Navegando por Assunto "Redes neurais auto-associativas"

Agora exibindo 1 - 5 de 5

Acesso aberto (Open Access)
Estrutura competitiva de redes neurais autoassociativas para classificação de fadiga mental através de sinais de eletroencefalografia
(Universidade Federal do Pará, 2018-12-21) FERREIRA, Mylena Nazaré Medeiros dos Reis; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
The complexity of mental fatigue signals in healthy people is due to the absence of specific perturbations in the electroencephalographic activity, and by the singularity and variability of the cognitive profile of each individual. Identifying this mental state requires the analysis of several factors that involve the brain behavior in its regions in various frequency bands. In concern to the industry, mental fatigue compromises the efficiency of the production chain by affecting the perception (concentration and attention) of people, which increases the risk of accidents and production costs. Thus, monitoring the cognitive condition is necessary for the maintenance of the productive and cognitive performance of the evaluated subject. This work proposes the classification of fatigue using a competitive structure of Associative Neural Networks. This type of neural network allows to find the association between the input data and the reconstructed data from a compact architecture, being indicated for real-time applications. The characteristics vector used for classification is composed of the normalized information of three frequency bands (theta, beta and alpha) and four metrics that, according to the literature, differentiate mental states from electroencephalographic data in terms of Power Spectral Density. The results show the capacity and usability of autoassociative neural networks in patterns classification.
Acesso aberto (Open Access)
Estrutura de redes neurais auto-associativas aplicadas ao processo de identificação de equipamentos elétricos em sistemas de monitoramento não intrusivo de cargas
(Universidade Federal do Pará, 2019-10-23) MORAIS, Lorena dos Reis; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
The pursuit of reducing and rationalizing electricity consumption is increasingly becoming a priority for all consumers worldwide. Residential environments are responsible for a large part of electricity consumption. Non-intrusive load monitoring systems were created with the aim of helping consumers, providing the possibility of obtaining information about the individual consumption of equipment and thus allowing a monitored consumption and the consequent increase in energy efficiency. In a Non-Intrusive Load Monitoring System, four steps are critical: acquiring aggregate data through a single sensor, detecting equipment on / off events from the aggregate load, extracting disaggregated signal characteristics and the identification of equipment based on the characteristics extracted from the disaggregated signal. In this context, this work proposes a new methodology for identification of electrical equipment in a residential environment employing a competitive structure of Auto-Associative Neural Networks. The proposed system is based on power signal measurements obtained from equipment on / off events. To test the proposed methodology 3 scenarios were developed using 3 different public databases. Due to the good results achieved, analyzed using statistical metrics, it is evaluated that the proposed methodology is able to efficiently perform the task of identifying electrical equipment, thus contributing to the development of future non-intrusive monitoring systems. meet market demands.
Acesso aberto (Open Access)
Inteligência computacional aplicada à detecção e correção de outliers em séries temporais: estudo de caso em consumo de energia elétrica
(Universidade Federal do Pará, 2015-09-04) MELO, Diemisom Carlos Romano de; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
The electric load prediction is a task that requires accurate models, as should properly influence the decision making in hydroelectric plants and power stations. These computer models are implemented from a data set that must faithfully represent the behavior of the variables. However, these data sets are quite common the presence of outliers, which arise due to sensor reading errors, errors in the actual processing system / storage of data or faults in the distribution system or power station. This paper proposes a new methodology based on Computational Intelligence for detection and treatment of outliers in time series of electric power load. An auto associative artificial neural network is used for outlier detection. Subsequently, it is reused together with a genetic algorithm to correct detected outliers. This approach was applied to a time series of electrical power load in the State of Pará. The computational experiments were performed using the MATLAB tool and the results demonstrate the efficiency of the proposal, which identified and corrected all virtual outliers introduced during the evaluation phase of the methodology.
Acesso aberto (Open Access)
Metodologias de inteligência computacional aplicadas ao problema de previsão de carga a curto prazo
(Universidade Federal do Pará, 2010-12-27) BRAGA, Marcus de Barros; SANTANA, Ádamo Lima de; http://lattes.cnpq.br/4073088744952858
Several activities of planning and operation in power systems rely on knowledge of early and accurate demand of electric load. For this reason, power generation and distribution companies are increasingly using technologies for load forecasting. These estimative may have a very short, short, medium or long-term horizon. Numerous statistical methods have been used for the problem of prediction. All these methods work well under normal conditions, but fail in situations where unexpected changes in the parameters of the environment occur. Currently, techniques based on Computational Intelligence have been presented in the literature with satisfactory results for the problem of load forecasting.Considering then the importance of load forecasting for the electric power systems, in this thesis a new approach to the load forecasting problem is evaluated by Auto-Associative Neural Networks and Genetic Algorithms. Three models based on Computational Intelligence are also presented with their performance evaluated and compared with the proposed system. With the obtained results, it was found that the proposed model is satisfactory for the problem of forecasting, thereby strengthening the applicability of computational intelligence methodologies to the problem of load prediction.
Acesso aberto (Open Access)
Reconhecimento de atividades humanas utilizando redes neurais auto-associativas e dados de smartphone
(Universidade Federal do Pará, 2016-12-16) SIQUEIRA, André Luis Carvalho; CASTRO, Adriana Rosa Garcez; http://lattes.cnpq.br/5273686389382860
Human Activity Recognition (HAR) is an important challenging research area with many applications in intelligence ambient, healthcare and homeland security systems. HAR is the process whereby a person is monitored through sensors and analyzed to infer the undergoing activities during a period of time. This work presents the development of two systems for the HAR using auto associative neural networks. The activity recognition systems are based on public dataset that has signal from three static postures (standing, sitting, lying) and three dynamic activities (walking, walking downstairs and walking upstairs).The dataset was captured by using accelerometer and gyroscopic sensor of a Smartphone. The features extracted from the time and the acceleration due to body motion were used to the development of the proposed systems. Our experimental results illustrates the effectiveness of the proposed system.