Navegando por Assunto "Redes neurais artificiais"

Agora exibindo 1 - 20 de 37

Acesso aberto (Open Access)
Algoritmos para seleção de metodologias de avaliação de softwares educacionais
(Universidade Federal do Pará, 2023-09-26) CASTILHO, Janize Monteiro de; FARIAS, Fabricio de Souza; http://lattes.cnpq.br/1521079293982268; https://orcid.org/0000-0003-4344-6953
In order to assist the teaching-learning processes, many teachers have decided to use Educational Software (ES) in their classrooms. However, to choose a ES as a teaching resource it is essential to endorse the methodology used by the teacher, once it needs to be pedagogically and functionally appropriate to meet the needs and objectives present in the classroom. Also, it is necessary to use mechanisms that the ES endorses to verify its adequacy to the professor’s objectives. Currently, it is verified that there are various techniques and methodologies available in the literature for ES assessment, but there is still no solution for decision making and selection of a ES that fully addresses the profiles of users and their different needs to be met by certain methodological application, or that arises from demand originating from the development of solutions based on demand and with a low capacity for generalization in terms of practical application. In this way, solutions are available without standardization and that several times do not take into consideration criteria relating to quality, measurement scales and verification procedures of the ES. This heterogeneity makes the evaluation of an ES very difficult, since the subjectivity in the selection of ES evaluation methodology can produce inconclusive results. Given this context, this work created a quality model that considers 24 ES assessment methodologies available in the literature and aims to automate the selection of ES assessment methodology based on the application of artificial intelligence (AI) algorithms, reducing the possibility of subjectivity in the screening process. During the investigation we used Natural Language Processing (NLP), Random Forest, k-Nearest Neighbors and Artificiais Neurais Networks. In all research scenarios, the natural language algorithm was combined with other algorithms, offering a solution based on the application of hybrid and loosely coupled AI algorithms, with excellent results. In this way, simulations were carried out considering NLP+Random Forest, NLP+k-Nearest Neighbors and NLP+Artificial Neurais Networks. After the simulations, the results indicate that it is possible to determine the best ES assessment methodology using AI algorithms, with the best results obtained with the combination of NLP+Random Forest.
Acesso aberto (Open Access)
Análise de tendências de variáveis hidroclimáticas na bacia hidrográfica Araguaia-Tocantins e suas implicações na agricultura irrigada
(Universidade Federal do Pará, 2019-02-28) SALAME, Camil Wadih; BARBOSA, Joaquim Carlos; SOUZA, Everaldo Barreiros de; http://lattes.cnpq.br/6257794694839685; https://orcid.org/0000-0001-6045-0984
The Araguaia-Tocantins Hydrographic Basin (BHAT) is the most extensive in drainage area within the Brazilian territory, with processes of use and occupation increasing in terms of the demands of agribusiness and mineral exploration. In this research, a statistical study was carried out on the hydroclimatic trends (precipitation and flow) in BHAT and its relations with irrigated agriculture. The hydroclimatic mapping based on cluster analysis identified four homogeneous regions within the BHAT, two to the north with a predominance of high rainfall/flow rates and less water availability. In the BHTA the rainy regime occurs between December to March and the dry regime between May and September. The months of October/November (dry to rainy) and April (rainy to dry) are transitional penods with pronounced variations in the seasonal cycle. The geostatistical study of rainfall/river flow forecast revealed that the results using the Box-Jenkings model are relatively better when compared to the Artificial Neural Networks model. The integrated approach of hydroclimatic variables with agricultural data within the BHTA revealed a significant pattern of negative trends in rainfall and flows that are spatially consistent in regions of intense productivity of com and soybeans and cattle. A relevant result was the detection of a significant spatial correlation between the number of central pivots (irrigation) in regions with low water availability, which favor the productivity of temporary crops.
Acesso aberto (Open Access)
Aplicação de sensores virtuais na estimação da concentração dos parâmetros físico-químicos e metais em corpos d’água de reservatórios de hidrelétricas: um estudo de caso na Região Amazônica
(Universidade Federal do Pará, 2014-10-23) RIBEIRO NETO, Benedito de Souza; OLIVEIRA, Terezinha Ferreira de; http://lattes.cnpq.br/6230804143692945; SILVEIRA, Antônio Morais da; http://lattes.cnpq.br/7549503749842625
This research introduces the use of virtual sensors to estimate the concentration of physico-chemical parameters and metals in monitoring water quality of reservoirs Amazon through artificial neural networks (ANN) and images of remote sensing. A factor analysis of the variables considered in the study confirmed the relationship of the first factor with Secchi disk, Total Iron, PO4, Total P, TSS and Turbidity on a single factor, as these have a high reflectance and good energy absorption by satellite sensors. These elements were determined by ANN's, producing satisfactory results approach 100% between observed and estimated. The tests resulted in a good approximation, the first band Secchi disk depth, total Fe, STS, and turbidity of the water reservoir. In the specific case of the parameters PO4 and Total P, besides the problem of the small number of sampling stations available data and the variability inherent in the hydrological cycle of the region, it was found, through the interpretation of images, lack of similarities between the data used in training and validation of RNA. Overall, the study demonstrated the effectiveness of the application of virtual sensors in monitoring water quality of reservoirs in the Amazon by satellite imagery, providing a precise and less expensive alternative resources in the process of environmental monitoring.
Acesso aberto (Open Access)
Avaliação de desempenho de técnicas de localização em ambientes reais aplicadas a redes de sensores sem fio
(Universidade Federal do Pará, 2014-05-26) MACHADO, Leomário Silva; MONTEIRO, Dionne Cavalcante; http://lattes.cnpq.br/4423219093583221; ARAÚJO, Josivaldo de Souza; http://lattes.cnpq.br/8158963767870649
The location of wireless sensor networks is a challenge that goes beyond the use of popular GPS through several studies that aim to improve it or even replace it. The location can be performed using multiple antennas and their respective angles, and time synchronization, time differential between transmission of two different or same power with a radio signal. From these patterns estimated, various techniques have been postulated in order to use the resources available to measure distances and estimate the coordinates of a node. Among these techniques may be cited as the most important Lateração, Nearest Neighbor, K-Nearest Neighbor, Min-Max, Non-Linear Regression, Linear Regression Non-Iterative, Sum-Dist, Dv-hop, Artificial Neural Network, filter Kalman . This paper conducts a series of tests conducted in two environments, the first indoor, outdoor and the second using the hardware as the MEMSIC IRIS modules to perform the experiment. These tests are compared Lateração, KNN and an Artificial Neural Network techniques is proposed for the purpose of estimating the location of a WSN node. Lateração mathematical formulations KNN and are presented as well as the configuration of the neural network used in the tests conducted in this work. The results are shown taking the benchmark for comparative analysis techniques to the percentage there between and better quantitative analysis, the data are tabulated for display accuracy.
Acesso aberto (Open Access)
Avaliação de modelos de inteligência artificial híbridos na estimativa de precipitações
(Universidade Federal do Pará, 2022-03-18) GOMES, Evanice Pinheiro; BLANCO, Claudio José Cavalcante; http://lattes.cnpq.br/8319326553139808
The hydrological analyzes carried out from rainfall in the Amazon are essential due to its importance in climate regulation, regional and global atmospheric circulation. However, in this region, there are limitations related to data series with short periods and many flaws, especially in the daily scale. Despite significant advances in science and technology, practical and accurate predictions have been a major concern due to their complexity. Therefore, several conceptual models, empirical or hybrid, have been tested to forecast rain with greater precision. Among empirical models, those that incorporate artificial intelligence (AI) methods are potentially useful approaches to simulate the precipitation process. Artificial Neural Networks (ANN), as AI models, are able to establish a relationship between historical inputs (rain, flow, etc.) and the desired outputs, through a non-linear function composed of several factors that are adjusted to the observed data, allowing your prediction. Thus, to improve the precipitation analysis, hybrid models were developed, involving Artificial Neural Network (ANN) of the type with Time Delay (TDNN), ELMAN network, Radial Base network (RBF) and Adaptive Neuro-Fuzzy Inference System (ANFIS), coupled with Maximum Overlap Discrete Wavelet (MODWT). Six rainfall gauge station were adopted, which are located in different biomes of the region, and satellite data (CMORPH). Rainfall data were evaluated by seasonal periods (rainy and dry). The results obtained demonstrated that the MODWT-ANFIS model had the best capacity to simulate the daily precipitation of the evaluated rainfall gauge station, even for dry periods, which are known to be more difficult to be simulated in relation to the rainy periods. In this case, data entries lagged by 4 days and 5 days performed better, with Nash values close to 1.0 and root mean square errors below 0.001.
Acesso aberto (Open Access)
Classificação de estratos florestais utilizando redes neurais artificiais e dados de sensoriamento remoto
(Instituto de Pesquisas Ambientais em Bacias Hidrográficas, 2016-09) GONÇALVES, Wanderson Gonçalves e; RIBEIRO, Hebe Morganne Campos; SÁ, José Alberto Silva de; MORALES, Gundisalvo Piratoba; FERREIRA FILHO, Hélio Raymundo; ALMEIDA, Arthur da Costa
This study classified forest types using neural network data from a forest inventory provided by the "Florestal e da Biodiversidade do Estado do Pará" (IDEFLOR-BIO), and Bands 3, 4 and 5 of TM from the Landsat satellite. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training neural networks belonging to the software tools package MATLAB(r) R2011b. The neural networks were trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and were evaluated in terms of the indicators confusion matrix, overall accuracy, the Kappa coefficient, and the receiver operating characteristics chart (ROC). The best result of classification was obtained by the probabilistic neural network of radial basis function (RBF) newpnn, with an overall accuracy of 88%, and a Kappa coefficient of 76%, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon that adopt agricultural systems with low carbon emissions.
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/4497607460894318
The 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.
Acesso aberto (Open Access)
Comparação entre regressão linear, redes neurais artificiais e árvores de regressão para quantificação do impacto harmônico de múltiplas cargas em redes elétricas de distribuição.
(Universidade Federal do Pará, 2018-11-19) PAIXÃO JÚNIOR, Ulisses Carvalho; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148
In recent years, the socio-economic development of the population, the growth of commercial and industrial sectors, as well as the ever-increasing installation of new electrical loads, have generated great evolution in demand of electricity consumption. In turn, to obtain more efficient systems, the manufacturers have produced equipment more energy efficient for residential, commercial and industrial use. However, these loads due their non-linearities, have contributed significantly to the increase in harmonic distortion levels of voltage and current, raising the concern of the power sector managers with respect to the power quality, mainly, due to the difficulty in the identification of the origin of the harmonic distortion. Therefore, to anticipate the harmonic effects and meet the current legislation, through computational techniques, this work emphasis is placed on the common coupling point (CCP) of consumers and utility, regardless of consumption characteristics and loads, to assess the harmonic impacts in his grid, besides comparing the reliability level of the techniques through the mean absolute error (MAE). The proposed methodology uses the Electrical Power Quality System (SISQEE) software that allows the use of three different computational techniques, such as Linear Regression, Artificial Neural Networks and Regression Trees, to evaluate the harmonic contribution of each feeder at the point of interest of the chosen electric grid. To prove the validity of the methodology, two case studies, based on real measurements at a university and at an industrial district, was carried out with a minimum sampling period of seven days using power quality analyzers, according to the distribution procedures by ANEEL (PRODIST). As a result of the power quality, it was verified how much each feeder impacts the voltage and current distortion at the CCP, besides classifying the feeders in relation to their respective impacts in the studied electrical grid. Also, as a result, the studies allowed the evaluation of performance between the different techniques, with different time intervals (weekly, daily and per load level), allowing to classify the behavior and reliability of each technique in each period. As a conclusion of the work, the proposed methods and analyzes presented allow managers to perform a more efficient mitigation action of the harmonic impacts caused in the electrical network and, also, to identify the differences between the techniques and their degree of reliability, in accordance with the time intervals studied.
Acesso aberto (Open Access)
Correlação de poços com múltiplos perfis através da rede neural multicamadas
(Universidade Federal do Pará, 2001-11-23) AMARAL, Mádio da Silva; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Stratigraphic correlation using well logs is a non-trivial geological activity and subject to endless possibilities of misunderstanding about the geometry or continuity of rock layers, for many reasons, like the geological variability and the ambiguous answers of the log tools. Thus, it is common to utilize a great log suite from the same well, for better comprehension. The stratigraphic correlation is a fundamental tool for a geologist or petroleum geophysist, because from its knowledge it is possible to interpret the hydraulic continuities of the reservoirs and to reconstruct the geological setting environment, which may corroborate for the construction of the reservoir geological model. This work produces an automation of manual activities involved in the stratigraphic correlation, with the use of the various well logs, and a convenient architecture of artificial neural network, trained with the backpropagation algorithm. The stratigraphic correlation, obtained from this method, makes the transport of the geological information possible along the basin and gives the interpreter, a general view of the structural behavior of the oil reservoir. With This methodology was possible the automatic construction of a geological block diagram showing the spatial disposition of a particular shale layer, from the well logs: Gamma Ray (GR), Clay Volume (Vsh), Density (ρb) and the Neutron Porosity (φn), selected in the five wells on the Maracaibo Lake basin, in Venezuela.
Acesso aberto (Open Access)
Desenvolvimento de softwares e algoritmo baseado em redes neurais artificiais para suporte à gestão da mobilidade urbana em smart campus com característica multimodal
(Universidade Federal do Pará, 2022-07-20) SÁ, Joiner dos Santos; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
This work presents the development of two software solutions and an algorithm based on artificial neural networks to support the management of urban mobility in a smart campus. The first software, called Norte Rotas, is a web solution whose objective is to support the planning of pedestrian routes, providing relevant information about the physical conditions of the routes of a smart campus. The second solution is an Android mobile software that aims to manage transport modes present in a smart campus. Tests in simulated and real environments were carried out and the results indicate that the proposed tools are good solutions for the planning and management of modal routes in an intelligent university campus. In addition to the software, a computational intelligence algorithm is proposed to determine the best travel route considering the options on foot, by bus and by boat in an IoT system of a smart campus. Data were collected from UFPA's Circular bus routes, and tests with different parameters of an ANN were performed. The results show that the solution based on ANN is promising to be implanted in urban mobility aid systems in a smart campus.
Acesso aberto (Open Access)
Determinação automática da porosidade e zoneamento de perfis através da rede neural artificial competitiva
(Universidade Federal do Pará, 2000) LIMA, Klédson Tomaso Pereira de; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Two of the most important activities of log interpretation, for the evaluation of hydrocarbon reservoirs are the log zonation and the effective porosity calculation of the rocks crossed by the well. The log zonation is the visual log interpretation for the identification, in depth, of the reservoir layers and its vertical limits, that is to say, it is the formal separation in reservoir rocks and non reservoir rocks (shales). The log zonation procedure is accomplished in a manual way, being been worth of the geologic and geophysical knowledge, and of the experience of the log analyst, in the visual evaluation of the curve patterns (log characteristics) corresponding to each specific rock type. The calculation of the effective porosity (porosity of the rock reservoir corrected by clay effects), combines a visual activity so much in the identification of the representative points of a reservoir rock in the log, as well as the adapted choice of the petrophysics equation, that relates the physical properties of the rock to the porosity. Starting from the knowledge of the porosity, the hydrocarbon volume will be established. This activity, essential for the reservoirs qualification, requests a lot of the knowledge and of the experience of the log analyst, for the effective porosity evaluation. An efficient form of automating these procedures and assistant the log analyst, in these activities, that particularly demand a great expenditure of time, is presented in this dissertation, in the form of a new log, derived of the traditional porosity logs, that presents the log zonation, highlighting the top and base depths of the occurrences of reservoir rocks, and non reservoir rocks, scaled in form of effective porosity, called here, as "zoning effective porosity log". The obtaining of the zoning effective porosity log, is based on the project and execution of several architectures of artificial neural feedforward networks, with not supervised training, and contends a layer of artificial competitive neurons. Projected in way to simulate the behavior of the log analyst, when he uses the neutron-density chart, for the situations of applicability of the shale-sandstone model. The applicability and limitations of this methodology will be appraised on real data, originated from of Lago Maracaibo's basin (Venezuela).
Acesso aberto (Open Access)
Estimação da porcentagem de flúor em alumina fluoretada proveniente de uma planta de tratamento de gases por meio de um sensor virtual neural
(Universidade Federal do Pará, 2011-06-22) SOUZA, Alan Marcel Fernandes de; OLIVEIRA, Roberto Célio Limão de; http://lattes.cnpq.br/4497607460894318; AFFONSO, Carolina de Mattos; http://lattes.cnpq.br/2228901515752720
The industries have been often seeking to reduce operating expenses, as to increase profits and competitiveness. To achieve this goal, it must take into account, among other factors, the design and implementation of new tools that accurately, efficiently and inexpensively allow access to information relevant to process. Soft sensors have been increasingly applied in industry. Since it offers flexibility, it can be adapted to make estimations of any measurement, thus a reducing in operating costs without compromising the measurements, and in some cases even improve the quality of generated information. Since they are completely softwarebased, they are not subjected to physical damage as the real sensors, and are better adaptated to harsh environments with hard access. The success of this king of sensors is due to the use of computational intelligence techniques, which have been widely used in the modeling of several nonlinear complex processes. This work aims to estimate the quality of alumina fluoride from a Gas Treatment Center (GTC), which is the result of gaseous adsorption on alumina virgin, using a soft sensor. The model that emulates the behavior of a alumina quality sensor the plant was created using an artificial intelligence technique known as Artificial Neural Network. The motivations of this work are: perform virtual simulations without compromising the GTC and make accurate decisions based not only on the operator's experience, to diagnose potential problems before they can interfere with the quality of alumina fluoride; maintain the aluminum reduction pot control variables within normal limits, since the production from low quality alumina strongly affects the reaction of breaking the molecule that contains this metal. The benefits this project brings include: increasing the GTC efficiency, producing high quality fluoridated alumina and emitting fewer greenhouse gases into the atmosphere and increasing the pot lifespan.
Acesso aberto (Open Access)
Estimação das parcelas de contribuição de cargas não lineares na distorção harmônica de tensão de um barramento de interesse do sistema elétrico de potência utilizando rede neural artificial
(Universidade Federal do Pará, 2019-09-06) MANITO, Allan Rodrigo Arrifano; TOSTES, Maria Emília de Lima; http://lattes.cnpq.br/4197618044519148; BEZERRA, Ubiratan Holanda; http://lattes.cnpq.br/6542769654042813
This work presents a methodology to estimate the non-linear loads contribution on voltage harmonic distortion at a bus of interest in the electric power system. The estimation process is carried out through the development of a model based on artificial neural networks (ANN) added to a sensitivity analysis in neural network input. The ANN model input is constituted by the non-linear loads harmonic currents considered in the studied system, and the ANN output corresponds to the harmonic voltage values in the bus under study, for the same harmonic frequency. The study is carried out for each harmonic order individually and the data required for the construction of the model as well as for the results validation have been obtained from synchronized measurement campaigns and by computational simulation, using harmonic load flow studies. Comparisons between reference results through computational simulation with the results obtained by neural model are carried out and it is observed that the developed methodology is able to classify correctly the impact of non-linear loads in the voltage distortion at a bus of interest of the electric system. Additionally, the effectiveness of the methodology is tested in two real systems in order to verify the good performance of this methodology considering real data obtained during measurement campaigns.
Acesso aberto (Open Access)
Estimativa dos perfis de permeabilidade e de porosidade utilizando rede neural artificial
(Universidade Federal do Pará, 2002-11-05) GOMES, Laércio Gouvêa; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
The permeability and the porosity are the two most important petrophysical properties for qualification of oil and gas reservoirs. The porosity is related to the capacity of fluids storage and the permeability, with the production of these fluids. The estimates of the permeability and porosity are of fundamental importance for reservoir engineers and geophysics, once its values can define the completacion or not of an oil well. Its measures are, usually, accomplished in laboratory, through cores of the rock. The porosity log and its relationship with the density log, is very well-known in the well logging, however, it just exist a few qualitative relationships (Kozeny's relation, for instance) between the porosity and the permeability. This work search the establishment of the permeability log and of the porosity log, starting from information of the density log. For so much, we looked for the relationship among the physical property of the rock (density) and the petrophysical properties: permeability and porosity, using as methodology the technique of artificial neural networks with radial base function. To obtaining the permeability and the porosity, the artificial neural network possessing as input the information of the density that facilitates a smaller cost for the acquisition of those important petrophysical information, giving possibility to the well log analysts, to opt or not for the exploration of a studied unit, in addition, it facilitates a more complete vision of the reservoir. The procedures for the estimate of the permeability and of the porosity are addressed for an only formation, but the log interpreters can apply the guideline presented in the program of artificial neural network with radial base function, using the estimate of those properties for another formations, besides of another oil fields. Therefore, is recommended the use of a large data set of the same well in order to make possible the best interpretation.
Acesso aberto (Open Access)
Extração de conhecimento em forma de regras difusas a partir de mapas auto-organizáveis de Kohonen: aplicação em diagnóstico de faltas incipientes em transformadores
(Universidade Federal do Pará, 2013-03-11) SILVA, Ana Carla Macedo da; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Despite the many advantages offered by the artificial neural networks, some limitations still prevent their widespread use, especially in applications that require making decisions essential to ensure safety in environments such as in Power Systems. A major limitation of artificial neural networks with respect to the inability of these networks is to explain how to arrive at certain decisions. This explanation must be humanly understandable. Thus, this paper proposes a method for extracting fuzzy rules from Kohonen self-organizing map, designing a fuzzy inference system capable of explaining the decisions taken by the map. To verify its effectiveness, the method is applied to solve the problem of classification for the diagnosis of incipient faults in power transformers used.
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)
Handoff de espectro em redes baseadas em rádio cognitivo utilizando redes neurais artificiais
(Universidade Federal do Pará, 2011-02-03) BALIEIRO, Andson Marreiros; COSTA JÚNIOR, Carlos Tavares da; http://lattes.cnpq.br/6328549183075122
Nowadays, the radio spectrum is one of the most important natural resources in the world. According to the FCC (Federal Communications Commission) report, the licensed bands are abundant but poorly utilized. The Cognitive Radio (CR) technology aims improving the spectral efficiency through the opportunistic access to the electromagnetic spectrum. It enables that new applications based on wireless communications are supported, without causing interference at the licensed communication and trying to ensure the quality of service to the applications. Thus, the efficient spectrum handoff is a critical requirement to be taken into account for the success of CR systems. This work outlines concepts about the cognitive radio technology. It proposes and evaluates a proactive strategy based on Artificial Neural Networks for spectrum handoff in CR networks. The performance of the proposal regarding the inference level to the primary user, spectrum handoff number triggered by secondary user and spectral utilization is compared with that one obtained by a reactive scheme. Differently from previous works, this study considers measured traces made available by IEEE Dyspan 2008 in order to evaluate our proposal. Numerical results show the superiority of the proposed scheme.
Acesso aberto (Open Access)
Identificação automática das primeiras quebras em traços sísmicos por meio de uma rede neural direta
(Universidade Federal do Pará, 2000) MIRANDA, Anna Ilcéa Fischetti; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926; CRUZ, João Carlos Ribeiro; http://lattes.cnpq.br/8498743497664023
In spite of the technologic development happened at seismic prospection, and the significative amount of data with seismic two-dimensional (2D) and three-dimensional (3D) surveys, some process in the seismic interpretation task like the first break picking, remains in a manual version, that still needs an intuitive human intervention. This dissertation purpose, fill in the seismic processing with the intention to look for an efficient method to enable the computational simulation of the human visual system behavior, through decision process automation involved in first break picking in a seismic trace; looking at to preserv the interpreter intuitive knowledgement to more complex tasks, where your knowledgement will be better profitable. Neural networks, the most important implementation of neurocomputing systems, were initially developed by neurobiologists as computer models of the neural system in the brain. They differ from conventional computation techniques in their ability to adaptively discriminate or learn through repeated exposure to examples, their tolerance to data component failure and their robustness in the presence of high noise levels. This computing technology provide some techniques that can reduce the labor intensive aspects of the first break picking, maintaining the quality and reliability of the results. The method here presented is an application of an artificial neural network computational process, known as feedforward multilayer perceptron trained with the error back-propagation algorithm; from the establishment of a convenient neural network architecture and learning set that make possible its application over seismic data. This method is a computational simulation of seismic interpreter decision intuitive process for first break picking in seismic traces. The applicability, efficiency and limitations of this approach will be appraised in synthetic data obtained starting out the ray theoretical method.
Acesso aberto (Open Access)
Imageamento da porosidade através de perfis geofísicos de poço
(Universidade Federal do Pará, 2004-01-27) MIRANDA, Anna Ilcéa Fischetti; ANDRADE, André José Neves; http://lattes.cnpq.br/8388930487104926
Porosity images are graphical representations of the lateral distribution of rock porosity estimated from well log data. We present a methodology to produce this geological image entirely independent of interpreter intervention, with an interpretative algorithm approach, which is based on two types of artificial neural networks. The first is based on neural competitive layer and is constructed to perform an automatic interpretation of the classical Pb - ΦN cross-plot, which produces the log zonation and porosity estimation. The second is a feed-forward neural network with radial basis function designed to perform a spatial data integration, which can be divided in two steps. The first refers to well log correlation and the second produces the estimation of lateral porosity distribution. This methodology should aid the interpreter in defining the reservoir geological model, and, perhaps more importantly, it should help him to efficiently develop strategies for oil or gas field development. The results or porosity images are very similar to conventional geological cross-sections, especially in a depositional setting dominated by clastics, where a color map scaled in porosity units illustrates the porosity distribution and the geometric disposition of geological layers along the section. The methodology is applied over actual well log data from the Lagunillas Formation, in the Lake Maracaibo basin, located in western Venezuela.
Acesso aberto (Open Access)
Implementação de modelos computacionais na predição temporal e espaço-temporal de parâmetros de qualidade de água
(Universidade Federal do Pará, 2021-12-14) ALMEIDA, Anderson Francisco de Sousa; MERLIN, Bruno; http://lattes.cnpq.br/7336467549495208; HTTPS://ORCID.ORG/0000-0001-7327-9960; GONZÁLEZ, Marcos Tulio Amaris; http://lattes.cnpq.br/9970287865377659
The quality of water is directy related to is level of pollution caused by anthropic and industrial actions, with a consequent reduction in the availability of quality water. Therefore, limological monitorig of the basic parameters os water quality is carried out, as away of obtaining data that guide the decision-making of water resouces management bodies. In this context, the present study has the implementation of machine learning algorithms to predict temporal and spatiotemporal water quality parameter data. The ML techniques used were linear regression, ramdom forest, MLP and LSTM neural networks. Two collection points from a Water Resources Management Unit in São Paulo, Brazil were used. Models are evaluated using MAPE( mean absolute percentage eror) and RMSE( root mean squared erro) metrics. Therefore, in temporal prediction, the LSTM technique presented the best performace in relation to the other techniques and the data used, as it has the lowest average RMSE result, with 2.47. However, in spatiotemporal prediction, MLP has the best performace both in relation to the other techniques and the data used , as it has the lowest averagee results of MAPE and RMSE, respectively, 5.94% and 1.34. Thus, these performaces of neural networks can be justified by the non-linearity of the parameter data. Other than that, the results of the experiments aim to contribute to the water quality monitorng process and assist in the planning of water management, so that it meets current legislation and enales the indication of public policies, through machine learning models in prediction of water quality parametes.