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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Item Acesso aberto (Open Access) Abordagem de leitura de texto em imagens provenientes de redes sociais para ganho em disponibilidade de dados(Universidade Federal do Pará, 2017-10-19) FERREIRA NETO, Luiz Cortinhas; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858This work aims to propose a methodological adaptation in the process of social network analisys, based on the inclusion of text extracted from images that are obtained from the social networks themselves. Highly important for market intelligence, product analysis, CRM and SCRM processes, since these are market trends used by large companies, thus, promotes financial and research incentives. The adaptation proposed in here has its importance based on data availability, which has become increasingly restricted, thanks to the use of APIs, interfaces of data access management where, in several different ways, each social network limits the data query, either by type of data, quantity or collected window. This research intends to prove, through case studies, that there is relevant information gain to sentiment analyses process when textual data derived from images are used.Item Acesso aberto (Open Access) Agrupamento de fornos de redução de alumínio utilizando os algoritmos Affinity Propagation, Mapa auto–organizável de Kohonen (som), Fuzzy C–Means e K–Means(Universidade Federal do Pará, 2017-10-11) LIMA, Flávia Ayana Nascimento de; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318The continuous development of technology accounts for measures that provide industries benefits to grant them profitability and competitive advantage. In the mineralogy field, aluminum smelting usually requires substantial number of cells, also known as reduction pots, to produce aluminum in a continuous and complex process. Analytical monitoring is essential for those industries’ competitive advantage, given that during operation some cells show behavior similar to others, thereby forming clusters of cells. These clusters depend on data patterns usually implicit or invisible for the operation, but can be found by data analysis techniques. In this work four clustering techniques are presented to that end: the Affinity Propagation; the Kohonen Self Organizing Map; the Fuzzy C–Means; and the K–Means Algorithm. These techniques are used to find and group cells that share similar behavior, by analysing seven variables which are closely related to the aluminum reduction process. This work aims at addressing the benefits of clustering, especially by simplifying the aluminum potline analysis, once a large group of cells might be summarized in one sole group, what can provide more compact yet rich information for data driven modeling and control. Moreover, the identification of similar data patterns in clusters makes the task of those who is going to be in charge of analyzing these dats. This work also identifies the ideal cluster size for each technique applied.Item Acesso aberto (Open Access) Alocação de dois níveis para uma arquitetura h-cran baseada em offloading(Universidade Federal do Pará, 2019-01-24) GONÇALVES, Mariane de Paula da Silva; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734The accelerated data and apps growth represents significant challenges to the next generation of mobile networks. Amongst them, it is highlighted the necessity for a co-existence of new and old patterns during the transition of architectures. Thus, this paper has investigated solutions for offloading into a hybrid architecture, also known as H-CRAN (Heterogeneous Cloud Radio Access Network Architecture), that centralizes processing and searches a better use of the network resources. The strategy of optimization was analyzed through the evolutive algorithm PSO (Particle Swarm Optimization), in order to find a suboptimal solution to the allocation of two levels (TLA) in the H-CRAN architecture and another one based on FIFO (First In, First Out), for benchmarking purposes. SNR (Noise Interference Signal) average, Maximum Bit Rate, the number of users with or without connections and number of connections in RRHs and macro were used as performance measurements. Through the results, it was noticed an improvement of approximately 60% in the Maximum Bit Rate when compared to the traditional approach, enabling a better service to the users.Item Acesso aberto (Open Access) Análise do QoE para streaming de vídeo em redes aéreas 5G mmWave de conectividade dupla(Universidade Federal do Pará, 2022-02-18) PEREIRA, Ronilson Williame da Silva; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497The evolution in mobile telecommunications has allowed the emergence of new network formats aimed at meeting the growing demand for wireless data, mainly fueled by more viewing of video content. The scarce spectrum available in current cellular networks does not seem to be able to meet this explosion of wireless data, motivating a shift to explore new frequency bands. Currently, one of the major problems of current mobile networks is congestion in denser areas such as concerts, sporting events, and festivals (temporary events), as we have a greater concentration of users, consequently increasing the volume of data traffic. Millimeter-wave (mmWave) communication, frequency bands between 30 and 300 GHz emerges as an essential part for the next generation of fifth-generation cellular networks (5G), as they adopt higher carrier frequencies offering high bandwidth and lower latency. However, system performance degrades due to high propagation loss and the sensitivity of the links to obstacles. To mitigate such factors, this dissertation proposes a dual connectivity 5G mmWave architecture using LTE (Long Term Evolution) and the use of UAVs (Unmanned Aerial Vehicles) as airbase stations to provide connectivity guarantees and quality of experience. in video streaming, one scenario where the proposal might be a suitable solution is to provide coverage for temporary events. Through simulations, an analysis of the Quality of Experience (QoE) was presented about the transmission of 4K videos in order to evaluate the transmission and quality of videos perceived by users. The simulated results show the efficiency of the system for multimedia applications using videos, improving the QoE of wireless users by 43%.Item Acesso aberto (Open Access) Análise dos fatores relacionados ao desempenho das escolas no IDEB: estudo de caso no Estado do Pará(Universidade Federal do Pará, 2022-02-11) GOMES, Vitor Hugo Macedo; SILVA, Marcelino Silva daThe complexity of identifying all the factors that are related to the performance of schools on the Basic Education Development Index (IDEB) is enormous. In this study, three databases were analyzed with the objective of identifying several factors that correlate with low performance in state schools in the state of Pará. Initially, it was observed through the analysis that 142 municipalities in the state were at risk of not meeting the goal regarding the reduction of school dropouts and, consequently, affecting the performance of schools. This study used educational data mining techniques to, first, select variables with structural characteristics in the teaching environment, comparing the schools with higher and lower performance in IDEB, identifying possible relationships with school dropouts. Then, the Randon Florest (RF) algorithm was used to select the most important variables that directly or indirectly impact the IDEB index. After the selection phase, the variables were submitted to the Linear Regression (LR) algorithm. The results reveal that in the group of schools below average in IDEB, 60.6% reside in families with incomes up to one minimum wage, while 37.5% have incomes above one minimum wage. In the group of schools above average in IDEB, 42.4% live in families with incomes up to one minimum wage, while 51.6% live in families with incomes above one minimum wage. Evidencing that family income is related to better IDEB scores and, consequently, better infrastructure conditions. The results also indicate that the income of students’ families is related to the average family income in the analyzed municipalities. Next, variables related to parents’ income were used to identify a possible relationship between parents’ schooling and students’ performance. Finally, the analysis ends with the analysis of the impact of the Municipal Human Development Index (HDI) on the variables related to the students’ grades, the teachers’ qualifications, and the teachers’ experience in the school environment. The results reveal that there is a correlation between the index and student learning in the classroom. On the other hand, better IDEB scores are directly related to the adequacy of the curriculum to the subject taught, in addition to good working conditions for teachers.Item Acesso aberto (Open Access) Análise e classificação de severidade de COVID-19 usando aprendizado de máquina(Universidade Federal do Pará, 2022-08-16) LIMA, Marco Antonio Loureiro; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734In the last years, with the alarming growth of COVID-19 cases, a highly contagious viral disease, new forms of diagnosis and control for this sickness have become necessary to the spread decreases until the population is effectively vaccinated. In this context, Artificial Intelligence (AI) and its subfields appear as possible alternatives to help and provides a response to combat the virus. Some Machine Learning (ML) methods are shown as an answer to control this disease, these methods can perform an analysis based on a set of symptoms presented by the patient and consequently indicating the diagnosis, as well as streamline the treatment process. To achieve this goal in this paper, three models that uses ML methods to predict COVID-19 severity on different degrees are proposed, unlike other works whose purpose was to diagnose only the presence or absence of COVID-19, this paper aims to improve the classification of the patient’s disease state. The results in each of these models are evaluated through the metrics established in this work. Furthermore, there are distinct suggestions to improve the analysis and make predictions with greater accuracy..Item Acesso aberto (Open Access) Uma análise sócio-demográfica da incidência de hanseníase na Amazônia legal brasileira: abordagem baseada em redes bayesianas(Universidade Federal do Pará, 2019-02-08) GOMES, José Maria da Silveira; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567Leprosy is a millenarian contagious disease, with chronic and stigmatizing characteristics, from the remotest times of humanity until today. It is characterized as a disease of the poor and Brazil is the second country in the world with the highest incidence. The lack of public policies aimed at reducing poverty through the improvement of socio-economic factors in the country is directly related to the incidence of the disease in Brazil. Strategies for control and monitoring should follow intelligent actions. One of the solutions for monitoring the disease is the use of Bayesian networks as a probabilistic method for taking decisions on both the control and the procedures to adopt in order to reduce the incidence of the disease. The objective of the present study is to analyse the association of leprosy incidence in relation to indicators of human development, habitation and income level, considering the Brazilian Amazon region in relation to the entire country. An ecological study, based on data obtained on cases of leprosy in Brazil for the year 2010, obtained from the Information System of Hardship Notifications (SINAN) through the Informatics Department of the National Health Service (DATASUS) and the socio-economic indicators found in the Demographic Census Research database of the Brazilian Institute for Geographical and Statistical Survey – IBGE, as well as information from the Municipal Human Development Index, regarding education and income, obtained from the website of the Human Development Atlas of Brazil, also for the year 2010. The methodology combined data mining with the analysis of spatial distribution. The Bayesian network technique was used aimed at measuring the association between variables of the domain of the problem as well as to establish the analogy of the data between the municipalities under study with data for all other Brazilian municipalities. Applying the algorithm K2 relevant associations were found for the following indicators applied in the investigation: Brazilian Legal Amazon, Municipal Human Development Index of Income and Education and Household Housing Condition. Using the Bayesian network model adopted, there is a significant association between the percentage of homes with more than 2 inhabitants and the rate of incidence of leprosy. Although the relationship between the rate of incidence, socio-economic factors (no water supply, no toilet, poverty and overcrowding of the home), low educational indices and income has already been reported in several studies, the insertion of the indicators that considers population density of the home was a novel proposition of the present study and the indicators of greatest most significance of this investigation. The analysis of leprosy incidence with respect to spatial distribution, comparing the Amazon region with the entire country, revealed that public policies for habitation in the studied region were almost non-existent, since the population density of homes is very high, facilitating the appearance of contagious diseases such as leprosy.Item Acesso aberto (Open Access) Uma análise técnico-econômica para implantação de arquiteturas centralizadas de redes de telefonia móveis(Universidade Federal do Pará, 2018-03-06) SOUZA, Daniel da Silva; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734Upon the the challenges proposed by the fifth generation of mobile networks, the architecture of C-RAN (Centralized Radio Acess Network) has gained space by supporting high-capacity ultra-densas networks of next generation and offering economies. This dissertation proposes a TCO (Total Cost of Ownership) for C-RAN, CAPEX (Capital Expenditure), OPEX (Operational Expenditure) and these are the fundamental criteria in the field of investment assessment and projection. It is soon presented with a higher level of detailing as to the investment aspects, which are of great relevance to the architectural landscape of mobile communication networks. In this way, this work is conducted in order to evaluate the economic context of the implementation of a centralized architecture, based mainly on the financial aspects that service operators need to plan before deploying a new Mobile Access Network. The proposed model is used in a case study where the total cost of implementation and operation of the distributed and centralized architectures is compared taking into account several specific scenarios. The results point to an economy in the centralized scenarios and highlight the most relevant economic aspects in the planning of C-RAN.Item Acesso aberto (Open Access) Arquitetura de modelos híbridos, machine learning e otimizadores para análise de consumo de energia elétrica e produtividade em pintura automotiva(Universidade Federal do Pará, 2024-03-27) OLIVEIRA, Rafael Barbosa de; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318Strategies for optimizing energy consumption in the painting stages are emerging as key factors in promoting more sustainable and competitive production in the automotive sector. This dissertation seeks to predict energy consumption and maximize productivity in automotive painting, using an approach that combines variable selection, hybrid models, hyperparameters of these models and meta-heuristic optimization in a 3-stage architecture. Automotive painting processes have variables in the form of time series that describe the history of energy consumption. In stage 1, the best machine learning model is chosen (Random Forest, Long-Short Term Memory, XGBoost and GRU-LSTM) to predict energy consumption time series at t+1. In step 2, the RF, XGBoost and Dense Artificial Neural Network (ANN) models are evaluated to select the best predictor of the number of vehicles produced (cycles). In step 3, the best metaheuristic between Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is selected to optimize the energy consumption predicted by the best model from step 1, using the best model from step 2 as a fitness measure. The final architecture reduced the energy consumed by up to 16% and increased the cycle by 127%, using the GRU-LSTM models in step 1, Dense ANN in step 2 and DE in step 3. The results highlight the opportunity to use the proposed approach to optimize energy consumption and productivity in automotive painting.Item Acesso aberto (Open Access) Uma arquitetura de pré-processamento para análise de sentimento em mídias sociais em português brasileiro(Universidade Federal do Pará, 2018-08-23) CIRQUEIRA, Douglas da Rocha; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858The Web 2.0 and the evolution of Information Technologies have brought novel interaction and relationship channels. In this context, the Online Social Networks (OSN) are an example as platforms which allow interactions and sharing of information between people. In this scenario, it is possible to observe the adoption of OSN as a channel for posting opinions regarding products and experience. This scene presents an excellent opportunity for companies that aim to improve products, services and marketing strategies, given OSNs are powerful sources of massive unstructured data generated by consumers (UGC), with opinions and reviews concerning offers, in platforms such as Facebook, Twitter and Instagram. Brazil is a highlight in this scenario, where this phenomenon can be observed, as the Brazilian population is one of the most active in social media platforms in the world. This makes it a country full of opportunities to market exploitation. In this context, computational techniques of Opinion Mining and Sentiment Analysis (SA) are applied aiming to infer the polarity (positive, negative, neutral) regarding a sentiment associated to texts, and can also be applied in data from OSN to evaluate the feedback from a target audience. Although the existing diversity of SA strategies reported in the literature, there are still challenges faced in the application of SA in text data from OSN, given the characteristics of the language adopted in such platforms. The state of art is focused on SA towards the English language, and the existing proposals for Brazilian Portuguese do not have a standardized methodology for preprocessing steps. In this context, this research investigates an approach with no translation, and proposes a novel preprocessing architecture for SA towards Brazilian Portuguese, aiming to provide enriched features to SA algorithms. The proposal was compared with well-established baselines from the literature, and the obtained results indicate that this architecture can overcome the state of art recall in at least 3% , for 6 out of 7 datasets evaluated.Item Acesso aberto (Open Access) Ciência de Dados Aplicada em Dados Públicos: Estudos de Caso Acerca da Previdência Social Brasileira.(Universidade Federal do Pará, 2020-04-17) FELIX JÚNIOR, Francisco Eguinaldo de Albuquerque; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497Data Science is an interdisciplinary area related to data analysis, which aims to extract knowledge and possible decision-making about specific problems. In this context, open government data, which often need pre-treatments and computational methods to process their data sets, present themselves as potential sources of information to be explored taking the Data Science’s perspective, allowing the development of strategies each time more efficient and optimized in public management. Given this, and allied to the recent discussions related to the reform in the Brazilian social security, this dissertation presents two case studies referring to analyzes in the national social security system. The first study used the microdata referring to the demographic censuses of 2000 and 2010, made available by IBGE, proposing to evaluate the participation that retirements and pensions have in the income inequality of the population in the years evaluated about Brazilian states and municipalities. The results show that, although the analyzed benefits contribute to the Brazil income concentration, the portion corres=ponding to a minimum wage contributes to the deconcentration of income, and the portion above one salary contributes to the concentration, being a repetitive pattern throughout the country. On the other hand, the second study proposed an evaluation of the impacts caused by the pension reform, which is proposed in PEC 06/2019. It was observed that PEC 06/2019 would hinder access to benefits, in which approximately 83,28% of the pensions would not have been granted had it been in effect since 1995.Item Acesso aberto (Open Access) Classificação de arritmias cardíacas através de uma estrutura competitiva de redes neurais convolucionais autoassociativas(Universidade Federal do Pará, 2023-05-11) CORRÊA FILHO, Sérgio Teixeira; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860This work proposes a system for classifying cardiac arrhythmias based on a competitive structure of Autoassociative Convolutional Neural Networks. Three neural networks were trained to reconstruct Electrocardiogram (ECG) signals for cases of patients with supraventricular, ventricular and normal beats. After training, the networks were allocated in a competitive parallel structure for classification of arrhythmias. The MIT-BIH arrhythmia public database of ECG signals was used for training and testing the networks, and for each ECG signal, from each patient, the QRS complexes of the heartbeats were extracted, which were the characteristics used as input. for the system, and these signals, which were in the form of temporal signals (1D), were transformed into digital images (2D) in order to use the capacity of convolutional neural networks for pattern recognition and feature extraction in images. For the development and performance analysis of the proposed structure, two paradigms that have been used in works already presented in the literature were used: interpatient paradigm and intrapatient paradigm, and the system obtained an accuracy of 96.97%, sensitivity of 96.30% and precision of 93.59% for the intrapatient case and accuracy of 94.05%, sensitivity of 70.43% and precision of 65.74% for the interpatient case. A comparative analysis with results from arrhythmia classification systems already presented in the literature shows that the proposed system presented similar results or, in some cases, better results than those already obtained, thus showing the applicability of the proposed structure to the problemItem Acesso aberto (Open Access) Classificação de ransomware utilizando MLP, redução de dimensionalidade e balanceamento de classes(Universidade Federal do Pará, 2023-07-03) PEREIRA, George Tassiano Melo; SALES JÚNIOR, Cloaudomiro de Souza de; http://lattes.cnpq.br/4742268936279649Ransomware is a type of malware that prevents or limits user access to system and files until aransom is paid. Combating this threat is difficult due to its rapid spread and constant changes in the encryption techniques used. Machine learning algorithms such as Artificial Neural Networks have been touted as promising tools in classifying ransomware because they can learn to identify complex patterns and features in large amounts of data. This allows neural networks be trained on sample examples of malicious software, including ransomware, and then be able to classify new examples with high accuracy. Furthermore, neural networks are also capable of learning and adapting to changes in malware behavior, making them effective tools for detecting new types of ransomware. In this work, three types of ransomware classification by ANN are explored within a composite pipeline with dimensionality reduction by Kernel PCA and class balancing with the random oversampling approach. The MLP (Multi-layer Perceptron) reached an average of 98% accuracy in the binary classification and 85% accuracy in the goodware family classification, where such values surpass the previous results and thus demonstrate the effectiveness of the inclusion of the class balancing in improving the ransomware detection model.Item Acesso aberto (Open Access) Classificação de regiões de desmatamento via imagens do satélite landsat no nordeste do Pará(Universidade Federal do Pará, 2023-12-18) CANAVIEIRA, Luena Ossana; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434Item Acesso aberto (Open Access) Classificação de tumores cerebrais: um estudo comparativo entre rede neural convolucional e rede neural convolucional com mecanismo de atenção(Universidade Federal do Pará, 2024-09-30) SILVA, Ulrich Kauê Mendes Alencar da; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860Brain tumors are neurological diseases with a high potential impact on the lives of affected individuals, requiring a rapid and accurate diagnosis through complementary imaging tests, such as magnetic resonance imaging, which is considered the gold standard in this process. Considering the need for faster diagnosis, classification systems based on Machine Learning have been developed and within this context, this dissertation aims to present a comparative study between a Convolutional Neural Network (CNN) and a CNN with an attention mechanism, developed for the classification of brain tumors from magnetic resonance images. The comparative study aims to identify the impact of the attention mechanism on the performance of the CNN for tumor classification. For the development and evaluation of the proposed models, a public database was used, collected from the Kaggle website and made available by Masoud Nickparvar, which is composed of 7023 brain magnetic resonance images, segmented into four classes: glioma, meningioma, no tumor and pituitary. As a result, from the performance metrics obtained, considering the image base used for testing in both CNNs, an improvement in the CNN performance was observed after the introduction of the attention mechanism, where the network with this mechanism presented an increase of 1.98% in the accuracy metric, 2.07% in the precision metric, 2.18% in the sensitivity metric and 1.72% in the F1-score metric in relation to the CNN without the attention mechanism. It is also possible to highlight the results obtained in particular for the meningioma tumor class, since the CNN without the attention mechanism presented difficulties in classifying this class and, after the integration of the attention mechanism, the model obtained an accuracy increase of 6.54% for this class.Item Acesso aberto (Open Access) Clustering-driven equipment deployment planner and analyzer for wireless non-mobile networks applied to smart grid scenarios(Universidade Federal do Pará, 2018-03-16) VRBSKÝ, Ladislav; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497The modern power grids, known as smart grids, rely on various advancements, one of them being the introduction of bi-directional communication. In some cases, data exchanged in the network is of critical importance. The data transmissions need to meet speci c delay limits set by the regulatory agencies in order for the smart grid to function properly. Meeting these standards allows the use of new applications of monitoring, control and system protection, resulting in a more e cient, stable and environment-friendly system. This thesis presents a methodology for analysis and planning of wireless communication networks for smart grid, which uses a clustering algorithm to determine the optimal positions of the routers and gateways of the network to be installed. After, it calculates the delay for each Intelligent Eletronic Device that is a network subscriber. This way, an analysis can be made to obtain the Quality of Service requested for a speci c network setup in a speci c scenario. The results obtained in the performed case study show that it is possible to achieve a network topology that satis es the maximum delay requirements of 100% of its subscribers, using WiMAX or a combination of Wi-Fi and WiMAX. Also, the thesis explores a restricted communication mode that can temporarily suspend the transferences of non-critical data. In most scenario con gurations, the restricted mode delivers all the data within the maximum delay. The software implementation of the proposed model is made publicly available under open-source license, so that anyone, including researchers, or private and public companies, can take advantage of it. The model presented in this thesis is customizable, allowing the use of other technologies and be used with other networks, including scenarios that are not related to smart grid.Item Acesso aberto (Open Access) . Clusterização, classificação e predição de “pré-efeito anódico” de cuba eletrolítica de alumínio primário(Universidade Federal do Pará, 2020-08-21) CONTE, Bruno Nicolau Magalhães de Souza; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318The industrial sector is one of the main responsible for the serious environmental situation on the planet and also for increasing legal requirements, in relation to the waste generated. On the other hand, many companies have reacted proactively, based on the implementation of management strategies such as: clean production, environmental certification, reduction of toxic waste, recycling, sustainable consumption and reuse, mainly. It is worth mentioning that the aluminum reduction process is responsible for a large amount of greenhouse gas emissions and, thus, promotes environmental impacts and serious climate changes. During the aluminum reduction process, the occurrence of the anodic effect causes an extreme increase in the tub tension and, consequently, an increase in the bath temperature, with very high temperatures, resulting in a thermal disturbance, with the possibility of melting the insulating layer of the vat and the final consequences are the loss of production in the entire vat line, its shortened service life and the production of PFC gases. Seeking a strategy based on sustainability, I try to take into account the problem of the worsening of the Greenhouse Effect, the extreme increase in kiln tension and, consequently, the loss of production in the entire line of vats, this work proposes the use of an Artificial Neural Network together with Clustering algorithms to automatically create anodic pre-Effect labels, and thus predict the nonlinear dynamic behavior of the primary aluminum reduction industry oven anodic pre-effect, based on actual vat data electrolytic. With the use of these Machine Learning models, it is possible to predict the occurrence of the anodic pre-effect, allowing process operators to take mitigating measures to suppress the anodic effect, avoiding the loss of aluminum production in the vat and decreasing the emission of gases from the greenhouse effect.Item Acesso aberto (Open Access) Custo total de propriedade para infraestruturas de comunicações 5G para smart grid(Universidade Federal do Pará, 2019-01-24) MELO, Paulo Tássio da Luz; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497Smart Grid communications networks are considerably different from the traditional communica-tion systems used to access the Internet when considering users, applications, Quality of service and, especially, the impacts/losses due to malfunctions. Such data networks are generally owned and used exclusively by electrical system operators and require a high financial investment. There fore, this paper presents an economic analysis to compare different possibilities of data network deployment for the Smart Grid. The results showed that for the proposed scenario, 5G compared toother technologies, obtained the best evaluation for the implementation of the communication of a network of data applied to the Smart Grid, since the data of Quality of Service and the results obtained in the Total Cost of Property, showed that in the medium and long term the 5 Ghas its lower cost when faced with expenditures of other communication technologies, presenting 100% and 99.02% of IEDs with Quality of Service for uplink and downlink maximum, and the average IEDs served, is 100% for uplink and 99.4% for downlink in NORMAL and STRIP mode, respectively.Item Acesso aberto (Open Access) Data science aplicado a dados abertos do Governo Federal: estudos de caso sobre a economia dos municípios brasileiros.(Universidade Federal do Pará, 2020-03-13) SANTOS, Sandio Maciel dos; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497The process of analyzing open databases in recent years has gained considerable prominence in the Brazilian scenario since the granting of Law 12,527 / 2011, which guarantees access to public information, allowing for better transparency of public spending by society. Allied to this, numerous discussions arose around the use of Brazilian government microdata, among which we highlight the discussions on social security reform and the analysis of fiscal health in Brazilian municipalities through social security approaches. Thus, this work focuses on the use of Data Science, specifically in the KDD process to analyze microdata from Brazilian municipalities. Thus, in this work, two different approaches are made, the first of which performs a descriptive statistical analysis without inferences, to understand the fiscal health of Brazilian municipalities between 2010 and 2017, through transfers from the RGPS. The second approach to fiscal analysis using the STVAR model through the following variables: expenditure, revenue, and GDP of the municipality of São Paulo. The results of analysis I show that municipalities with populations greater than 100 inhabitants do not show a deficit due to the difference between municipal collections and transfers from the RGPS. In analysis II, the results found show that the economic cycle analyzed when undergoing exogenous shock (or external impulse) can generate changes in the states of recession and expansion with an average duration of 12 months.Item Acesso aberto (Open Access) Deep learning in education 5.0: proposing 3d geometric shapes classification model to improve learning on a metaverse application(Universidade Federal do Pará, 2024-01-18) SANTOS, Adriano Madureira dos; SERUFFO, Marcos César da Rocha; http://lattes.cnpq.br/3794198610723464; https://orcid.org/0000-0002-8106-0560The Brazilian educational system faces significant challenges, as evidenced by low educational development assessment scores. Due to the traditional educational model employed in the country, there are difficulties in the effective transmission of complex content, leading to high rates of academic failure and subsequent school dropout. The lack of innovation, especially in basic education settings, contributes to a scenario of low mathematical proficiency among Brazilian students. In this context, this work arises as a result of an innovation built to enhance the Geometa application, developed by the Inteceleri company, through the integration of Metaverse and Artificial Intelligence technologies to create an immersive and interactive educational environment. The intention is to train Artificial Intelligence for real-time three-dimensional geometric shape recognition from real-world object images. The proposal aims to mitigate challenges faced in Brazilian basic Mathematics education by adopting innovative technological approaches aligned with Education 5.0, which can be replicated for similar technologies involving the Metaverse. Furthermore, it is also intended to create a dynamic and sustainable educational environment that not only facilitates the mathematical concepts understanding but also promotes active student participation, encouraging their creativity and autonomy in the learning process. The method used relies on the ObjectNet dataset image reclassification from objects to three-dimensional geometric shapes. The reclassified images are used to train CNN, MobileNet, ResNet, ResNeXt, ViT and BEiT Deep Learning models, which are subsequently evalua ted through Machine Learning, inference time and dimension performance measures. Thus, the best-performance Artificial Intelligence model is selected for future integration into Geometa. As contributions of this work, the following were accomplished: (i) the defined models were trained for the three-dimensional geometric shapes recognition; (ii) the models were evaluated through Machine Learning, inference time and dimension performance measures; and (iii) the best-performance model was selected considering the highest assertiveness and smoothness based on models performances analysis. Concerning the obtained results, the ResNet surpassed BEiT, which was the second better performance model, in 5% Precision and 5 Inference Per Second. Finally, the ResNet model reached 84% Precision and 9 Inferences Per Second, being observed as the best-performance Artificial Intelligence for Geometa application integration flow.