Navegando por CNPq "CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO"

Agora exibindo 1 - 11 de 11

Acesso aberto (Open Access)
Algoritmos culturais com abordagem memética e multipopulacional aplicados a problemas de otimização
(Universidade Federal do Pará, 2012-04-20) SILVA, Deam James Azevedo da; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318
In many optimization problems is hard to reach a good result or a result close to the optimum value in a feasible time, especially when working on large scale. So, many of these problems are addressed by heuristics or metaheuristics running search for better solutions within the defined search space. Within the natural computing algorithms there are the cultural and genetic algorithms. These are evolutionary metaheuristics complement each other due to the dual mechanism of cultural heritage/genetic. The purpose of this paper is to study and use such mechanisms adding local search heuristics and multipopulation applied to combinatorial optimization problems (knapsack and travel salesman problems), constrained problems and multimodal functions. Some experiments have been conducted to assess the performance of the proposed combination of meta-heuristisc and heuristics mechanisms against approaches found in literature as applied to problem addressed here.
Acesso aberto (Open Access)
Clusterização de padrões espaço-temporais de precipitação na Amazônia via deep convolutional autoencoder
(Universidade Federal do Pará, 2023-07-07) SILVA, Vander Augusto Oliveira da; TEIXEIRA, Raphael Barros; http://lattes.cnpq.br/4902824086591521; https://orcid.org/0000-0003-2993-802X
Studies using different machine learning methods for knowledge discovery and pattern recognition in precipitation time series are increasingly frequent in the literature. Identify and analyze patterns in precipitation time series in a particular region is fundamental for its socioeconomic development. Therefore, it can be stated that knowledge and understanding of the rainfall characteristics of the regions are important to enable the planning of the use, management and conservation of water resources. The natural phenomenon of precipitation is a fundamental process with a direct impact on watersheds and on human and environmental development. The variability of this phenomenon has important implications for the navigability of rivers, individual abundance and species richness. In recent years, many studies with this approach have been carried out in Brazil, mainly in the Amazon region. This research aimed to develop a computational method for analyzing time series of precipitation using machine learning techniques with unsupervised learning, in order to propose an method capable of extracting complex features from the data, obtaining a map of attributes at low dimensionality for pattern recognition, discovery of homogeneous regions with respect to precipitation and approximate reconstruction of precipitation time series in the Legal Amazon. The proposed deep learning neural network model is trained to learn the main and most complex features of the original data and present them in low dimensionality in latent space. After the training, the results are promising, the observations of the reconstructed data showed a good performance as evaluated by the RMSE and NRMSE metric with resulting values equal to 0.06610 and 0.3355 respectively. The analysis of the representation of the data in low dimension was applied and analyzed by a clustering structure using hierarchical agglomerative with Ward’s method. This methodology also showed good results, as it carried out consistent groupings characterizing ho- mogeneous regions in relation to precipitation data. Thus, demonstrating that the representation in low dimensionality carried the main characteristics of the time series of the analyzed data. It is noteworthy that the method developed in this study can be applied not only in the Amazon region, but also in other areas with similar challenges related to time series analysis.
Acesso aberto (Open Access)
Estratégia de otimização para a melhoria da interpretabilidade de redes bayesianas: aplicações em sistemas elétricos de potência
(Universidade Federal do Pará, 2009-12-10) ROCHA, Cláudio Alex Jorge da; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The study of methods, techniques and tools that can aid the decision processes in power systems, in its many sections, is a subject of great interest. This decision support can be accomplished through many different techniques, particularly those based on computational intelligence, given their applicability on domains with uncertainty. In this proposal, Bayesian networks are used for the extraction of knowledge models from the available data on power systems. Moreover, given the demands of these systems and some limitations imposed to the inferences in Bayesian networks, a method is proposed, using genetic algorithms, capable of extending the power of comprehensibility of the patterns discovered; it aims at finding the optimal scenario in order to attain a given target, considering the incorporation of a priori knowledge from domain specialists, identifying the most influent variables in the domain for the maximization of the target variable.
Acesso aberto (Open Access)
Experimentos de mineração de dados aplicados a sistemas scada de usinas hidrelétricas
(Universidade Federal do Pará, 2012-04-13) OHANA, Ivaldo; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
The current model of the Brazilian electric sector allows equal terms to all actors and reduces the role of the State in this sector. This model forces the electrical utilities to improve the quality of their products and, as a prerequisite for this purpose, they should make more effective use of the enormous amount of operational data that are stored in databases, acquired from the operation of their electrical systems which use the hydroelectric power plants as their main source of energy generation. One of the main tools for managing the operation of these plants are the Supervisory Control and Data Acquisition systems (SCADA). Thus, the large amount of data stored in databases by SCADA systems, certainly containing relevant information, should be treated to discover relationships and patterns that would help in the understanding of many important operational aspects as well as in the evaluation of operational performance of the electric power systems. The process of Knowledge Discovery in Database (KDD) is the process of identification of patterns in large data sets, that are valid, new, and useful to improve the understanding of a problem or a decision-making procedure. Data Mining is the step within KDD that extracts useful information from large databases. In this scenario, the present study objective is to perform data mining experiments on data generated by power plants SCADA systems, to produce relevant information to assist in planning, operation, maintenance and security of hydro power plants and also contribute to the implementation of the culture of using data mining techniques applied to these plants.
Acesso aberto (Open Access)
Um framework para a previsão de cenários com o uso de sistemas híbridos neurogenéticos para compra e venda de energia elétrica no mercado futuro
(Universidade Federal do Pará, 2012-05-04) CONDE, Guilherme Augusto Barros; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
In the context of time series forecasting, is great the interest in studies of forecasting methods of time series that can identify existing structures and patterns in historical data, allowing generate the next patterns of the series. The proposal defended in this thesis is the development of a framework that uses the full potential of forecasting techniques (neural networks) with the optimization techniques (genetic algorithms) in a hybrid system that well enjoy the advantages of each of these techniques to the generation of future scenarios that can show, in aaddition to normal forecasts based on historical values, alternative pathways of the curves of time series analyzed.
Acesso aberto (Open Access)
ICM Space Game: uma interface baseada na imaginação de movimentos
(Universidade Federal do Pará, 2023-03-10) CALVINHO, Jhoanyn Valois Fantin; MERLIN, Bruno; http://lattes.cnpq.br/7336467549495208; HTTPS://ORCID.ORG/0000-0001-7327-9960; SILVA, Cleison Daniel; http://lattes.cnpq.br/1445401605385329; https://orcid.org/0000-0001-8280-2928
Brain-Machine Interfaces can help users participate in routine tasks, such as moving around. The scientific community works daily in an attempt to offer increasingly robust Brain-Machine Interface systems, with better responses to user commands. However, these works usually focus on improving the system itself. Therefore, the objective of this work is to offer an alternative to the users to help in the learning of the use of equipment of a Brain-Machine Interface based on the imagination of movements. For this, a computational tool based on a virtual game is developed in an attempt to improve the accuracy of users in controlling the devices of these systems. The results show that the tool works when connected to a Brain-Machine Interface, and can serve as an alternative in the process of collecting EEG signals. Throughout this work, programming languages dedicated to ICMs, such as OpenVibe, are used, as well as a language widely used in the programming of electronic games, Python. In the experiment carried out with 8 volunteers, there is no discrepant difference between the classification rates performed with the aid of the conventional protocol and the ICM Space Game, approximately 56% for both
Acesso aberto (Open Access)
Métricas de QoE/QoS de vídeo em redes sem fio para auxilio ao planejamento de ambientes indoor utilizando uma abordagem bayesiana
(Universidade Federal do Pará, 2015-03-30) CARVALHO, André Augusto Pacheco de; CAVALCANTE, Gervásio Protásio dos Santos; http://lattes.cnpq.br/2265948982068382
The evolution of applications on wireless networks has grown in recent years, due to the increased number of smartphone users, tablets and others. The availability of demanding services such as video transmission, affects Quality Experience (QoE) and Quality of Service (QoS) provided to domestic users and trade, this had stimulated the study of new resource management techniques networks, aiming to provide quality services to a customer each increasingly demanding. This thesis presents a methodology Intelligence Artificial using a Bayesian network with a hybrid evaluation strategy analyzing the behavior metrics QoE and QoS in the LAN network design wireless. The diversity of the place of Measurements chosen compound materials such as brick, glass, wood and concrete. It was necessary first to map all the points to be measured before and after deliberately placing each barrier outdated the signal. Metrics as level Receiver Signal Strength Intensity signal (RSSI) Jitter, delay end to end network for the video transmission, PeakSignal-to-NoiseRatio (PSNR) and Structural Similarity (SSIM) were collected during the Measurements. And using the Bayesian Network inferences were made for each metric and could not find satisfactory results for the proposed solution assist the wireless network planning in indoor environments. Enabling demonstrate that up to 10 meters away from the transmitter, the signal has its best power, and delay metrics in order to have more than 65% probability that the lower delay range and following this optimum performance the Jitter has more than 65% probability in this lower range. And the QE metrics, PSRN and SSIM have a similar behavior and has more than 80% probability of getting your greater value, and consequently the video has its best reception. These results show that does not preclude the use of this proposal in other situations.
Acesso aberto (Open Access)
Predição de falhas em redes de grades OBS com plano de controle GMPLS
(Universidade Federal do Pará, 2013-01-09) BECHARA, Mariana Castro; CERQUEIRA, Eduardo Coelho; http://lattes.cnpq.br/1028151705135221
This paper presents a proposal for predict failures in OBS grid network with GMPLS to assist applications in collaborative environments, like E-Science. Agents monitoring traffic (DQMA-Fuzzy) for related QoS parameters and others related to imperfections in optical links. A system based on fuzzy logic has been developed to give more robustness and flexibility in decision-making agents, because it presents a solution faster and easily implementable. NS-2 (Network Simulator – 2) simulations show that the proposed DQMA-Fuzzy is able to minimize blockages and balancing the use of grid resources, ensuring well-defined service levels, assisting in traffic engineering and fault prediction.
Acesso aberto (Open Access)
Predição de qualidade de experiência para internet do futuro em arquiteturas heterogêneas de redes sem fio móveis
(Universidade Federal do Pará, 2013-12-06) FERREIRA JUNIOR, José Jailton Henrique; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The Next Generation Networks are architectures that allow vertical handovers and the user-centric vision by the appropriate Quality of Experience (QoE) provisioning for multimedia applications. The discussion is much less protocol-oriented perspective and is more service-oriented perspective. This thesis presents an architecture for next-generation networks to provide wireless heterogeneous access and seamless vertical handover for multimedia applications. The proposal considers different wireless technologies and also adopts the standard IEEE 802.21 (Media Independent Handover – MIH) to assist in the integration and the mobility management of heterogeneous wireless networks. The technologies in architecture are: IEEE 802.11 (popular known as Wi-Fi), IEEE 802.16 (popular known as WiMAX) and LTE (popular known as Fourth Generation – 4G). The objective is to choose the best connection for user. The proposal architecture presents mechanisms to predict quality of experience that will be decisive parameter to do or do not the handover, the prediction will be using artificial intelligence neural networks, in addition to architecture also provides a mechanism for QoE discard packets to specific multimedia applications. The proposal is evaluated by simulation using the ns-2 (Network Simulator) and the performance results are presented through the QoS/QoE metrics and also visually by displaying the video frames transmitted in architecture.
Acesso aberto (Open Access)
Proposta de arquitetura para o estabelecimento dinâmico de conexões determinísticas e busca de recursos multidomínio em redes grid OBS baseadas no GMPLS
(Universidade Federal do Pará, 2011-06-17) NUNES, Anderson Marques; CERQUEIRA, Eduardo Coelho; http://lattes.cnpq.br/1028151705135221
The processing capacity of research institutions has been growing significantly as processors and workstations increasingly powerful emerging in the market. Considering the improvement of performance in the area computer networks and seeking to fill the demand for ever greater processing, the idea to use networked computers as independent platform for running parallel applications, thus creating the area grid computing. In a network that is under the same administrative domain, is common for there to share resources such as disks, printers, etc. But when the network goes beyond an administrative domain, this sharing becomes very limited. The purpose of computing grids is to allow sharing resources even if they are scattered in different areas administration. This thesis proposes an architecture for dynamic establishment of connections multidomain which makes use of optical burst switching (OBS) using a GMPLS control plane (Generalized Multiprotocol Label Switching). The architecture is based on storing information about grid resources of autonomous systems in a separate component called GOBS Root Server (Grid OBS) and the use of explicit routing to reserve resources along a route that satisfies the performance constraints of an application. The validation of the proposal is made through simulations that show that the architecture is capable of levels of performance differed according to the application class and provides a better utilization of network resources and computing.
Acesso aberto (Open Access)
Reconstrução e modelagem in silico da via de biossíntese de ácidos graxos da bactéria psicotrófica Exiguobacterium antarticum linhagem B7
(Universidade Federal do Pará, 2016-04-04) FRANCÊS, Regiane Silva Kawasaki; SCHNEIDER, Maria Paula Cruz; http://lattes.cnpq.br/3901112943859155
Mathematical modeling in silico based restrictions is an approach adopted by systems biology to analyze metabolic networks. The Gram-positive bacterium Exiguobacterium antarticum B7 is an extremophile organism able to survive in cold environments as glacial ice and permafrost. The ability of these microorganisms of adaptation to cold attracts great biotechnological interest. An important factor for the understanding of cold adaptation process is related to the chemical modification of fatty acids constituting the cell membrane of psicotrophic bacteria in order to maintain membrane fluidity to avoid freezing ofthe bacteria. In this work, the metabolic pathway of fatty acid biosynthesis of the bacterium E. antarticum B7 was rebuilt from its annotated genome. The software tools KEGG (Kyoto Encyclopedia of Genes and Genomes) and RAST (The Rapid Annotation Server) were used to generate a preliminary network model. The next step was to cure manually the genomic, biochemical and physiological informations available in different databases and specific literature. During this process, the FabZ and DesK enzymes responsible for adding carbon-carbon unsaturations in the fatty acid chain during synthesis have been identified in the genome, though in a truncated form. The fluxome metabolic pathway was defined, describing the routes of the main reactions since the first monomer, Acetyl-CoA, to the final product, the Hexadecenoic acid. A computational modeling was done using the software MATLAB® with toolboxes and specific tools for systems biology. The quantification of metabolites produced via was performed by the method constraint-based Flux Balance Analysis (FBA). To evaluate the influence of the gene expression in the fluxome analysis, the FBA method was also calculated using the log2FC values obtained in the transcriptome analysis at 0ºC and 37ºC. The fatty acid biosynthesis pathway showed a total of 13 elementary flux modes, four of which showed routes for the production of hexadecenoic acid. The reconstructed pathway demonstrated the capacity of E. antarcticum B7 to produce fatty acid molecules. Under the influence of the transcriptome, the fluxome was altered, promoting the production of short-chain fatty acids. The calculated models contributes to better understand the bacterial adaptation at cold environments.