Mestrado em Engenharia Elétrica e da Computação (EMC)

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    Algoritmos bioinspirados aplicados ao problema de alocação de geração distribuída
    (Universidade Federal de Goiás, 2023-02-02) Santos, Josephy Dias; Garcés Negrete, Lina Paola;; Garcés Negrete, Lina Paola; Belati, Edmarcio Antonio; López Lezama, Jesus Maria; Brito, Leonardo da Cunha
    This work presents the performance comparison of different meta-heuristics, two classic and one modern. The implemented optimization algorithms aim to solve the distributed generation allocation problem in electricity distribution networks widely known in the literature. The study confronts the following computational techniques applied in algorithms classified as bioinspired: the Chu-Beasley Genetic Algorithm (AGCB), the Symbiotic Organisms Search (SOS) and the Coronavirus Optimization Algorithm (CVOA). The allocation of DG units in the Electric Power System gives the system advantages and disadvantages. Among the advantages we can mention: reduction of power losses, expansion of investments in the electrical sector, expansion and diversification of the electrical matrix, mostly, use of clean energy and indirect benefits such as job creation. Among the disadvantages are difficulties in charging for the use of the electrical system, possible incidence of undue taxes, need to change operating procedures, indiscriminate elevation of the voltage profile if the penetration factor is high and the allocation of DG is random, increase in short circuit levels, failures in the protection operation, among others. The network operating conditions are verified through the forward and reverse sweep method, specifically using the Power Sum Method. The objective function, in the optimization model for the allocation of distributed generation, aims to minimize the total losses of active power in the system. For the implementations, the allocation of modules (100, 200 and 500kW) of distributed generation is considered, with the number of these modules limited by the penetration factor of each network. The specialized algorithms are tested on four electrical systems: 10, 34, 70 and 126 buses. The results obtained show the rapid convergence and robustness of the AGCB of the implemented algorithms, the same cannot be said about SOS, which had an intermediate performance. The CVOA, as an unprecedented contribution in this work, presented a lower performance than expected, largely due to its nature and architecture of the proposed modeling.
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    Algoritmos de inteligência computacional aplicados à otimização de sistemas de controle em acionamentos elétricos
    (Universidade Federal de Goiás, 2023-03-29) Santos, Guilherme Fernandes dos; Silva, Wander Gonçalves da;; Cruz Junior, Gelson da;; Cruz Junior, Gelson da; Silva, Wander Gonçalves da; Pickert, Volker; Oliveira, Marco Antonio Assfalk de; Cardoso, Alisson Assis
    This work presents the use of different computational intelligence methods applied to the tuning of a set of PI controllers for a DC motor drive with speed and position control. For position control, three closed control loops are used: armature current, speed and position. For speed control, only the armature current and speed loops are considered. In both cases, the outputs of the PI armature current and speed regulators are limited to the rated armature voltage and current, respectively. Then, it is possible to use higher gains for the controllers, what makes the system to respond faster. However, the windup phenomenon can arise. To avoid it, anti-windup circuits are also used and therefore, the system becomes non-linear. Because of this, an optimum tuning of the controllers may become a difficult task. In order to explore different possibilities, firstly, the speed and position control problems are formulated so that only one objective is minimised. Within this single-objective optimisation context, the PSO and SA algorithms are used to tune the controller parameters, them, the capability of each one is investigated when compared to each other. Multiobjective formulations are also explored to address three objectives simultaneously. In this part of the work, the multi-objective evolutionary algorithms NSGA-II and SPEA2 are used. All algorithms were implemented in MATLAB and the electric drive models were developed in the SIMULINK environment. Simulation results are presented showing that for the single-objective formulation, for both, the for speed and position control problems, the PSO algorithm outperformed the SA. For the multi-objective formulation, the SPEA2 algorithm presented better characteristics with respect to the Spread quality indicator in the only, when compared to NSGA-II. Furthermore, it’s shown to outperform the NSGA-II with respect to the Hypervolume indicator within the position control problem. A series of tests were carried out by varying the values of the main parameters setting for each algorithm. However, in most cases, no statistically significant advantage was observed. In general, the results presented demonstrate the ability of the algorithms to find optimal tuning for the controllers, either for the single-objective or the multiple and conflicting objectives problem.
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    Desempenho de métodos clássicos e meta-heurísticas aplicadas à sintonia de controladores PID sob o aspecto do fator de incontrolabilidade
    (Universidade Federal de Goiás, 2023-03-30) Andrade Filho, Alfredo de Paulo; Cardoso, Alisson Assis;; Cruz Júnior, Gelson da;; Cruz Júnior, Gelson da; Avelar, Henrique José; Teixeira, Edilberto Pereira
    The PID (Proportional Integral Derivative) is one of the most used in industry, and has been employed around the world for decades in industrial control systems. Its popularity can be attributed in part, to its robust performance, in a wide range of operating conditions, and their functional simplicity, which allows engineers to operate them in a simple and direct way. However, tuning such controllers effectively, represents a challenge to control engineers, since it directly affects the efficiency of the system. In the context of modern industry, the setback is even greater, due to the complexity of the processes involved, in which the presence of the Dead Time and the Time Constant of the system, have a great impact on its response. Tuning of PID controllers aims to satisfy some specifications imposed on the transient and steadystate characteristics of the response of the system being controlled. Generally, these specifications fall on the values of Rise Time (Tr), Settlement Time (Ts), Maximum Value overshoot (Mp) and a maximum acceptable Stationary Error (Ess) value. Within the scope of control problems that offer great complexity, either because of the impossibility of previously knowing the transfer function of the system, or due to its multivariable characteristics, several researches have been carried out within the scope of optimizing the tuning of PID controllers using meta-heuristics. In many situations, the system PID cannot be accurately modeled, and still malfunctions in the presence of noise or external disturbances due to the adjustment problem of its parameters that are challenging in practice, as they depend on characteristics of the environment. In this work, we sought to evaluate the performance of the PID controller under the aspect of Uncontrollability Factor when tuned by Genetic Algorithm heuristics (GA) and Particle Swarm (PSO), which have been shown to be more effective in compared to classical tuning methods. Six case studies were evaluated, with varied Uncontrollability Factors.
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    Influência da taxa de subida da corrente na regularidade do processo MIG/MAG por curto-circuito
    (Universidade Federal de Goiás, 2023-02-28) Niz, Weslei Rodrigues; Ferreira Filho, Demostenes;; Souza, Daniel;; Souza, Daniel; Ferreira Filho, Demostenes; Figueiredo, Kléber Mendes de; Rossi, Marcelo Lemos
    The present work investigates the effects of the current rise rate (power source inductance adjustment) on the regularity of the MIG/MAG welding process operating in short-circuit mode. For this, initially the adjustment of the inductive effect of the power source was varied, keeping the regulation voltage and the supply speed constant. Using a regularity index calculated from the current voltage signals, the best fit of the inductive effect was determined. Then, the value of the inductive effect (average rate of current rise) was fixed and the welding voltage was varied with the feed speed kept constant. All tests were performed with 3 shielding gases with different percentages of CO2 (100% Ar, Ar+25%CO2, 100% CO2). As a result of the data analysis, it was possible to find the range of values for the rate of rise of welding current and voltage where the process operates with greater regularity. Preliminary results show that shielding gas influences the correlation between welding voltage, current rise rate and process regularity. After finding the range of values where the process is more regular, experiments were carried out to try to relate regularity with the generation of spatter, the generation of spatter had a tendency contrary to that of regularity, the best performance was with 100% CO2, followed by 100% Ar and the worst mass yield were Ar + 25% CO2.
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    Caracterização do modelo ZIP na análise de sistemas de distribuição de energia elétrica
    (Universidade Federal de Goiás, 2022-10-27) Silva, Arthur Oliveira; Garcés Negrete, Lina Paola;; Garcés Negrete, Lina Paola; Sousa, Thales; Oliveira, Marcelo Escobar de; Brigatto, Gelson Antônio Andrêa
    This paper presents a proposal for optimizing the ZIP model coefficients for modeling the loads present in electrical power distribution systems. Initially, the basic power flow theory is presented and, through an example using the theoretical IEEE 118-bus network, it is shown that the technical losses and voltage profiles obtained from the power flow solution are impacted when three ZIP model scenarios are considered. From the results of the example, it can be seen that this approach should not be neglected in system planning and operation studies, since the non-consideration of the ZIP model can bring impacts in the network diagnosis and, consequently, lead to the direction of imprudent investments under the view of the minimum global cost, as recommended by the National Electric Energy Agency (ANEEL). Thus, this paper proposes a mathematical optimization model that allows obtaining the characteristic coefficients of the ZIP model of the loads, which has as an objective the minimization of the difference between the measured actual current and the calculated one through the solution of the load flow problem. This model will be solved using a genetic algorithm, where each consumer class has its own ZIP model coefficients, separated in active and reactive power portions. Two case studies in real power distribution networks are performed to demonstrate the impact of the optimal ZIP model coefficients on the technical losses (MWh) of the feeders. Finally, the results of the work allow utilities to perform network diagnostics in a more assertive way, since each network can have a ZIP model that best fits the behavior of its loads.
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    Estudo empírico de avaliação inteligente computadorizada de questões abertas baseado em colaboração
    (Universidade Federal de Goiás, 2009-09-12) Corrêa, Kleber Pullig; Machado, Paulo César Miranda;; Machado, Paulo César Miranda; Nalini, Lauro Eugênio Guimarães; Martins, Weber; Lemos, Rodrigo Pinto; Fleury, Claudio Afonso
    This research submits to experimental tests in real-world environment, the Conexionist Collaborative Intelligent Evaluation System idealized by Martins (2004) and tested with simulations by Guimarães (2004), who have noticed consistent and promising results when dealing with synthetic datas. The system explores the Learning Theory by Kolb (1984), Peer Collaboration Evaluation and Artificial Intelligence (multilayer perceptrons neural networks), which are capable to apply and correct tests with open (free) questions. To get a real-world data, some students from the course of Engineer registered at Universidade Federal de Goiás (UFG) on the period of june 2008 to july 2009 were taken in heterogeneous groups composed by men and women in age from 16 to 26 years old and at least 18 students per test. The software SAICOweb, developed specifically to this data collection, allows the computerized evaluations of open (free) questions, by using methods and stages described at Guimarães (2004) research. SAICOweb system were experimental validited in a satisfactory way by an unique group composed by 38 students (by using two tests with 08 open questions each). The analisys of the results with Pearson correlation shows strong association (0,81) between grades given by SAICOweb and the human grades (corrected by professors). Another important result is the satisfaction of professor and students when evaluating Software SAICOweb, evaluation method, pedagogical experience and anonymity. Specific itens were identified and suggested as future updates, when more experimental tests should be designed to validade other aspects of the System.
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    Otimização do desempenho de enrolamentos de máquinas elétricas através de algoritmo de enxames de partículas multiobjetivo
    (Universidade Federal de Goiás, 2022-10-28) Martins, François de Souza; Alvarenga, Bernardo Pinheiro de;; Alvarenga, Bernardo Pinheiro de; Camargo, Ivan Marques de Toledo; Silva, Wander Gonçalves da
    The present work demonstrates the optimization of the operation of electric machine windings. The parameters under study are the magnetomotive force and the end winding leakage inductance, obtained from the discrete distribution of conductors in the airgap. A multi-objective particle swarm metaheuristic optimization routine was proposed. The developed application is capable of generating the airgap conductor distribution for different machine configurations (single or poly-phase, single or double-layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.), as well as the magnetomotive force curves and the end winding leakage inductance. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, the optimization by multi-objective particle swarm algorithm was used to obtain the optimal electrical machine parameter configuration.
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    Dispositivo de análise e monitoramento on-Line de transformadores de potência Imersos em óleo isolante: corrente elétrica, temperatura e umidade
    (Universidade Federal de Goiás, 2020-07-29) Leite, Railson Rodrigues; Paula, Geyverson Teixeira de;; Paula, Geyverson Teixeira de; Brito, Leonardo da Cunha; Ribeiro, Cacilda De Jesus; Marques, André Pereira
    This work deals with the development of a device capable of monitoring real-time power transformers immersed in isolated mineral oil, proving a historical monitoring data analysis solution and a decision-making tool integrated in a cloud supervisory. This is a demand from the power sector, which needs accurate and low cost solutions due to the large number of power system transformers and the strategic importance of this asset. The monitored variables are: temperature and relative humidity of the climate, temperature and water activity (humidity), and currents on the primary and secondary side. Every configuration of device use a application for mobile devices(smartphones, tablets e etc), eliminating the use of displays and buttons. It is tolerant to power and communication failures, presents different ways of connecting to the Internet. The results presented in this work were satisfactory with validation in the laboratory, providing two applications of monitoring of the powertransformer insulation system, regarding: a) the state of the equipment in operation; b) in the drying process of the active part in the maintenance procedures. This work led to the registration of two software and a patent deposit.
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    Alocação de recursos em redes sem fio multiportadoras com ondas milimétricas utilizando aprendizado por reforço baseado em modelo Markoviano
    (Universidade Federal de Goiás, 2022-07-08) Carneiro, Daniel Porto Queiroz; Cardoso, Alisson Assis;; Vieira, Flávio Henrique Teles;; Vieira, Flávio Henrique Teles; Soares, Anderson da Silva; Cardoso, Alisson Assis; Lemos, Rodrigo Pinto
    In this dissertation, we present reinforcement learning-based resource allocation algorithms for a multicarrier communication system considering multiple users and the effects of multipath and average path loss in a transmission assuming millimeter waves. To this end, it is proposed that the communication system can be described by a Markovian model represented by queue states in buffers and channel states. For the resource allocation algorithms of this work, we introduce reward functions to be used in the reinforcement learning algorithm Q-learning. The results obtained in the simulations show that the application of the proposed algorithms for resource scheduling provides, in general, an improvement in the performance parameters of the considered communication system, such as, for example, increased throughput and decreased packet loss. Comparisons with other algorithms presented in the literature are carried out, also showing that the use of the proposed reward function and considered Markovian model makes the scheduling of users and the sharing of resources more efficient. Furthermore, a solution for resource and power allocation using a Deep Q-Network is presented. The modeling of states proposed for the DQN network covers some limitations encountered with the matrix representation of states and extends the limits for the size of the buffer.
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    Aspectos luminotécnicos e da qualidade da energia de lâmpadas com tecnologia LED, fluorescente e vapor de mercúrio
    (Universidade Federal de Goiás, 2021-10-22) Rosa, Lucas Loures; Santos, Euler Bueno dos;; Alvarenga, Bernardo Pinheiro de;; Alvarenga, Bernardo Pinheiro de; Santos, Euler Bueno dos; Belchior, Fernando Nunes
    The advancement of technology and power electronics has allowed a strong advance in large-scale production of electronic devices, which are incorporated into existing electrical systems, in most cases, so there is a change scenario in the type of interconnected load to the electricity distribution system. However, these devices can cause changes in the behavior of system components, caused by harmonics that can result in various damages to electrical equipment. These devices are attractive to consumers, as they offer the opportunity to consume less electricity, as in the case of lamps with active power less than conventional by more than 50%. The electrical characteristics of these devices must be better known so that the feeder circuits can be correctly dimensioned, as well as strategies to solve possible problems arising from unwanted effects. In this context, this work constitutes an analysis, performing confrontations between data obtained from traditional and more modern (“efficient”) devices, in a lighting system in the industrial sector. Among traditional devices, this research addresses fluorescent and mercury vapor lamps and, in the case of modern lamps, LED. In this sense, case studies are presented, with different types of technologies, which allow obtaining indicators of the power quality, with satisfactory precision and reliability. It is important to point out that the data referring to the experimental part were collected in an industrial environment, with the purpose of providing a qualitative and quantitative analysis, with the best possible fidelity.
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    Alocação e dimensionamento ótimo de geração distribuída utilizando o fluxo de potência intervalar
    (Universidade Federal de Goiás, 2021-11-30) Nogueira, Wallisson Calixto; Garcés Negrete, Lina Paola;; Garcés Negrete, Lina Paola; Brigatto, Gelson Antônio Andrea; Belati, Edmarcio Antonio
    Modern Power Systems must deal with high levels of uncertainty in their planning and operation, these uncertainties are mainly due to variations in loads and distributed generation introduced by new technologies. This scenario brings new challenges for system planners and operators who need new tools to carry out more assertive analysis of the state of the network. This work presents an optimization methodology capable of considering uncertainties in the problem of sizing and sitting distributed generation in the networks. The proposed methodology uses the interval power flow (ILF) in order to add uncertainties to the combinatorial optimization problem that is solved through the meta-heuristics Symbiotic Organism Search (SOS) and Particle Swarm Optimization (PSO) for performance comparison purposes. The addition of uncertainties by ILF is validated by the probabilistic power flow (PLF) solved by Monte Carlo Simulation (MCS). This methodology was implemented in Python®, and was applied in the IEEE 33-bus, IEEE 34-bus and IEEE 69-bus test networks where distributed generation sizing and sitting problems were solved in order to minimize technical losses and to improve the voltage levels of the network. For the addition of uncertainties, the results obtained from the proposed ILF in the tested networks are compatible with those obtained by the PLF, thus showing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS meta-heuristic proved to be robust, since it was able to find the best solutions that present the lowest losses, keeping the voltage levels regulated to the predetermined levels. On the other hand, the PSO meta-heuristic presents less satisfactory results, because for all the systems tested, the solution has a lower quality than that found by SOS, thus showing that the PSO algorithm presents difficulties to escape the minimum locations found during the simulation.
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    Escalonamento de recursos para redes móveis que utilizam o paradigma de fatiamento da rede
    (Universidade Federal de Goiás, 2021-11-04) Lopes, Hudson Henrique de Souza; Rocha, Flávio Geraldo Coelho;; Deus Júnior , Getúlio Antero de; Borges , Vinicius da Cunha Martins; Vieira , Flavio Henrique Teles; Rocha, Flávio Geraldo Coelho
    In this dissertation, algorithms are proposed for scaling mobile communication network resources to users with different Quality of Service (QoS) requirements from different network slices. For such a purpose, first we perform mathematical modeling of the probability of packet transmission success per unit of power consumed by the User Equipment (UE). We also use the Gauss-Newton, Levenberg-Marquardt, and Tikhonov regularization methods to accurately estimate the parameters of the sigmoidal utility functions for different Channel Quality Indicators (CQIs). Using the proposed utility functions, we present a algorithm to optimally allocate the Base Station (BS) power, a heuristic based on the foraging behavior of bees. the proposal is compared with other algorithms present in the literature and maintains the same probability of successful packet transmission among UEs while raising the fairness index in the distribution of available resources. The simulations are performed based on a simplified network slicing (NS) scenario for fifth generation mobile networks (5G). Two slices are created for the UEs that use Ultra-reliable and Low Latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB) services. Moreover, taking into account the bandwidth sharing, we also propose a joint power and bandwidth algorithm to satisfy QoS criteria in networks based on the NS paradigm. The results obtained are compared to those obtained by combining the power allocation algorithms and the Round-Robin algorithm for bandwidth allocation, and show that the proposal is especially interesting when throughput maximization is desired.
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    Alocação de recursos em sistemas Internet das Coisas utilizando aprendizagem por reforço
    (Universidade Federal de Goiás, 2021-08-04) Vasconcelos, Matheus Matos; Cardoso, Álisson Assis;; Vieira, Flávio Henrique Teles;; Vieira, Flávio Henrique Teles; Cardoso, Kleber Vieira; Rocha, Flávio Geraldo Coelho; Cardoso, Álisson Assis
    This paper proposes a utilization of a reinforcement learning (RL) algorithm to control the packet transmission of multiple devices of a Cognitive Internet of Things (IoT) wireless communication system. The proposed approach consists of adopting a Markov chain to model the states of the communication system and its transitions, providing the required parameters to determine actions to the system using a Q-Learning algorithm. This paper also presents a performance evaluation of the developed algorithm in comparison to some scheduling algorithms in terms of: utility function, flow rate, buffer occupancy, packet loss rate, etc.
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    Proteção diferencial baseada nas correntes de sequência zero e de neutro para reatores shunt com núcleo de ar tipo seco
    (Universidade Federal de Goiás, 2021-06-30) Santos, Guilherme Gomes dos; Almeida, Maria Leonor Silva de;; Almeida, Maria Leonor Silva de; Garcés Negrete, Lina Paola; Silva, Kleber Melo e
    The growing increase in the number of long transmission lines, with high voltage levels, makes necessary the connection of parallel shunt reactors in the lines, as a way to circumvent the consequences of overvoltage, due to the capacitive effect of the lines. The use of shunt reactors enables voltage regulation, by absorbing the excess of capacitive reactive in the line. Considering the important role of shunt reactors for the correct operation of long transmission lines, it is essential to guarantee protection logics that allow the quick identification of short circuits inside this equipment. Therefore, this work presents a differential protection algorithm based on the analysis of zero and neutral sequence currents, which identifies abnormal operating conditions in the shunt reactor and, consequently, allows a safe and selective operation of the protection for turn-to-ground and turn-to-turn faults. This logic is based on the comparison of the zero sequence current, calculated on the basis of the phase currents, measured near the bushing, with the current measured in the neutral winding. The zero sequence and neutral current phases of the reactor are reconstructed in time and, finally, are compared, allowing the identification of the short circuit within the reactor. In addition, the algorithm incorporates a function that detects the occurrence of disturbances of an internal or external nature to the reactor. Simulations were performed using the software Alternative Transient Program (ATP), in which a 230 kV, 380 km transmission line with 50 % of compensation was modeled, such that, in the reactor, different internal short circuits were applied, of the turn-to-ground and turn-to-turn faults. Furthermore, in order to perform a comparative evaluation, the proposed algorithm was analyzed together with the traditional differential protection Restricted Earth Fault-(REF), which uses the same input signals. From the results obtained, it was found that the traditional protection did not operate for turn-to-ground with high fault resistance, and was also not able to identify any of the analyzed turn-to-turn faults. On the contrary, it was verified that the proposed algorithm operates correctly for all the turn-to-ground and turn-to-turn faults tested, independently of the variations in the value of the dispersion factor, the number of turns involved and the value of the fault resistance. Futhermore, it is worth to mention the fast speed operation of the proposed protection Also noteworthy was the speed in which the proposed algorithm, which identified all the faults analyzed with an approximate operation time order of 1 ms after their occurrence. It should be noted that in order to guarantee the correct performance of the proposed protection for all external short circuits, it is necessary to use additional logic to guarantee non-reaction for external faults.
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    Controle vetorial em máquinas de relutância variável usando transformação dq não senoidal
    (Universidade Federal de Goiás, 2021-07-21) Vilela, Wellington Misael; Paula, Geyverson Teixeira de;; Paula, Geyverson Teixeira de; Oliveira, Eduardo Sylvestre Lopes de; Vieira, Rodrigo Padilha
    This work presents a method of Field Oriented Control and torque ripple minimization for Variable Reluctance Machines. It utilizes a non-sinusoidal generalised Park transform, also known as dqx transform, and the derivatives of inductance of the machine as state variables. As a consequence, the mathematical formulation becomes simpler than previous works in literature, which use the traditional Park transform. To validate the method proposed, phase inductances and its derivatives for a 6/4 machine were obtained from Finite Element Analysis simulations and used in the calculations. Magnetic saturation effects were also considered. The mathematical formulation, numeric and simulation results are shown. Simulation results demonstrate that the method proposed in this work effectively reduced torque ripple by 2.9 times, torque ripple factor was reduced by 4.65 times and Joule Effect losses are decreased by 79.68% in comparison with conventional driving strategies. Flux weakening and flux enhancement simulations were also performed and showed that machine speed increased up to 19.22% with respect to base speed.
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    Avaliação de características e previsão de sucesso de canções populares brasileiras por meio de aprendizado de máquina
    (Universidade Federal de Goiás, 2021-04-13) Bertoni, André Augusto; Lemos, Rodrigo Pinto;; Lemos, Rodrigo Pinto; Vieira, Flávio Henrique Teles; Calixto, Wesley Pacheco; Almeida, Anselmo Guerra de
    This work aims to develop algorithms capable of predicting whether a Brazilian popular song can become a commercial success, using the help of Machine Learning techniques. To achieve this goal, a bank of popular Brazilian songs performed on the radio from 2014 to 2019 was created, applying the number of radio plays as the criterion for separating the songs into a successful group and another of non-successful. Several techniques of Data Analysis were studied and applied to optimize the databases and extract statistical characteristics of the songs. From the study of music theory, a set of musical semantic characteristics extracted from each song was also defined to support the Machine Learning algorithms, and then employ Data Science techniques to predict if a song can become a commercial success. Classification algorithms with supervised training were used, both by the classical approach and by means of Deep Neural Networks. For training and validation, cross-validation was used with ten subsets for the classical approach, and five subsets for convolutional networks. The performance of the algorithms was compared basically in terms of accuracy, precision, sensitivity and specificity. The discussion of the results of this work showed that statistical characteristics extracted from the songs brought satisfactory results in several metrics, such as: Accuracy (69.63%), Precision: (69.03%), Sensitivity (71.55%), Balanced Accuracy (69.75%) and ROC (69.75%), using classic techniques as: Naive Bayes, Decision Tree, Random Forest, kNN, Logistic Regression, SVM and MLP - which represents an excellent result, when compared to several other works of literature. Deep Neural Networks of the Convolutional type did not bring good results, with little better accuracy than randomness. The best scenario was achieved by combining three distinct banks of characteristics: a) statistics; b) spectrographs extracted from the Main Voice Melody; c) Musical Melodic Semantic information. With the combination of these three distinct banks of characteristics, 74.54% Accuracy was obtained.
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    Aplicação de algoritmos de controle e balanceamento de carga a um sistema perinatal
    (Universidade Federal de Goiás, 2021-02-18) Oliveira, Ricardo Bruno Osés de; Rocha, Flávio Geraldo Coelho;; Rocha, Flávio Geraldo Coelho; Lemos, Rodrigo Pinto; Castro, Marcelo Stehling de; Pinto, Leizer de Lima
    In this work, the process of admission and scheduling of pregnant women in a perinatal network composed of two maternities is considered. The main service sectors that make up each perinatal unit and their respective functions and resources used are described. In addition, a survey is made of the main challenges and difficulties faced by these health units in Brazil in recent decades. Thus, given the problems and challenges pointed out, different load balancing algorithms are used in the proposed perinatal network, to find the best task scheduling policy in the system that increases the efficiency of the network. A solution for a mixed integer linear programming problem is proposed using a load balancing algorithm based on metaheuristics of the behavior of honey bees. Besides, some algorithms are analyzed where each considered model has its own task routing strategy designed to reduce the average time of the pregnant women entering the perinatal system, balancing the workload among the perinatal care centers. Two classes of routing are used, non-deterministic and deterministic. In the deterministic class, three routing policies are analyzed that seek to decrease the average stay time, the average service time, or to improve the flow in the system. In addition, a dynamic control policy based on a queuing threshold is also analyzed, where a specific queue length is defined and identified by a threshold. The routing policy named Join-the-Shortest-Queue (JSQ) is also analyzed, where each pregnant woman who enters the system is directed to the maternity ward with the shortest queue. The results are presented and analyzed varying both the arrival rates of pregnant women and the rates of care in the main sectors in a maternity hospital. Also, a discrete event simulation model is made to analyze the waiting time in queues. Finally, using the formula of Erlang-B, the capacity of the perinatal units is calculated based on the time of permanence of the pregnant women in the system obtained through the load balancing algorithms. The results obtained confirm that routing and scheduling policies that consider the task arrival rate and the system queue length are more efficient as the arrival rate increases, therefore being applicable in healthcare systems with increasing demand and that with planning it is possible to obtain an accurate description of the number of occupied beds and the number of beds needed according to the demand required by the perinatal units making these units more efficient.
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    Análise da classificação de sinais eletro-oculográficos utilizando aprendizado de máquina
    (Universidade Federal de Goiás, 2020-01-19) Melo Júnior, Gilberto de; Vieira, Sílvio Leão;; Vieira, Sílvio Leão; Alcala, Symone Gomes Soares; Calixta, Wesley Pacheco
    This work aims at the comprehensive study involving electro-oculographic signals, acquisition methodologies, digital filters and Machine Learning algorithms. The research methodology was divided into three major stages. The first stage aimed at developing an environment and methods for acquiring electro-oculographic signals. In the second stage, digital filters were applied to the acquired signals. In the third and last stage, the signal patterns were analyzed using Machine Learning algorithms responsible for the classification of electro-oculographic signals. As a result, accuracy in the classification of 76.596 \% was obtained with the Random Forest algorithm.
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    Alocação de taxa de transmissão utilizando predição do tráfego de rede baseada no expoente de Lyapunov e no parâmetro de Hurst
    (Universidade Federal de Goiás, 2021-02-25) Rosa, Évelynn Regina da Costa; Rocha, Flávio Geraldo Coelho;; Rocha, Flávio Geraldo Coelho; Vieira, Flávio Henrique Telles; Souza, Marcos Antonio de; Cardoso, Alisson Assis
    This project describes a standard approach to analyze and measure experimental and synthetic data, which display linear and non-linear characteristics, within the spectrum of Chaos Theory and its canonical procedures, evaluating the performance of several algorithms. The main goal of this work is to propose a method to predict traffic and perform a dynamic traffic rate allocation for the network servers based on the Lyapunov exponent and the Hurst parameter, considering the analysis of a range of traffic samples and synthetic data, in order to quantify them with the use of mathematical methods that reveal their intrinsic features. Some of the characteristic processes described are self-similarity, long-range dependency among samples and multiscale behavior. Thus, it is necessary to: reconstruct phase space and the attractor with the ideal delay $\tau$, while describing a couple of methods to compute it; to determine embedding and correlation dimensions ($m$ and $D_2$, respectively); to calculate the Lyapunov exponent $\lambda$ and Hurst parameter $H$; to perform principal component analysis ($PCA$); to predict the traffic within the longest predictable duration constrained by the inverse of the Lyapunov exponent, and finally perform dynamic transmission rate allocation for the network servers. The simulations performed confirmed the efficiency of the proposed approach, regarding the sequence adopted in this work and the classification of data, especially in cases of mixed behavior between randomness and determinism.
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    Redução do cogging torque por meio da otimização da segmentação dos polos na máquina síncrona de ímãs permanentes no rotor
    (Universidade Federal de Goiás, 2020-12-14) Santos, Hugo Emerenciano; Paula, Geyverson Teixeira de;; Paula, Geyverson Teixeira de; Alvarenga, Bernardo Pinheiro de; Silva, Wander Gonçalves da
    This work presents an application of an optimization technique in the pole segmentation of permanent magnet synchronous machines with the purpuse of minimizing the cogging torque by acquiring optimal design parameters of the machine. As will be presented pole segmentation has a negative impact regarding the reduction of airgap flux density, which decreases the average value of the developed torque. The behavior of the airgap flux density is then modeled considering the distribution of magnet segments and the angular position of the rotor. After obtaining the flux distribution function model, the analytic expression of the cogging torque derived. Due to a lack of studies regarding pole segmentation applied to rectangular shaped magnets, the behavior of airgap flux density is deeply investigated and the results are presented in this work. Three different optimization scenarios are proposed.The first one considers all pole segments (arc shaped) having equal space between them as well as having same dimensions. The second one considers the possibility of different dimensions and different distances between them. Finally, to analyse the efficacy of the usage of rectangular segments, they are submitted to an optimization process and the results are compared to those with arc shaped. The proposed mathematical models, as well as the optimization proposals, are validated by means of finite elements analysis, where the results are compared to the reference (non-segmented rotor). It is observed a several reduction in the cogging torque peak values in all methods, up to 84%. The application of an optimization process has ensured the preservation of the airgap flux density in all cases, with the best case maintaining 96% of the reference flux density. It is worth mentioning that the usage of rectangular magnets achieved satisfactory results proving to be a suitable option in segmentation, with reduction of cogging torque up to 79%, and preserving 91% of the reference flux density.