Dissertações em Engenharia Elétrica (Mestrado) - PPGEE/ITEC

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/2316

O Mestrado Acadêmico inicou-se em 1986 e pertence ao Programa de Pós-Graduação em Engenharia Elétrica (PPGEE) do Instituto de Tecnologia (ITEC) da Universidade Federal do Pará (UFPA).

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Agora exibindo 1 - 8 de 8

Acesso aberto (Open Access)
Desenvolvimento de softwares e algoritmo baseado em redes neurais artificiais para suporte à gestão da mobilidade urbana em smart campus com característica multimodal
(Universidade Federal do Pará, 2022-07-20) SÁ, Joiner dos Santos; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
This work presents the development of two software solutions and an algorithm based on artificial neural networks to support the management of urban mobility in a smart campus. The first software, called Norte Rotas, is a web solution whose objective is to support the planning of pedestrian routes, providing relevant information about the physical conditions of the routes of a smart campus. The second solution is an Android mobile software that aims to manage transport modes present in a smart campus. Tests in simulated and real environments were carried out and the results indicate that the proposed tools are good solutions for the planning and management of modal routes in an intelligent university campus. In addition to the software, a computational intelligence algorithm is proposed to determine the best travel route considering the options on foot, by bus and by boat in an IoT system of a smart campus. Data were collected from UFPA's Circular bus routes, and tests with different parameters of an ANN were performed. The results show that the solution based on ANN is promising to be implanted in urban mobility aid systems in a smart campus.
Acesso aberto (Open Access)
Intelligent positioning of drones via metaheuristic optimization algorithms for maximizing signal coverage area in forested environments
(Universidade Federal do Pará, 2022-01-31) FERREIRA, Flávio Henry Cunha da Silva; ALCÂNTARA NETO, Miércio Cardoso de; http://lattes.cnpq.br/0549389076806391; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
This dissertation aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base-stations. For this purpose, two types of networks utilizing UAVs have been analyzed: a standard Wi-Fi network operating at 2.4 GHz, and a low-power wireless area network (LPWAN), both considering medium to high-density forest environments. LPWAN are systems designed to work with low data rates but still keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen herein is LoRa, which operates at an unlicensed spectrum of 915 MHz, and requires users to connect to gateways in order to relay information to a central server – in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for every UAV in the array, three concomitant bioinspired optimization methods have been chosen: the cuckoo search (CS), the flower pollination algorithm (FPA) and the bat echolocation algorithm (BA). All of these methods have a search space distribution based on Lévy / Mantegna flights (CS, FPA) and normal distribution (BA), and present distinct performance results for both drone array network cases. Positioning optimization results are then simulated and presented via MATLAB, first for the Wi-Fi network and later for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on the UFPA campus was developed to obtain a propagation model in forested environments. Finally, drone positioning utilizing the propagation model corrected with measurements is compared with the positioning using the classical theoretical model, showing that the corrected model is more efficient in representing the forest environment than the classical model usually used in recent publications.
Acesso aberto (Open Access)
Otimização do posicionamento de múltiplas small cells em ambientes outdoor da região amazônica utilizando enxame de partículas e polinização de flores.
(Universidade Federal do Pará, 2019-01-14) SILVA FILHO, Frederico Guilherme Santana da; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
Acesso aberto (Open Access)
Proposta de um framework para identificação de sistemas dinâmicos multivariáveis não lineares
(Universidade Federal do Pará, 2020-02-27) OLIVEIRA, Ewerton Cristhian Lima de; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
The techniques of dynamic systems identification are algorithms of most importance for generating mathematical and computational models capable to represent the dynamic of systems and processes present in many fields of society, such as: industrial processes; automobiles; food production; aerospace vehicles; biological systems and etc. The identification of these systems, which generally have more than one variable of input and output (multivariable systems) and also are nonlinear, it is very important for science and engineering in relation to the development of new control techniques, fault monitoring and prediction of operating state of these mechanisms. Nonetheless, the identification of nonlinear MIMO (Multiple Input Multiple Output) systems is a hard task, as much due the difficulty of implementing the classic algorithms for solve this problem, as the fact that nonlinear systems require complex models for represent their dynamics in satisfactory way. In order to contribute with the solution of this problem, this work proposes a framework capable of performing as much the identification of nonlinear dynamic MIMO systems in multivariable fuzzy TSK model, which can represent in simple way the coupling among the variables involved in identification, as the selection of regressor vector used in model. To perform fuzzy TSK multivariable model parameterization, the proposed framework uses the algorithms Least Square (LS) and Particle Swarm Optimization (PSO), which are responsible to estimate the matrix of parameters and the set of standard deviation of the Gaussians in model inputs, respectively. The proposed methodology is tested and compared with RNA and a Hammerstein-Wiener (WH) model in identification of two nonlinear MIMO industrial plants: Continuous Stirred Tank Reactor (CSTR); Industrial Dryer. The comparison of these three techniques is made with base in indices of Mean Squared Error (𝑀𝑆𝐸) and Variance Accounted For (𝑉𝐴𝐹), further the analysis of residues between the observed and estimated data. The results show that the proposed framework got the best performance, based in the two indices, in 80% of outputs estimation of the two multivariable plants, and also reached the best performance in 60% of residual analysis of plants identification.
Acesso aberto (Open Access)
Redes neurais aplicadas à modelagem de canais de comunicação utilizando VANTs e dispositivos IoT
(Universidade Federal do Pará, 2023-02-08) CARDOSO, Caio Mateus Machado; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
After the occurrence of the cellular networks fifth generation auction (5G), carried out by the National Telecommunications Agency (ANATEL), carriers started to implement the technology on Brazilian soil and it is expected that a massive amount of smart devices will become capable to connect to 5G networks, promoting the advancement and improvement of internet of things (IoT). However, Narrowband-IoT (NB-IoT) technology, used by 5G for IoT applications, is still not enough to meet all user requirements, with that in mind, LoRa technology emerges as an auxiliary to meet the requirements of users. Given this scenario, this work aims to analyze the behavior of the LoRa signal in a suburban and densely wooded environment. For this, measurement campaigns are carried out at the Federal University of Pará (UFPA) and from the collected data a neural network model capable of reproducing this behavior is proposed. The standard model is compared to baseline models and proves to be superior in the downlink and uplink scenarios with a minimum RMSE error of 1,6623 dB for the first and 1,3891 dB for the second
Acesso aberto (Open Access)
Sistema web de suporte a mobilidade multimodal em smart campus usando algoritmos baseados em inteligência artificial e análise estatística
(Universidade Federal do Pará, 2023-08-16) SILVA, Edinho do Nascimento da; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
Urban mobility in a large city largely depends on public transport, which plays a vital role in facilitating the flow of citizens between different areas where a variety of goods and services, such as health and physical activities, are available. According to the UN, it is expected that more than 60% of the world’s population will live in urban areas by 2050, which increases the need to make the entire transport system more sustainable and energy efficient. For this, the transition from modes powered by fossil fuels to options powered by electricity is a watershed that will require the implementation of new infrastructure to provide electricity to future modes. Such installation of energy points, called points of electric recharge of electric modes, is a technical, economic and energetic challenge. To discuss this current and relevant topic, this paper presents the results obtained from the Amazon Multimodal Intelligent System Project (SIMA), developed in partnership between the company Norte Energia and the Federal University of Pará (UFPA), focusing on the implementation at UFPA’s Smart Campus in Belém. In possession of the implementation of the structure, this branch of the project, made use of the infrastructure project for the development of a software-based solution that serves as support in decision-making on the management of the energy efficiency of an intelligent environment, that is, a Smart Campus. In view of the above, this work has as its main focus the design and development of an energy efficiency management system that converts the data generated by electric modes in order to be useful in the management of the energy efficiency of the entire multimodal system. For this, several algorithms were implemented in the software, namely: linear regression, general regression neural network and moving average. From this work it is possible to conclude that the tool can be useful to help modal managers to achieve reductions in energy consumption, consequently, reducing operating costs and increasing equipment longevity, which will also impact capital costs.
Acesso aberto (Open Access)
Software para planejamento de redes IoT: uma solução baseada em algoritmo genético, algoritmo de múltiplas tentativas e EPSO
(Universidade Federal do Pará, 2022-07-20) GONÇALVES, Leonardo Nunes; ARAÚJO, Jasmine Priscyla Leite de
The Internet of Things (IoT) allows the ubiquitous monitoring of environments through sensors arranged in a certain area of interest. Such data collection generates unprecedented content of information that is presented to different algorithms that serve to assist in decision-making associated with urban mobility, economy, health, well-being, among others. To ensure the success of this communication chain, defined from the collection of data to the extraction of valuable decisions, it is necessary to implement an end-to-end communication. For this, the IoT makes use of Long Range communication technology (LoRa), which in turn guarantees wireless and cost-free communication between the sensors installed in the endnodes arranged in the area of interest and the data traffic aggregation points installed in the area to be monitored, ie the gateway. Although the solution is practical, there are cost minimization challenges associated with deploying the fewest number of gateways in the area to be covered, as well as the task of planning the IoT network taking into account the optimal positioning of the gateways. Given this context and to respond to the challenges imposed by the planning of IoT networks, this work aims to propose an optimizing software for planning IoT networks based on Genetic Algorithm, Evolutionary Particle Swarm Optimization (EPSO) and Multiple Attempts algorithm, in order to to minimize the number of gateways and determine the geolocation for their installation, thus aiming to guarantee the coverage of all endnodes and their respective sensors arranged in the field.
Acesso aberto (Open Access)
Transmissão IoT e sistema fuzzy para detecção dos níveis de interferência em sensores de temperatura afetados pela formação de biofilme
(Universidade Federal do Pará, 2024-02-28) BALBINOT, Tatiane Ferraz; RAMOS, Rommel Thiago Jucá; http://lattes.cnpq.br/1274395392752454; https://orcid.org/0000-0002-8032-1474; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004; https://orcid.org/0000-0003-3514-0401
The development of biofilm on the surface of sensors in prolonged contact with water can result in inconsistent data collection and even equipment wear. Therefore, constant monitoring of the equipment becomes necessary, and for maintaining data integrity, predictive maintenance is essential. In this scenario, the Internet of Things (IoT) and its applications emerge as one of the viable alternatives for real-time data transmission, as it offers distributed communication solutions, lower costs, and easy access for users. This study analyzes temperature sensor data stored in the cloud through statistical calculations. Based on these analyses, the thresholds of the developed Fuzzy system are defined, indicating the need for sensor cleaning according to the noise level generated by the presence of biofilm, to maintain the integrity of the collected data.

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