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 - 6 de 6

Acesso aberto (Open Access)
Designing cost-efficient transport solutions for fixed and mobile broadband access network
(Universidade Federal do Pará, 2016-03-03) FARIAS, Fabrício de Souza; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
This thesis undertakes a techno-economic evaluation of transport solutions for fixed and mobile broadband access. In the case of future mobile access networks, it is proposed to make use of backhaul architectures using fiber and microwave applied to Greenfield deployments and a copper-legacy backhaul infrastructure based on Brownfield migration, i.e. finding a way of using a legacy infrastructure to its full capacity. At the same time, protection deployments based on fiber-wireless schemes are recommended for future fixed broadband. The main contribution made by this thesis is to carry out a research investigation into the total investment cost of the broadband transport infrastructure. This will be determined by employing two sets of models to assess the capital and operational expenditures, (CAPEX and OPEX respectively), of mobile and fixed broadband access network operators. First, this involves a set of models for mobile broadband that are summarized in a general methodology that aims at providing: traffic forecasting, wireless deployment, mobile backhaul deployment and total cost assessment. It was found that, fiber-based backhaul through a Greenfield deployment is the most energy-efficient option. Furthermore, Brownfield reveals that copper-based backhaul can still play a key role if used up to its full capacity and sharply reduces the investment costs in infrastructure. Additionally, there is an examination of the main differences in cost and energy values between Greenfield and Brownfield. Finally, a methodology is employed for fixed broadband based on network dimensioning, failure costs and an assessment of the total cost of ownership. The models are used to assess five architectures that represent different protection schemes for fixed broadband. This research shows the economic benefits of using a hybrid protection scheme based on fiber-wireless architecture rather than fiber-based protection options and a sensitivity analysis is conducted to show that the extra CAPEX invested to protect the infrastructure might be recovered through the OPEX after a number of years. The results obtained in the thesis should be useful for network operators to plan both their fixed and mobile broadband access network infrastructure in the future.
Acesso aberto (Open Access)
Estratégia para análise da concentração de infraestrutura de acesso às tecnologias da informação e comunicação nos municípios da Amazônia legal Brasileira
(Universidade Federal do Pará, 2016-03-31) BRITO, Silvana Rossy de; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
Access to information is a fundamental right in democratic societies, essential for economic development, social equality, improvement of social, health care, education services and governance. This study is situated on the Concentration of Access to Information and Communication Technologies (ICT) in the municipalities of the Brazilian Legal Amazon. For this, we developed the conceptual model from which we identify the main variables of ICT infrastructure in urban and rural households. Our strategy is to (i) analyze the ICT infrastructure concentration in Brazilian municipalities; (ii) analyze the spatial distribution of ICT infrastructure concentration in urban and rural households in Brazilian municipalities; and (iii) search for associations between parameters describing the ownership of ICT resources in urban and rural households with indicators for income, education, population size, and the existence of electricity in municipalities. To understand these associations at the municipality level, we use Bayesian Networks to reveal dependencies among the variables studied. The results of applying the proposed strategy show that for urban households, the average concentration in the municipalities of the Amazon for computers and Internet access and for fixed phones is lower than in other regions of the country; meanwhile, that for no access and mobile phones is higher than in any other region. For rural households, the average concentration in the municipalities of the Amazon for computers and Internet access, mobile phones, and fixed phones is lower than in any other region of the country; meanwhile, that for no access is higher than in any other region. The study shows that education, income, and population size are determinants of inequality in accessing ICT in Brazilian households. In addition, we advance to analyze the association between teenage motherhood and the representative variables of income, education, and computer and Internet access in the municipalities of the Brazilian Legal Amazon compared to other regions of the country. These networks pointed to the region as the influential variable on the high percentages of teenage mothers and households with computer and Internet access.
Acesso aberto (Open Access)
Estratégia para predição de consumo de energia elétrica de curto prazo: uma abordagem baseada em densificação com MEAN SHIFT para tratamento de dias especiais
(Universidade Federal do Pará, 2016-11-04) RÊGO, Liviane Ponte; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
The use of short-term prediction strategies is an important tool for planning and operation of electrical systems, playing a crucial part in aiding the decision support process for buying and selling of electricity in the future market. For the energy market, in particular, an important component to take into account for consumption forecasting are the special days (holidays or atypical days, for example). Given its unusual behavior, the estimation of such events can be a complex task, when compared to the forecasting of ordinary days. In addition, as they are often found with only a small number of samples, it is difficult to adequately train and validate prediction algorithms. To tackle these problems, this work presents a model for short-term load forecasting using the Information Theoretic Learning Mean-Shift model to clustering and densify the sample size of special days's events on a time series, there on followed by the prediction using statistical and/or machine learning algorithms; in this work represented by artificial neural network algorithms and multiple Linear regression. The model was applied in a load forecasting problem for the electric utility in the northern region of Brazil, providing an improvement in the accuracy of results.
Acesso aberto (Open Access)
Imitação da voz humana através do processo de análise-por-síntese utilizando algoritmo genético e sintetizador de voz por formantes
(Universidade Federal do Pará, 2015-12-18) ARAÚJO, Fabiola Pantoja Oliveira; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
Voice imitation through the utterance copy mechanism is estimating the value of the input parameters of a speech synthesizer to generate a similar signal with the original voice. This process is distinct from the more traditional text-to-speech, but yet used in many areas, especially, Linguistics and Health System. Imitate the human voice through this mechanism is a difficult inverse problem because the mapping is non-linear and from many to one. For instance, there are different combinations of the synthesizer input parameters values that produce the same synthetic voice signal. Therefore, perform voice imitation manually requires a considerable amount of time. In addition to automatic methods are our interest of study as well, as proposed here. This work presents our system based on Genetic Algorithm (GA) to automatically estimate the value of the input parameters of a speech formant synthesizer using the analysis-by-synthesis process. Results are presented for synthetic (computer-generated) and natural (human-generated) speech in American English, for male and female speakers. These results are compared with the ones obtained with Winsnoori, the only currently available software that performs the same task. The experiments showed that the proposed newGASpeech framework is an effective alternative to the laborious manual process of estimating the input parameters values of a formant synthesizer. Besides it has overcome the quality of the generated voices by the baseline if compared to five objective metrics and a subjective evaluation applied to twenty seven no-expert listeners in the speech area neither the adopted language.
Acesso aberto (Open Access)
Uma metodologia biologicamente inspirada para projeto automático de redes neurais artificiais usando Sistemas-L paramétricos com memória
(Universidade Federal do Pará, 2016-08-26) CAMPOS, Lidio Mauro Lima de; ROISENBERG, Mauro; http://lattes.cnpq.br/5872119613051645; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
This thesis proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established stateof- the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro®). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decisionmaking process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems.
Acesso aberto (Open Access)
Uma nova solução para a otimização do despacho econômico e ambiental utilizando metaheurísticas da computação bio-inspirada
(Universidade Federal do Pará, 2016) NASCIMENTO, Manoel Henrique Reis; NUNES, Marcus Vinícius Alves; http://lattes.cnpq.br/9533143193581447
Due to the significant industrial growth in the North of Brazil, especially at the Industrial Pole of Manaus (PIM), it has been an increased necessity for energy generation, which in this region is provided by thermoelectric plants (UTEs) in over 90% of its total. Thus, it became necessary the use of computational tools that help the specialists or operators of electrical systems, for making decisions about the optimal power dispatch of each generating unit that contemplate not only to reduce costs but also reduce the atmospheric pollution levels. Optimization of Economic Dispatch (ED) is one of the oldest and most important tasks in power plant management, and currently, due to growing concerns about the environment, this problem is extended to the optimization of the Economic and Environmental Dispatch (EAD). This thesis has as main objective to analyze a new proposal to solve the old optimization problem of ED and the EAD implemented by several Deterministic methods (Iteration Lambda, Quadratic Programming and Newton method) and Heuristic methods (Genetic Algorithms, Particle Swarm, Differential evolution, Simulated Annealing, Optimization by Grey Wolf and Artificial Bee Colonies) for the ED problem. Non-dominated Sorting Genetic Algorithms (NSGA II and NSGA III), were used for evaluating the problem of EAD, considering the shutdown of the generators with higher losses and thus reducing the fuel cost. The method of incremental cost and transmission losses are used to determine the best active power values for each generating unit. It was ensured the energy balance between the total generated power, the demand of the electrical system, losses and minimizing, on the other hand, the total cost of fuel, reducing emissions, and further improving efficiency not only for generators but also to UTE as a whole. Consequently, the proposed new solution has the following contributions: contemplates the turning off generation systems that have higher fuel cost, reducing the overall costs and enabling predictive maintenance on these machines. This approach also determines optimal solutions for the power output in various scenarios characteristic and not characteristic of UTEs or power plants, considering changes in active power generation and reducing greenhouse gas emissions as NOx and CO2. To explore the feasibility of the new solution proposed by this theory, it was used as a test system a set of ten (10) generating units for the case study and three sets of generators´ parameters described in the literature. They were used for demonstrating the robustness of the proposed solution considering the use of various deterministic and Bioinspired computing methods for mono-objective and multi-objective optimization. The results presented here, from an analysis of several practical examples show the advantages of the new proposed solution.

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