Navegando por Assunto "Aprendizagem profunda"

Agora exibindo 1 - 5 de 5

Acesso aberto (Open Access)
Beam-selection otimizado por aprendizado de máquina : uma abordagem multimodal
(Universidade Federal do Pará, 2023-12-30) FERREIRA, Jamelly Freitas; GOMES, Diego de Azevedo; http://lattes.cnpq.br/5116561408505726; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
This dissertation aims to investigate the use of machine learning models using multimodal data as input to optimize the Beam-Selection process in millimeter-wave based networks. The use of Deep Learning has intensified in different areas, and it is possible to obtaing performance equal or superior to human performance, so its use is also promising in wireless communication scenarios. This work used data from different sources, which proved to be convenient since it is possible to adjust the model according to the quality/availability of this data. After executing the experiments and obtaining the results, it was observed that it is possible to obtain significant performance in different metrics even with simpler data such as image and coordinate.
Acesso aberto (Open Access)
Deep learning software-based holdover for PTP IEEE 1588 synchronization in 5G networks
(Universidade Federal do Pará, 2023-03-28) DUTRA, Rodrigo Gomes; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
This work proposes evaluates software-based algorithm mechanisms for maintaining the synchronization of a real-time clock in holdover operation when the timing reference input is unavailable. Three algorithms, Autoregressive Integrated Moving Average (ARIMA), long short term memory (LSTM), and Transformer networks, are implemented and trained using timestamps and temperature data acquired while the slave clock is locked to a master clock. When the slave clock loses its reference, the algorithm-based models take over and control the clock. The proposed method is evaluated on a testbed of IEEE 1588 Precision Time Protocol (PTP) clocks based on field-programmable gate arrays, where nanosecond-accurate timestamps are collected for offline analysis. The models are evaluated using two clocks, one cost-effective, cristal oscillator (XO), and one robust, oven controlled cristal oscillator (OCXO), in both constant and variable temperature scenarios. The results show that all algorithms can sustain clock synchronization accuracy within reasonable Time division duplex (TDD) synchronization limits over intervals of 1000 seconds in all temperature and clock scenarios, with the transformerbased holdover mechanism outperforming the statistical approach and LSTM network. This cost-effective software-based approach proves to be feasible for increasing clock accuracy during holdover operation and can be generalized to other holdover contexts, such as in a Global Navigation Satellite System (GNSS) scenario.
Acesso aberto (Open Access)
Detecção e rastreamento de componentes de vagões ferroviários utilizando redes neurais convolucionais e restricões geométricas
(Universidade Federal do Pará, 2020-04-27) GONÇALVES, Camilo Lélis Assis; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609
A inspeção de componentes de trem que podem causar descarrilamento possui um papel importante na manutenção ferroviária. A fim de aumentar a produtividade e a segurança, empresas prestadoras de serviços procuram por soluções de inspeção automáticas e confiáveis. Apesar da inspeção automática baseada em visão computacional ser um conceito consolidado, tais aplicações desafiam a comunidade de desenvolvimento em razão de fatores ambientais e logísticos a serem considerados. Este trabalho propõe uma técnica de detecção e estimativa das posições das regiões de dreno presentes em vagões de trem. Nosso detector/rastreador consiste em uma rede neural convolucional e um conjunto de restrições geométricas, que levam em conta a trajetória ideal dos componentes de interesse dos vagões e as distâncias entre eles. Detalhamos os procedimentos de treinamento e validação, juntamente com as métricas utilizadas para aferir a performance do sistema proposto. Os resultados apresentados são comparados com outras duas técnicas, e exibem um bom custo‑benefício entre confiança e complexidade computacional para a detecção dos componentes de interesse.
Acesso aberto (Open Access)
Estimativa Volumétrica de Resíduos Sólidos Urbanos em Imagem de Visualização Única.
(Universidade Federal do Pará, 2024-09-02) AZANCORT NETO, Júlio Leite; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
Efficient solid waste management is crucial for keeping the city clean and sustainable. This work presents a methodology that uses well-established algorithms for volume estimation in urban solid waste management from single-view images. The proposed system is based on state-of-the-art computer vision concepts and models, including instance segmentation, depth estimation, and volume calculation based on point clouds. The methodology demonstrated the ability to accurately estimate the volume of both individual and multiple solid waste objects in images. We evaluated our approach using real-world data. Despite challenges such as manual rescaling of distances and limited datasets, our system shows considerable potential for refinement and improvement, targeting complex scenarios like real urban environments. Numerical results indicated that the proposed system is promising even in complex scenarios, with mean absolute percentage errors (MAPE) of 8.60% for single waste and 9.23% for multiple wastes, resulting in an overall average of 8.91%. The coefficient of determination was 95.11% for single instances and 87.64% for multiple instances. The proposed methodology significantly contributes to the advancement of management technologies in smart cities.
Acesso aberto (Open Access)
Identificacao de larvas de mosquitos do genero aedes utilizando redes neurais convolucionais
(Universidade Federal do Pará, 2023-09-29) SILVA, Romário da Costa; FERREIRA JÚNIOR, José Jailton Henrique; http://lattes.cnpq.br/9031636126268760; FRANCÊS, Carlos Renato Lisboa
Arboviruses transmitted by mosquitoes of the Aedes genus constitute a threat to public health. Detection and control of these vectors are critical to preventing disease outbreaks including Dengue, Chikungunya, Zika and Yellow Fever. Computer vision and deep learning techniques have been increasingly used in epidemiological control, mainly with regard to the classification and detection of these mosquitoes. In this sense, three models are proposed for classification, detection and segmentation of mosquito larvae based on the use of convolutional neural networks (CNN) and object detection algorithms (YOLO). For this purpose, a dataset was created for training purposes. The dataset is composed of images of larvae, being categorized between Aedes and Non-Aedes classes. The results show that the proposed models are promising strategies and achieved accuracy values of 86.71%, mAP (Mean Average Precision) of 88.3% and 95.7% for the tasks of classification, detection and segmentation, respectively.