Teses em Engenharia Elétrica (Doutorado) - PPGEE/ITEC

URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/2317

O Doutorado Acadêmico inicio-se em 1998 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 - 19 de 19

Acesso aberto (Open Access)
Abordagem Inteligente com Combinação de Características Estruturais para Detecção de Novas Famílias de Ransomware
(Universidade Federal do Pará, 2024-03-22) MOREIRA, Caio Carvalho; SALES JUNIOR, Claudomiro de Souza de; País de Nacionalidade Brasi
Ransomware is a malicious software that aims to encrypt user files and demand a ransom to unlock them. It is a cyber threat that can cause significant financial damage, as well as compromise privacy and data integrity. Although signature-based detection scanners commonly combat this threat, they fail to identify unknown ransomware families (variants). One method to detect new threats without the need to execute them is static analysis, which inspects the code and structure of the software, along with classification through intelligent approaches. The Detection of New Ransomware Families (DNFR) can be evaluated in a realistic and challenging scenario by categorizing and isolating families for training and testing. Hence, this thesis aims to develop an effective static analysis model for DNFR, which can be applied in Windows systems as an additional security layer to check executable files upon receipt or before execution. Early ransomware detection is essential to reduce the likelihood of a successful attack. The proposed approach comprehensively analyzes executable binaries, extracting and combining various structural features, and distinguishes them between ransomware or benign software employing a soft voting model comprising three machine learning techniques: Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGB). Results for DNFR demonstrated an average accuracy of 97.53%, precision of 96.36%, recall of 97.52%, and F-measure of 96.41%. Additionally, scanning and predicting individual samples took an average of 0.37 seconds. This performance indicates success in quickly identifying unknown ransomware variants and adapting the model to the constantly evolving landscape, suggesting its applicability in antivirus protection systems, even on resource-limited devices. Therefore, the method offers significant advantages and can assist developers of ransomware detection systems in creating more resilient, reliable, and fast-response solutions.
Acesso aberto (Open Access)
Algoritmo memético cultural para otimização de problemas de variáveis reais
(Universidade Federal do Pará, 2019-03-29) FREITAS, Carlos Alberto Oliveira de; SILVA, Deam James Azevedo da; http://lattes.cnpq.br/8540875293894747; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
Technology has made great strides in recent years, but computing resources for certain applications need optimization so that the costs involved in solving some problems are not high. There is a very broad area of research for the development of efficient algorithms for multimodal optimization problems. In the last two decades the use of evolutionary algorithms in multimodal optimization has been shown to be a success. Among these evolutionary algorithms, which are global search algorithms, one can cite the use of Cultural Algorithms. A natural enhancement of the Cultural Algorithm is its hybridization with some other local search algorithm, so as to have the advantages of global search combined with local search. However, the local search Cultural Algorithms used for multimodal optimization are not always evaluated by efficient statistical tests. The objective of this work is to analyze the behavior of the Cultural Algorithm, with populations evolved by the Genetic Algorithm, when the local search heuristics are used: Tabu Search, Beam Search, Climbing and Simulated Annealing. One of the contributions of this work was the updating of the topographic knowledge of the cultural algorithm by the use of the triangular area defined by the best results found in the local search. To perform the analysis, a memetic algorithm was developed by hybridizing the cultural algorithm with the local search heuristics mentioned, being selected one at a time. Real world problems usually have multimodal characteristics, so the evaluations were performed using multimodal benchmark functions, which had their results evaluated by non-parametric tests. In addition, the memetic algorithm was tested on real optimization problems with constraints in the engineering areas. In the evaluations carried out, the developed Cultural Algorithm presented better results when compared to the available results of the researched scientific literature.
Acesso aberto (Open Access)
Alocação ótima de geração distribuída em redes de distribuição utilizando algoritmo híbrido baseado em cuckoo search e algoritmo genético
(Universidade Federal do Pará, 2018-09-02) OLIVEIRA, Victoria Yukie Matsunaga de; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
This thesis presents a novel Cuckoo Search (CS) algorithm called Cuckoo-GRN (Cuckoo Search with Genetically Replaced Nests), which incorporates the benefits of genetic algorithm (GA) into the CS algorithm. The proposed method handles the abandoned nests from CS more efficiently by genetically replacing them, significantly improving the performance of the algorithm by establishing optimal balance between diversification and intensification. The algorithm is used for the optimal location and size of distributed generation units in a distribution system, in order to minimise active power losses while improving system voltage stability and voltage profile. The allocation of single and multiple distribution generation units is considered. The proposed algorithm is extensively tested in mathematical benchmark functions as well as in the 33-bus and 119-bus distribution systems. Simulation results show that Cuckoo-GRN can lead to a substantial performance improvement over the original CS algorithm and others techniques currently known in literature, regarding not only the convergence but also the solution accuracy.
Acesso aberto (Open Access)
Análise de desempenho de algoritmos para classificação de sequências representando faltas do tipo curto-circuito em linhas de transmissão de energia elétrica
(Universidade Federal do Pará, 2019-12-05) FREIRE, Jean Carlos Arouche; MORAIS, Jefferson Magalhães de; http://lattes.cnpq.br/5219735119295290; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Maintaining power quality in electrical power systems depends on addressing the major disturbances that may arise in their generation, transmission and distribution. Within this context, many studies have been developed aiming to detect and classify short circuit faults in electrical systems through the analysis of the electrical signal behavior. Transmission line fault classification systems can be divided into two types: online and post fault classification systems. In the post-missing scenario the signal sequences to be evaluated for classification have variable length (duration). In sequence classification it is possible to use conventional classifiers such as Artificial Neural Networks, Support Vector Machine, K-nearest neighboors and Random forest. In these cases, the classification process usually requires a sequence preprocessing or a front end stage that converts the raw data into sensitive parameters to feed the classifier, which may increase the computational cost of the classification system. An alternative to this problem is the FBSC-FrameBased-Sequence Classification (FBSC) architecture. The problem with FBSC architecture is that it has many degrees of freedom in designing the model (front end plus classifier) and it should be evaluated using a complete dataset and rigorous methodology to avoid biased conclusions. Considering the importance of using efficient short-circuit fault classification methodologies and mainly with low computational cost, this paper presents the results of the KNN-DTW (K-Nearest Neighbor) algorithm analysis study associated with Dynamic similarity measurement. Time Warping (DTW) and HMM (Hidden Markov Model) algorithm for fault classification task. These two techniques allow the direct use of data without the need for front ends for signal pre-processing, as well as being able to handle multivariate and variable time series, such as signal sequences for the post-miss case. To develop the two proposed systems for classification, simulated data of short-circuit faults from the UFPAFaults public database were used. To compare results with methodologies already presented in the literature for the problem, the FBSC architecture was also evaluated for the same database. In the case of FBSC architecture, different front ends and classifiers were used. The comparative assessment was performed from the measurement of error rate, computational cost and statistical tests. The results showed that the HMM-based classifier was more suitable for the problem of classification of short circuits on transmission lines.
Acesso aberto (Open Access)
Aplicação de fanets e ca-markov para captura de imagens para o estudo de uso e cobertura da terra em projetos de assentamentos na amazônia
(Universidade Federal do Pará, 2019-12-06) SOUZA, Jorge Antonio Moraes de; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
Projetos de assentamentos de reforma agrária são uma das medidas adotadas pelo governo na tentativa de criar um relacionamento sustentável com a natureza. Como a área de assentamentos cobre mais de 77.483.317,86 hectares da Amazônia Legal, é essencial compreender as causas da degradação ambiental desses espaços. Isto posto, foram utilizados, de forma combinada, cadeias de Markov e autômatos celulares (CA-Markov) para, a partir de duas imagens classificadas, prever cenários de mudanças no uso e cobertura da terra (LULC). Esta tese apresenta uma metodologia inovadora que difere daquelas usualmente utilizadas em CA-Markov, pois os aspectos de tempo e espaço são observados pela cadeia de Markov e servem como base para a função de transição do autômato celular (CA). A metodologia também contempla a aquisição de imagens, nesse sentido, como a região de interesse permanece, em boa parte do ano, com uma cobertura de nuvens significativa, a obtenção de imagens por sensores ópticos, fica prejudicada, por conta disso, foi imperativa a busca por uma alternativa. As Flying Ad-hoc Networks (FANETs) podem ser utilizadas para complementar informações da região de estudo, capturando imagens de alta qualidade, sem o inconveniente das nuvens. Por outro lado, os nós da rede precisam manter, pelo maior tempo possível, a conexão entre eles, o que é dificultado pela mobilidade e autonomia de voo dos drones. Por esse motivo, é imprescindível a utilização de um protocolo de roteamento que seja capaz de adaptar-se à dinâmica da rede. Além disso, também foi desenvolvido um algoritmo de roteamento baseado em sistema Fuzzy. Testes e simulações foram realizadas com o intuito de validar tanto a metodologia geral MAPS, quanto o protocolo de roteamento.
Acesso aberto (Open Access)
Aplicação de redes neurais artificiais para predição de RSSI e SNR em ambiente de bosque amazônico
(Universidade Federal do Pará, 2024-06-11) BARBOSA, Brenda Silvana de Souza; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004; https://orcid.org/0000-0003-3514-0401; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609
The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with data transmission between IoT devices, resulting in the need for signal propagation modeling that considers the effect of vegetation on its propagation. In this context, this research was conducted at the Federal University of Pará, using measurements in a wooded environment composed of the Pau-Mulato species, typical of the Amazon. Two propagation models based on machine learning, GRNN and MLPNN, were developed to consider the effect of Amazonian trees on propagation, analyzing different factors such as the height of the transmitter relative to the trunk, the beginning of the foliage, and the middle of the tree canopy, as well as the LoRa spreading factor (SF) 12 and the copolarization of the transmitter and receiver antennas. The best models were the machine learning ones, GRNN and MLPNN, which demonstrated greater accuracy, achieving root mean square error (RMSE) values of 3.86 dB and 3.8614 dB, and standard deviation (SD) of 3.8558 dB and 3.8564 dB, respectively. On the other hand, compared to classical models in the literature, the best-performing model was the Floating Intercept (FI) model, with RMSE and SD errors around 7.74 dB and 7.77 dB, respectively, while the FITU-R model had the highest RMSE and SD errors, around 26.40 dB and 9.65 dB, respectively, for all heights and polarizations. Furthermore, the importance of this study lies in its potential to boost wireless communications in wooded environments, as it was observed that even at short distances at heights of 12 m and 18 m, the SNR (Signal-to-Noise Ratio) had lower values due to the influence of the foliage, but it was still possible to send and receive data. Finally, it was shown that vertical polarization achieved the best results for the Amazon forest environment.
Acesso aberto (Open Access)
Classification and characterization methods of non-tchnical losses on smart grid scenarios
(Universidade Federal do Pará, 2024-03-28) BASTOS, Lucas de Lima; ROSÁRIO, Denis Lima do; http://lattes.cnpq.br/8273198217435163; https://orcid.org/0000-0003-1119-2450; CERQUEIRA, Eduardo Coelho; ttp://lattes.cnpq.br/1028151705135221
Nowadays, grid resilience as a feature has become non-negotiable, significantly when power interruptions can impact the economy and society. Smart Grids (SGs) widespread popularity enables an immense amount of fine-grained e lectricity consumption data to be collected. However, risks can still exist in the Smart Grid (SG), since SG systems exchange valuable data, the distribution system loses substantial electrical energy. We divide this loss into two categories: technical and non-technical loss. A substantial amount of electrical energy is lost throughout the distribution system, and these losses are divided into two types: technical and non-technical. Non-technical losses (NTL) are any electrical energy consumed that is not invoiced. They may occur due to illegal connections, fraudulent activities, issues with energy meters such as delay in the installation or reading errors, contaminated, defective, or non-adapted measuring equipment, very low valid consumption estimates, faulty connections, and disregarded customers. Non-technical losses are the primary cause of revenue loss in the SG. Annually, electrical utilities incur billions in losses due to non-technical reasons. This thesis presents two detection methods of NTL: classification a nd c haracterization. We c reate a n ensemble predictor-based time series classifier t o c lassify N TL d etection. T his p redictor u ses the user’s energy consumption as a data input for classification, f rom s plitting t he d ata to executing the classifier. A lso, i t a ssumes t he t emporal a spects o f e nergy consumption data during the pre-processing, training, testing, and validation stages. The classification method has the advantage of classifying heterogeneous features in data. The characterization method proposes a study based on Information Theory Quantifiers (ITQ) to mitigate this challenge. First, we use a sliding window to convert the user’s energy consumption time series into a Bandt-Pompe (BP) probability distribution function. Then, we extract the used ITQ. Finally, we apply each metric to the Probability Density Function (PDF) and map the layers to characterize their behavior. The characterization method is advantageous to be used when we have big data. Overall, our best results have been recorded in the fraud detection-based time series classifiers (TSC) model, improving the empirical performance metrics by 10% or more over the other developed models. Our results show that users with normal and abnormal energy consumption can be distinguished using only Information Theory Quantifiers by considering the range of values for each metric.
Acesso aberto (Open Access)
Desenvolvimento e avaliacao empirica de um simulador educacional para o apoio ao ensino de ECG, baseado na orientacão espacial do coração.
(Universidade Federal do Pará, 2018-09-21) PONTES, Paulo André Ignácio; SERUFFO, Marcos Cesar da Rocha; http://lattes.cnpq.br/3794198610723464; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The electrocardiogram (ECG) is one of the most commonly used diagnostic procedures in medicine, so it is essential that undergraduate medical students learn to interpret it correctly while they are still in training. Of course, students go through classical learning (ex: lectures and lectures). However, they are generally not efficiently trained in ECG interpretation. In this regard, educational support methodologies and tools in medical practice, such as educational software, should be considered as a valuable approach for medical training purposes. This thesis deals with the development of a simulator (VETOECG) that allows experiential teaching, so that students can relate to projections of the cardiac electrical vectors, through the manipulation of the spatial orientation of the heart and the repercussions in their respective waves in the ECG. In addition, this thesis reports a formal experiment (pre / posttest with a randomized control group) to evaluate empirically the learning effetiveness of the tool and analyzes the subjective factors of students' perception regarding motivation, user experience and collected through questionnaires. The results indicated that the simulator has positive learning efficacy compared to traditional methodologies (statistically significant difference, p-value <0.0001 *, median of 38.5 points and interquartile range 23.1 to 46.2 points) used for learning in the proposed study. It can be verified that the simulator is adequate in the most diverse dimensions, since they were evaluated positively: in terms of motivation (88.15%), user experience (76%) and learning (96.5%).
Acesso aberto (Open Access)
Designing feasible deployment strategies for cell-free massive MIMO networks : assessing cost-effectiveness and reliability
(Universidade Federal do Pará, 2024-06-14) FERNANDES, André Lucas Pinho; MONTI, Paolo; http://lattes.cnpq.br/4220330196422554; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
Cell-free Massive Multiple-Input Multiple-Output (mMIMO) networks are a promising solution for the Sixth Generation of mobile systems (6G) and beyond. These networks utilize multiple distributed antennas to transmit and receive signals coherently, under an apparently non-cellular communication paradigm that eliminates the traditional concept of cells in mobile networks. This shift poses significant deployment challenges, as conventional tools designed for cellular systems are inadequate for planning and evaluating cell-free mMIMO architectures. In this sense, the literature has been developing models specific to cell-free mMIMO that deal with system coordination, fronthaul signaling, required computational complexities of processing procedures, segmented fronthaul, transitioning from cellular network deployments, and integration to Open Radio Access Network (O-RAN) technologies. These advancements are instrumental in transforming cell-free mMIMO from a theoretical system to a practical application. Despite this, further study is needed to integrate existing models and develop practical evaluation tools to assess the feasibility of cell-free mMIMO and its enablers. This thesis addresses these gaps by proposing new tools to evaluate the feasibility of cell-free mMIMO networks regarding reliability and costs. The first tool focuses on evaluating the reliability of cell-free mMIMO. It is used to improve the understanding of possible failure impacts and to develop effective protection schemes for the fronthaul network of cell-free mMIMO networks. Results for an indoor office implementation with an area of 100 m2 and a Transmission-Reception Point (TRP) spacing of 20 m, demonstrate that cell-free systems with segmented fronthaul, i.e., with serial fronthaul connections between TRPs, require protection strategies. It is shown that interconnecting serial chains and partially duplicating serial chains (40% redundancy) are effective protection schemes. Finally, in the considered indoor scenarios, interconnection appears to be the most feasible alternative when the number of serial chains is higher than three. The second tool assesses the Total Cost of Ownership (TCO) of cell-free mMIMO and its enablers, considering essential aspects, like user demands, fronthaul bandwidth limitations, and hardware processing capacities. The tool is used to evaluate the costs of two functional splits from the literature that are equivalent to distributed and centralized processing architectures for cell-free mMIMO networks. Results for an ultra-dense urban scenario covering an area of 0.25 km2 with up to 800 TRPs, reveal that centralized processing is more feasible for most user demands, hardware configurations of TRP, and cost considerations. Despite this, distributed processing may be more feasible in limited cases of low demand (up to 50 Mbps per user) and under massive cost reductions for expenses related to TRPs deployment.
Acesso aberto (Open Access)
Detecção de danos em superfícies geotécnicas com redes neurais convolucionais de baixa complexidade
(Universidade Federal do Pará, 2024-05-29) ARAÚJO, Thabatta Moreira Alves de; FRANCÊS, Carlos Renato Lisboa; ttp://lattes.cnpq.br/7458287841862567
Most natural disasters result from geodynamic events, such as landslides and collapse of geotechnical structures. These failures are catastrophic that directly impact the environment and cause financial and human losses. Visual inspection is the main method for detecting surface flaws in geotechnical structures. However, visits to the site can be risky due to the possibility of soil’s instability. Furthermore, the terrain design, hostile environment and remote installation conditions make access to these structures impractical. When a quick and safe assessment is necessary, computer vision analysis becomes a potential alternative. However, studies on computer vision techniques still need to be explored in this field due to the particularities of geotechnical engineering, such as limited, redundant and scarce public data sets. In this context, this thesis presents a redes neurais convolucionais, do inglês Convolutional Neural Network (CNN) approach for identifying defects on the surface of geotechnical structures to reduce dependence on human-led on-site inspections. To this end, images of surface failure indicators were collected on slopes on the banks of a Brazilian highway, with the help of UAVs and mobile devices. Next, low-complexity CNN architectures were explored to build a binary classifier capable of detecting flaws apparent to the naked human eye in images. The architecture composed of three convolutional layers, each with 32 filters, followed by two fully connected layers, each composed of 128 neurons and output with one neuron, showed an accuracy of 94.26%. The performance evaluation of the model with the test set obtained AUC metrics of 0.99, confusion matrix, and a AUPRC curve that indicates robust performance of the classifier in detecting damage, while maintaining a low computational complexity, making it suitable for applications field practices. The contributions of the thesis include the provision of an image database, the obtaining of a classification model suitable for scarce data and limited computational resources, and the exploration of strategies for remote inspection and detection of signs of failure in geotechnical structures.
Acesso aberto (Open Access)
Development of machine learning-based frameworks to predict permeability of peptides through cell membrane and blood-brain barrier
(Universidade Federal do Pará, 2024-03-27) OLIVEIRA, Ewerton Cristhian Lima de; LIMA, Anderson Henrique Lima e; http://lattes.cnpq.br/2589872959709848; https://orcid.org/0000-0002-8451-9912; SALES JUNIOR, Claudomiro de Souza de; http://lattes.cnpq.br/4742268936279649
Peptides comprise a versatile class of biomolecules with diverse physicochemical and structural properties, in addition to numerous pharmacological and biotechnological applications. Some groups of peptides can cross biological membranes, such as the cell membrane and the human blood-brain barrier. Researchers have explored this property over the years as an alternative to developing more powerful drugs, given that some peptides can also be drug carriers. Although some machine learning-based tools have been developed to predict cell-penetrating peptides (CPPs) and blood-brain barrier penetrating peptides (B3PPs), some points have not yet been explored within this theme. These points encompass the use of dimensionality reduction (DR) techniques in the preprocessing stage, molecular descriptors related to drug bioavailability, and data structures that encode peptides with chemical modifications. Therefore, the primary purpose of this thesis is to develop and test two frameworks based on DR, the first one to predict CPPs and the second to predict B3PPs, also evaluating the molecular descriptors and data structure of interest. The results of this thesis show that for the prediction of penetration in the cell membrane, the proposed framework reached 92% accuracy in the best performance in an independent test, outperforming other tools created for the same purpose, besides evidencing the contribution between the junction of molecular descriptors based on amino acid sequence and those related to bioavailability and cited in Lipinski’s rule of five. Furthermore, the prediction of B3PPs by the proposed framework reveals that the best model using structural, electric, and bioavailability-associated molecular descriptors achieved average accuracy values exceeding 93% in the 10-fold cross-validation and between 75% and 90% accuracy in the independent test for all simulations, outperforming other machine learning (ML) tools developed to predict B3PPs. These results show that the proposed frameworks can be used as an additional tool in predicting the penetration of peptides in these two biomembranes and are available as free-touse web servers.
Acesso aberto (Open Access)
Estratégia de planejamento e otimização do handover em redes móveis densificadas
(Universidade Federal do Pará, 2018-06-29) SILVA, Ketyllen da Costa; ARAÚJO, Jasmine Priscyla Leite de; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The increase in mobile devices and applications in recent years has led to an overload of the network infrastructure responsible for disposing this traffic, thus affecting the performance of the network as the user experience.Heterogeneous mobile networks are already a reality and their densification has been put forward as one of the suggested solutions to meet the expected demands for 5th generation (5G) mobile networks. However, in the current networks, it is still common for fixed parameters to be used for the configuration of the network although this strategy does not always prove to be efficient. It is within this context that the concept of selforganizing networks (SONs) has been established, in which several network parameters are automatically adjusted on the basis of measurements and intelligent systems in real time. This thesis presents a strategy to optimize the handover in LTE networks with dense small cells. Based on measurements and fuzzy logic new algorithms are proposed for self-tuning network parameters. From discrete simulation using MATLAB, the results are evaluated and presented through the main performance metrics of handover.
Acesso aberto (Open Access)
Geração de tarefas de ensino adaptadas através de algoritmos bio-inspirados para crianças em fase inicial da alfabetização
(Universidade Federal do Pará, 2018-09-14) SOUZA JÚNIOR, Gilberto Nerino; MONTEIRO, Dionne Cavalcante; http://lattes.cnpq.br/4423219093583221; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
Advances in learning systems over the past two decades have enabled the development of technologies that help in the engagement of students. Although these systems may use behavioral procedures to improve reading skills, better outcomes for each student are obtained in the manual elaboration of a set of tasks by educational experts. However, the use of a manual process requires too much time, effort and subjectivity for the creation of tasks. Additionally, even with the aid of computational processes, the automatic generation may be impracticable due to the high search space for the possible combinations of tasks. This process could consider adapting the difficulty of a task to the student's knowledge, something little explored in educational work for children at the beginning of reading learning. The present thesis implements an approach to generate teaching tasks from the Matching-to-Sample procedure, adapting its difficulties through bio-inspired optimization meta-heuristics. This approach uses pre-test results applied to students and the configuration of teaching contents determined by educational tutors; these data allow the use of the algorithms to generate tasks and then the tasks can be presented in learning software. Experiments demonstrated a better convergence of the genetic algorithms for this domain, being able to generate tasks on a level of difficulty adapted to the students, and also according to pretests and configurations of attributes of the tasks defined by behavioral psychologists. As validation for this study, the tasks were applied to a group of students in the early stages of literacy achieving satisfactory effects in the individual learning process. In addition, two interactive learning software were implemented through a digital game and a web application, where the use of the digital game with playful features showed superior acceptance in the use of teaching tasks adapted for children in the initial phase of literacy.
Acesso aberto (Open Access)
Intent-based radio resource scheduling in ran slicing scenarios using reinforcement learning
(Universidade Federal do Pará, 2024-11-04) NAHUM, Cleverson Veloso; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization to deal with scarce and limited frequency spectrum resources while fulfilling the slice intents in an intent-based system. In this scenario, radio resource scheduling is an essential function to provide the resource management needed to prevent intent violations, hence providing sufficient radio resources for RAN slices to accomplish their intents. The wide variety of scenarios supported in 5G and beyond 5G (B5G) networks makes the radio resource scheduling (RRS) problem in RAN slicing scenarios a significant challenge. This thesis proposes an intent-based RRS for RAN slicing using reinforcement learning (RL) to fulfill the slice intent. The proposed method aims to prevent intent violations by making the management of resource block groups (RBGs) available between slices and users’ equipment (UEs) using inter-slice and intra-slice schedulers, respectively. This thesis also proposes investigating a slice prioritization structure to ensure the intent of more important slices when the available radio resources are insufficient to guarantee all slice’s intents. This thesis proposal presents results obtained using an intent-based RRS with RL for a fixed number of slices and also for multiple network scenarios, aiming to demonstrate the importance of intentbased RRS design for scenarios with RAN slicing. The proposed method outperformed the baselines in fixed and multiple network scenarios, protecting high-priority slices and minimizing the total number of violations.
Acesso aberto (Open Access)
Uma metodologia para aferição da acurácia de modelos de projeção de longo prazo para a Previdência Social no Brasil
(Universidade Federal do Pará, 2017-12-01) SILVA, Carlos Patrick Alves da; PUTY, Claudio Alberto Castelo Branco; http://lattes.cnpq.br/5885474167011571; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
Long-term social security statistical forecasts produced and disseminated by the Brazilian government aim to provide accurate results that would serve as background information for policy decisions. These forecasts are being used as support for the government’s proposed pension reform. However, the reliability of official results is uncertain since no systematic evaluation of these forecasts has ever been published by the Brazilian government or anyone else. This work aims to present a study of the accuracy and methodology of the instruments used by the Brazilian government to carry out long-term actuarial forecasts. More specifically, this work investigates what would be the source of data, assumptions, equations, variables, parameters and estimation methods used to compute results released by the federal government. An empirical analysis shows that the long-term Social Security forecasts are systematically biased in the short term and have significant errors that render them meaningless in the long run. In addition, attempts to reproduce the results of the 2012 and 2018 LDOs demonstrate the lack of transparency in official documents, both in the described equations and the databases used. From a mathematical model developed, it was shown that the long-term forecasts of variables, such as GDP, Social Security revenues and expenses, have a large component of volatility and uncertainty which make your forecast challenging in the short term and impossible, with an acceptable level of confidence, in the long run. A sensitivity analysis for the productivity and average contribution rate parameters showed the impact of these on Social Security results, showing a gain of up to 72% in revenue for an annual labor productivity growth of 3%, for example. Finally, a free and open source software, developed under this research, that implements the current official forecast model is presented, as well as several improvements in the design process, such as the ability to simulate changes in the labor market.
Acesso aberto (Open Access)
Network slice admission using reinforcement learning and information-centric networking for mobile networks
(Universidade Federal do Pará, 2019-08-21) BATISTA, Pedro dos Santos; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
The evolution of the current most popular mobile network (4G), the so-called 5G, is targeting an increased traffic load at a lower cost. Thus, optimization of the delivery network plays an essential role at 5G; another aspect of the evolution is that 5G has the ambition to be highly customized, e.g., reliable enough to be used in industrial automation and cheap enough to be used for mobile broadband services. In this context, this thesis assesses two aspects of 5G: the first is to use information-centric networking (ICN) to improve the efficiency of multimedia delivery in mobile broadband services; and the second is the application of a reinforcement learning strategy as an enabler for the highly configurable network, which could pose a challenge to be understood and configured manually. ICN aims at circumventing several issues of current internet protocol, among them, achieving a more efficient multimedia distribution. Given the significant growth rate of video transmission over mobile networks, it is sensible to consider how mobile networks can leverage ICN. There is a substantial body of work considering ICN for fixed networks and also for the core of mobile networks. Less attention has been dedicated to ICN on the radio access network (RAN) or ICN-RAN, which has currently a user plane based on many connection-oriented protocols. To fully benefit from ICN, mobile networks must enable it on the RAN, not only on the core. This work details an ICN deployment on the RAN of the fourth and fifth generation of mobile networks and also presents a testbed that enables proofs of concept of this ICN-RAN using 4G. The results indicate, for example, that evolving ICN features can be tested with currently available tools, but the lack of hardware accelerators and optimized code limit the bit rate that can be achieved in real-time processing. In the context of network customization, the most prominent enablers are the so-called network slices. Slices can be understood as a part of the network that is customized to deliver certain services. The service requirements are imposed by the tenant, which acquire slices from an infrastructure provider. The 5G infrastructure provider must optimize the infrastructure resource utilization, usually admitting as many slices as possible. However, infrastructure resources are finite and admitting all the slices could increase the probability of service level agreement violation. This thesis investigates the application of reinforcement learning agents that learn how to increase the infrastructure provider revenue by intelligently admitting network slices that bring the most revenue to the system. We present a neural networks-driven agent for network slice admission that learns the characteristics of the slices deployed by the tenants from their resource requirements profile and balances the benefits of slice admission against orchestration and resource management costs.
Acesso aberto (Open Access)
Um Protocolo de roteamento colaborativo para transmissão de vídeo com computação em névoa em redes ad hoc veiculares
(Universidade Federal do Pará, 2019-03-22) BEZERRA, Paulo Henrique Gonçalves; CERQUEIRA, Eduardo Coelho; ROSÁRIO, Denis Lima do; http://lattes.cnpq.br/8273198217435163
Vehicular Ad hoc Networks (VANETs) play an important role in the efficiency of road traffic by improving safety and acting as a facilitator of services for passengers, drivers and public safety officers. Recent improvements in the routing protocols and topologies used in vehicular networks have contributed to improvements in scalability, reliability and the quality of the information-sharing experience. Vehicles can cooperate with each other to stream videos of accidents or disasters and provide visual information of the monitored area with great precision. This Ph.D thesis proposes a Collaborative Routing Protocol for Video streaming VANETs (CRPV) using the service of fog storage to minimize the sharing of content. The routing table is based on an indicator that is generated by combining the speed, location and recording angle parameters of each vehicle involved in vehicular collaboration to reduce the unnecessary exchange of video data in vehicle-to-vehicle communications. The results of the simulations show that the proposed model performs favorably when compared to other routing protocols with respect to the availability of end-to-end communication and Quality of Experience.
Acesso aberto (Open Access)
Sistema fuzzy para decisão de handover vertical e maximização da vida útil da bateria em redes multimídia sem fio heterogêneas
(Universidade Federal do Pará, 2019-03-15) COQUEIRO, Thiago Antônio Sidônio; FERREIRA JÚNIOR, José Jailton Henrique; http://lattes.cnpq.br/9031636126268760; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The applications that consume high bandwidth and energy consumption have been increasing considerably fast in mobile networks. However, the mobile devices do not offer support to battery capacity for the intensive / continuous use of such applications. In addition, mobile networks currently have a high degree of heterogeneity and comprise a wide variety of networks with different technologies, such as LTE, Wi-Fi and WiMAX. Therefore, it is necessary the tradeoff to ensure that QoE is provided to users in this scenario, as well as an energy efficiency strategy designed to extend the battery life of mobile devices. This thesis proposes an intelligent architecture based on Fuzzy logic, capable of providing support to decision making to save the energy of mobile devices within an integrated LTE and Wi-Fi network. Considering user satisfaction, the gains obtained through the PSNR, SSIM and VQM metrics were respectively 32%, 31% and 91% higher than the architecture without Fuzzy logic. Thus, the simulated experiments show the benefits and feasibility of the proposed solution.
Acesso aberto (Open Access)
Uso de algoritmo genético com operadores modificados para otimização de funções de variáveis reais
(Universidade Federal do Pará, 2019-04-26) YASOJIMA, Edson Koiti Kudo; TEIXEIRA, Otávio Noura; http://lattes.cnpq.br/5784356232477760; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
and a novel statistic correlation mutation algorithm (CAM). Both ADX and CAM work with population information to improve existing individuals of the GA and increase the exploration potential via the correlation mutation. Solution-based methods offers good local improvement of already known solutions while lacking at exploring the whole search space, evolutionary algorithms provide better global search in exchange of exploitation power. Methods that increase the search potential are widely used for constrained optimization problems due to increased global and local search capabilities. The GA with the proposed operators improves results of constrained problems by balancing the exploitation and exploration potential of the algorithm. The conducted tests present average performance for various CEC’2015 benchmark problems, while offering good reliability and superior results on path planning problem for redundant manipulator and most of the constrained engineering design problems tested when compared with current works in the literature and classic optimization algorithms.

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