaje del individuo y es en esta perspectiva que pretendemos proponer una mediación para facilitar la enseñanza de Física y Matemáticas en las escuelas primarias. El proyecto se basa en la idea de que el lenguaje de programación Python es extremadamente importante para la Radioastronomía, ya que son con sus algoritmos que la mayoría de los científicos leen e interpretan los datos recopilados por los radiotelescopios. Así, con el objetivo de utilizar las imágenes y gráficos generados por los datos, que nos ayuden a comprender diversos aspectos de los objetos espaciales, este trabajo propone el uso pedagógico de esos elementos para ayudar a los estudiantes a comprender conceptos físicos, como ondas electromagnéticas, relatividad; conceptos matemáticos como estadísticas, matrices, análisis de gráficos; y conceptos y estructuras del campo de las tecnologías de la información, como análisis de datos y lógica de programación, buscando practicar una enseñanza amplia e integrada al mundo profesional.
Automatização dos serviços de coleta domiciliar com o QGIS e Python
(2024-10-04) Prado, Artillis Henrique Mendes do; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/7294722691197017
This work describes the development of an innovative tool for monitoring household waste collection services in the city of Recife, Brazil, using QGIS and Python. The main objective is to optimize the management of the Operational Control Center (CCO) of TPF, which operates within Emlurb, allowing for better monitoring of collection operations and analysis of vehicle routes. The developed platform collects and processes geospatial data, providing accurate and timely information about the trucks’ paths and generating reports that assist in service management. By integrating these technologies, the tool offers features such as identifying unserviced areas, analyzing response times by sector, and evaluating vehicle productivity. The generated reports provide a comprehensive view of operational efficiency, facilitating strategic decision-making, including route adjustments and the reallocation of more productive vehicles. Additionally, the tool allows filtering data by speed and distance tolerance, helping to verify whether the trucks adhered to their planned routes. Implementing this solution yields significant benefits for public management, including enhanced operational control, data accuracy, and transparency in waste collection activities. By generating real-time information and rapid results, the tool contributes to the efficiency of operations and the continuous improvement of services, fostering a more effective and data-driven administration. Thus, the developed system helps streamline the waste collection process, making it more agile and sustainable.
Comparação da construção de redes neurais nas linguagens R e Python
(2021-03-12) Liberal, João Paulo Godê; Paiva Júnior, Sérgio de Sá Leitão; http://lattes.cnpq.br/7706717198580424; http://lattes.cnpq.br/1024308436712090
This report aims to make a comparison between Artificial Neural Networks (RNAs) written in R and Python languages on Windows 10 and Linux-Ubuntu 20.04 operating systems.The main metrics observed were: execution time in both languages at the different operating systems, considering the time for training and testing the network; the level of accuracy in the different operating systems will also be assessed.The tool used for the development was pycharm, together with the use of libraries keras, tensorflow and pandas. For the Python language, an environment was set up in miniconda 2 and for the R language the execution was by command line.The database used by the Neural Networks was taken from the Machine Learning Repository (UCI) and deals with the diagnosis of cancerous tumor.
Da propagação do calor à construção de desenhos: uma aplicação das séries de Fourier com Python
(2024) Domingos, Cleianderson Paz; Freitas, Lorena Brizza Soares; http://lattes.cnpq.br/2302580820419163; http://lattes.cnpq.br/8909785797719318
This work aims to present an application of Fourier series in generating figures. To do so, we first study the problem of heat conduction in a finite rod, as well as the equation that models it and its solution, both proposed by Joseph Fourier in the early 19th century. Initially, a historical note is presented, exhibiting some facts that lead to the motivation for studying heat propagation. Then, we derive the Heat Equation from two physical laws and study how Fourier series emerge in an attempt to solve this equation. Subsequently, through convergence theorems, we study necessary conditions for a function to be represented by its Fourier series. Finally, we explore an application of Fourier series in figure generation using epicycles and develop a Python algorithm to visualize this application.
Desenvolvimento de programa para verificação de paquímetros do Laboratório de Metrologia da UACSA
(2023-04-28) Andrade, Luca Lima da Silva Pires de; Silva, Karla Carolina Alves da; Silva, Rogério Soares da; http://lattes.cnpq.br/5570338185824272; http://lattes.cnpq.br/6261877082189807; http://lattes.cnpq.br/9828015547946019
The reliability of measurements in measurement instruments is crucial for or-ganizational per- for-mance. Although this topic is addressed in several national and international mentions, it lacks gui-dance for performing instrument checks asser-tively. This study aims to contribute in a significant way to increase the quality of the measurements performed in calipers, developing a program, in Python language, which performs the necessary calculations for verification and at the end generates a verification report. Ensuring an internal quality control of these measuring instruments belonging to the metrology laboratory of the Academic Unit of UFRPE-UACSA. A case study was developed in which the calculations, corresponding to the sources of uncer- tainty, performed manually were compared with those obtained by the software. In addition, an error curve was generated, allowing a visual analysis of the instrument's behavior in operation between verifications and determining a temporal correction factor. The application of the pro- gram proved to be effective, facilitating the construction of verification reports, with a drastic reduction in time, compared to that spent to prepare a manual report. It also allowed the valida- tion of its operation. This work may contribute to applications in academic and professional environments, allowing a simple way to verify and mea-sure the measurement uncertainty of calipers.
Desenvolvimento de uma ferramenta computacional para a análise de membranas em concreto armado
(2023-09-22) Medeiros, Khrys Kathyllen da Silva de; Silva, Jordlly Reydson de Barros; http://lattes.cnpq.br/4382584044561547; http://lattes.cnpq.br/5605761011467421
In this work, the behavior of reinforced concrete membranes is studied using the Modified Compression Field Theory (MCFT) and Rotating-Angle Softened Truss Model (RA-STM), both based on the solution of non-linear equation systems formulated based on equilibrium equations, strains compatibility, and material constitutive models. Initially, the models were implemented in Python programming language, and the Mohr Compatibility Truss Model (MCTM) was used as the initial estimate for the solution. Subsequently, the computational code was tested through some examples, and the results obtained were compared with numerical solutions from the literature and experimental tests, thus verifying the effectiveness of the method. With this, it was concluded that both analysis models presented good results, but the MCFT technique was closer to reality since it considers the concrete tension stiffening, unlike RA-STM. The developed implementation is a simple, effective, and computationally low-cost approach for assisting in the analysis of reinforced concrete panels subjected to membrane forces. Additionally, a graphical user interface was developed, making it accessible to professionals and students in the field of Civil Engineering, regardless of their familiarity with the programming language used. Therefore, the tool becomes an affordable alternative in terms of design, as it easily allows testing various combinations of material properties to determine the most viable option without compromising productivity.
Processo de ETL voltado para jurimetria
(2021-12-20) Cristovam, Edson Fagner da Silva; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/1519472768897299
Large companies may have difficulty managing numerous court cases they are defendants in, as a team of the size needed to manage these cases can be very costly. With the digitization of judicial processes, a strategy that automates the adjustment of process information and is easy to consult can be very useful. Therefore, the data warehouse seems to be a very suitable technique for this, as it normalizes and standardizes information from different sources that are contained in it. But to load the data into a data warehouse, it’s necessary to prepare it with a method commonly known as ETL. With this method we can create data extraction, transformation and load flows that will be written in the data warehouse. With a data warehouse filled with legal data, we can use it to help decision-making in the legal sector of companies through jurimetry, which consists of applying statistical tools to Law, providing new points of view based on data.
Técnicas de Modelagem Matemática e os Métodos de Runge-Kutta
(2021-07-23) Silva, Angelo Antunes da Rocha; Didier, Maria Ângela Caldas; Gondim, João Antônio Miranda; http://lattes.cnpq.br/2674397127545655; http://lattes.cnpq.br/9721552594807972; http://lattes.cnpq.br/9069459979748516
This work consists in the study of Mathematical Modeling with numerical analysis of the models. In it we present the steps of a modeling process, define and evaluate a mathematical model and also discuss the technique of modeling by fitting curves through the Minimal Squares Method, as well as by differential equations where we approach some models, among them those which describe a populational growth dynamic and epidemiological models. We also present the methods from Taylor Series and Runge-Kutta for the construction of numeric solutions for a initial value problem. As the main contribution we simulated analytical and numerical solutions for four problems of initial value, analysing the error linked to numerical solutions, aiming to answer questions related to the general formula of the Runge-Kutta method of order 2. To calculate error for a certain range we used L2 norm and a closed formula from Newton-Cotes. The purpose here is to offer material in the subject of Mathematical Modeling which can be used by Mathematics Graduation students as another area that utilizes Differential Calculus as a tool. The simulations were coded using Python and the code may be accessed through the link in the beggining of this essay.
Uso da ciência de dados para estudo de falhas e fraudes dos abastecimentos de postos de gasolina
(2019-12-19) Arruda, Luiz Felipe Ribeiro de; Albuquerque Júnior, Gabriel Alves de; Roullier, Ana; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/1825682578554550
Uso de Machine Learn para classificação de lançamentos financeiro: estudo comparativo entre modelo AutoML e Redes MLP
(2022-10-10) Silva, Vinicius Mateus Mendonça da; Monteiro, Cleviton Vinicius Fonsêca; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/6180002649065928
The study of this work aims to help companies in their financial management by generating models based on Machine Learning to classify financial releases. With the help of libraries developed in the Python language, it was possible to train AutoML models and Multilayer Perceptron Neural Networks responsible for data classification. With results above 85% in the metrics of Accuracy, Recall, F-measure and Precision for both models, using them brings the possibility of better management of financial releases with less effort.