Programa de Pós-Graduação em Engenharia Elétrica e de Computação
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Doctoral Thesis ELSA - Expanded Latent Space Autoencoder Architecture for Feature Extraction: a case study application to Covid-19 time series forecasting(Universidade Federal do Rio Grande do Norte, 2024-09-20) Oliveira, Emerson Vilar de; Gonçalves, Luiz Marcos Garcia; https://orcid.org/0000-0002-7735-5630; http://lattes.cnpq.br/1562357566810393; http://lattes.cnpq.br/8790940901329225; Oliveira, Luiz Affonso Henderson Guedes de; https://orcid.org/0000-0003-2690-1563; http://lattes.cnpq.br/7987212907837941; Silva Júnior, Andouglas Gonçalves da; Santos, Davi Henrique dos; Aroca, Rafael VidalThe global SARS-CoV-2 pandemics compelled governments, institutions, and researchers to assess its impact and develop strategies based on general indicators to achieve the most accurate predictions possible, in order to help managers mitigating its effect. While known epidemiological models were naturally used, they often produced uncertain forecasts due to insufficient or missing data. In addition to data limitation, various machine-learning models such as random forests, support vector regression, LSTM, auto-encoders, and traditional time-series models like Prophet and ARIMA—were employed, yielding impressive yet somewhat limited results. Some of these methods struggle with precision when handling multi-variable inputs, which are crucial for problems like pandemics time series prediction that require both short- and long-term forecasting. In response to this challenge, we propose a novel approach for time-series prediction that utilizes a stacked auto-encoder structure. Our model uses n internal autoencoders to process the input and generate different latent spaces for this respective input. Then these different latent spaces are concatenated and the expanded latent space is obtained. We conducted an experiment using previously published data series on COVID-19 cases, deaths, temperature, humidity, and the air quality index (AQI) in São Paulo City, Brazil. This experiment assessed the suitability of our model for short-, medium-, and long-term forecasting. Furthermore, we directly compared our proposed model with two existing works in the literature that have already undergone expert scrutiny. The first comparison places our model among those that use one network for feature extraction and another for predicting the pandemic trends. The second comparison highlights our model’s effectiveness in multi-series forecasting of pandemic indicators. The results suggest that our proposed model possesses strong capabilities in both feature extraction and multi-series forecasting, offering improvements over the two comparison works. Finally, the model demonstrates promising forecasting accuracy and versatility across datasets of varying lengths, making it a standout option for time-series forecasting tasks.Master Thesis Análise de desempenho de método baseado em rede LSTM para classificação de falhas em um processo de controle de nível(Universidade Federal do Rio Grande do Norte, 2020-08-28) Oliveira, Emerson Vilar de; Oliveira, Luiz Affonso Henderson Guedes de; ; ; Bezerra, Clauber Gomes; ; Gonçalves, Luiz Marcos Garcia;Due to the increasing demands in the operation monitoring of industrial plants, methodologies for fault detect and diagnose in the operation of these processes are gaining more and more importance, because they can contribute to more assertive and even predictive repairs in the components that generated such disturbances to the proper functioning of the system. With the growth of data-oriented approaches, Artificial Neural Networks have become considerable allies in solving these problems, and Recurrent Neural Networks, in particular, has gained strength due to their affinity in dealing with series that have temporal links between their samples, which is the case of industrial process variables monitoring. Due to this relevance, this dissertation analyzes the performance of Long Short-Term Memory (LSTM) recurrent neural network for the detection and classification of faults in a pilot-scaled level control process. For the performance evaluation, a methodology based on Monte Carlo statistical tests was used, in which the influence of the LSTM network hyperparameters, such as the number of layers and size of the input and regressors, was analyzed. The accuracy was the metric chosen to quantify the fault classification performance. The data set obtained from the operation of the pilot plant contained 23 situations of disturbances in this process, which resulted from disturbances applied to components such as sensors, valves, and the water tank itself. The adopted methodology proved to be quite efficient to examine both the performance and the robustness of these neural networks for the fault classification activity, in addition to indicating the best network architecture configurations.