Ilan Sousa Figueirêdo, Lilian Lefol Nani Guarieiro, ERICK GIOVANI SPERANDIO NASCIMENTO (2020)Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms, In: Brain-Computer Interface IntechOpen

The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.

Raphael Souza de Oliveira, ERICK GIOVANI SPERANDIO NASCIMENTO (2021)Clustering by Similarity of Brazilian Legal Documents Using Natural Language Processing Approaches, In: Data Clustering IntechOpen

The Brazilian legal system postulates the expeditious resolution of judicial proceedings. However, legal courts are working under budgetary constraints and with reduced staff. As a way to face these restrictions, artificial intelligence (AI) has been tackling many complex problems in natural language processing (NLP). This work aims to detect the degree of similarity between judicial documents that can be achieved in the inference group using unsupervised learning, by applying three NLP techniques, namely term frequency-inverse document frequency (TF-IDF), Word2Vec CBoW, and Word2Vec Skip-gram, the last two being specialized with a Brazilian language corpus. We developed a template for grouping lawsuits, which is calculated based on the cosine distance between the elements of the group to its centroid. The Ordinary Appeal was chosen as a reference file since it triggers legal proceedings to follow to the higher court and because of the existence of a relevant contingent of lawsuits awaiting judgment. After the data-processing steps, documents had their content transformed into a vector representation, using the three NLP techniques. We notice that specialized word-embedding models—like Word2Vec—present better performance, making it possible to advance in the current state of the art in the area of NLP applied to the legal sector.

Mirella Lima Saraiva Araujo, Yasmin Kaore Lago Kitagawa, Davidson Martins Moreira, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)Forecasting Tropospheric Ozone Using Neural Networks and Wavelets: Case Study of a Tropical Coastal-Urban Area, In: Computational Intelligence Methodologies Applied to Sustainable Development Goalspp. 159-173 Springer International Publishing

Air quality improvement is directly associated with the Sustainable Development Goals (SDGs), established by the United Nations in 2015. To reduce potential impacts from air pollution, computational intelligence by supervised machine learning, using different artificial neural networks (ANNs) techniques, shows to be a promising tool. To enhance their abilities to predict air quality, ANNs have been combined with data preprocessing. The present work performs short-term forecasting of hourly ground-level ozone using long short-term memory (LSTM), a type of recurrent neural network, with the discrete wavelet transform. The study was performed using data from a tropical coastal-urban site in Southeast Brazil, highly influenced by intense convective weather with complex terrain. The models’ performance was carried out by comparing statistical indices of errors and agreement, namely: mean squared error (MSE), normalized mean squared error (NMSE), mean absolute error (MAE), Pearson’s r, R2 and mean absolute percentage error (MAPE). When comparing the statistical metrics values, it is shown that the combination of artificial neural networks with wavelet transform enhanced the model’s ability to forecast ozone levels compared to the baseline model, which did not use wavelets.

ERICK GIOVANI SPERANDIO NASCIMENTO, D.M. Moreira, Gilberto Fisch, Taciana Toledo de Almeida Albuquerque (2014)Simulation of Rocket Exhaust Clouds at the Centro de Lancamento de Alcantara Using the WRF-CMAQ Modeling System, In: Journal of aerospace technology and management6(2)pp. 119-128 Inst Aeronautica & Espaco-Iae

In this work we report numerical simulations of the contaminant dispersion and photochemical reactions of rocket exhaust clouds at the Centro de Lancamento de Alcantara (CLA) using the CMAQ modeling system. The simulations of carbon monoxide (CO), hydrogen chloride (HCl) and alumina (solid Al2O3) pollutants emission represent an effort in the construction of a computational tool in order to simulate normal and/or accidental events during rocket launches, making possible to predict the contaminant concentrations in accordance with emergency plans and pre and post-launchings for environmental management. The carbon monoxide and the alumina concentrations showed the formation of the ground and contrail cloud. The results also showed that hydrogen chloride concentrations would be harmful to human health, demonstrating the importance of assessing the impact of rocket launches in the environment and human health.

ERICK GIOVANI SPERANDIO NASCIMENTO, Davidson Martins Moreira, Taciana Toledo de Almeida Albuquerque (2017)The development of a new model to simulate the dispersion of rocket exhaust clouds, In: Aerospace science and technology69pp. 298-312 Elsevier Masson SAS

This study presents the development of a new model named MSRED, which was designed to simulate the formation, rise, expansion, stabilisation and dispersion of rocket exhaust clouds for short-range assessment, using a three-dimensional semi-analytical solution of the advection–diffusion equation based on the ADMM method. For long-range modelling, the MSRED was built to generate a ready-to-use initial conditions file to be input to the CMAQ model, as it represents the state-of-the-art in regional and chemical transport air quality modelling. Simulations and analysis were carried out in order to evaluate the application of this integrated modelling system for different rocket launch cases and atmospheric conditions, for the Alcantara Launching Center (ALC, the Brazilian gate to the space) region. This hybrid, modern and multidisciplinary system is the basis of a modelling framework that will be employed at ALC for pre- and post-launching simulations of the environmental effects of rocket operations.

J. V. C. Santos, D.M. Moreira, Marcelo Albano Moret, ERICK GIOVANI SPERANDIO NASCIMENTO (2019)Analysis of long-range correlations of wind speed in different regions of Bahia and the Abrolhos Archipelago, Brazil, In: Energy (Oxford)167pp. 680-687 Elsevier

This work analyzes the time series of wind speeds in different regions of the state of Bahia and the Abrolhos Archipelago, Brazil, through the use of the DFA technique (Detrended Fluctuation Analysis) to verify the existence of long-range correlations and associated power laws. The time series of wind velocities are derived from measurements with hourly means that are acquired in three towers equipped with anemometers at heights of 80, 100, 120 and 150 m, and in the Abrolhos Archipelago with measurements taken at 10 m. These measurements are then compared with numerical simulations of the wind speed obtained with the WRF mesoscale model (Weather Research and Forecasting model). In the onshore case, the results of the application of the DFA technique in the measured and simulated datasets show correlations with power laws in two regions of distinct scales (subdiffusive and persistent) for both time series. It is suggested that this occurs due to the mesoscale effects and local circulations acting on the planetary boundary layer, where the turbulence in the daily cycle is generated by thermal (buoyancy) and mechanical (wind shear) forcing. However, in regions that are not subject to local-effect conditions, such as small islands far from the mainland, the synoptic effects are the most important and active in the maritime boundary layer, so the series of real and simulated datasets exhibit only subdiffusive behavior. (C) 2018 Elsevier Ltd. All rights reserved.

Taciana Toledo de Almeida Albuquerque, Maria de F. Andrade, RY Ynoue, D.M. Moreira, Willian Lemker Andreão, Fabio S. dos Santos, ERICK GIOVANI SPERANDIO NASCIMENTO (2018)WRF-SMOKE-CMAQ modeling system for air quality evaluation in SAo Paulo megacity with a 2008 experimental campaign data, In: Environmental science and pollution research international25(36)pp. 36555-36569 Springer Nature

Atmospheric pollutants are strongly affected by transport processes and chemical transformations that alter their composition and the level of contamination in a region. In the last decade, several studies have employed numerical modeling to analyze atmospheric pollutants. The objective of this study is to evaluate the performance of the WRF-SMOKE-CMAQ modeling system to represent meteorological and air quality conditions over SAo Paulo, Brazil, where vehicular emissions are the primary contributors to air pollution. Meteorological fields were modeled using the Weather Research and Forecasting model (WRF), for a 12-day period during the winter of 2008 (Aug. 10th-Aug. 22nd), using three nested domains with 27-km, 9-km, and 3-km grid resolutions, which covered the most polluted cities in SAo Paulo state. The 3-km domain was aligned with the Sparse Matrix Operator Kernel Emissions (SMOKE), which processes the emission inventory for the Models-3 Community Multiscale Air Quality Modeling System (CMAQ). Data from an aerosol sampling campaign was used to evaluate the modeling. The PM10 and ozone average concentration of the entire period was well represented, with correlation coefficients for PM10, varying from 0.09 in Pinheiros to 0.69 in ICB/USP, while for ozone, the correlation coefficients varied from 0.56 in Pinheiros to 0.67 in IPEN. However, the model underestimated the concentrations of PM2.5 during the experiment, but with ammonium showing small differences between predicted and observed concentrations. As the meteorological model WRF underestimated the rainfall and overestimated the wind speed, the accuracy of the air quality model was expected to be below the desired value. However, in general, the CMAQ model reproduced the behavior of atmospheric aerosol and ozone in the urban area of SAo Paulo.

Paulo Henrique Farias Xavier, ERICK GIOVANI SPERANDIO NASCIMENTO, D.M. Moreira (2019)A Model Using Fractional Derivatives with Vertical Eddy Diffusivity Depending on the Source Distance Applied to the Dispersion of Atmospheric Pollutants, In: Pure and applied geophysics176pp. 1797-1806 Springer Nature

This work presents an analytical solution of the two-dimensional advection-diffusion equation of fractional order, in the sense of Caputo and applied it to the dispersion of atmospheric pollutants. The solution is obtained using Laplace decomposition and homotopy perturbation methods, and it considers the vertical eddy diffusivity dependency on the longitudinal distance of the source with fractional exponents of the same order of the fractional derivative (Kx). For validation of the model, simulations were compared with data from Copenhagen experiments considering moderately unstable conditions. The best results were obtained with =0.98, considering wind measured at 10m, and =0.94 with wind measured at a height of 115m.

ERICK GIOVANI SPERANDIO NASCIMENTO, Noele B. P. Souza, Yasmin Kaore Lago Kitagawa, D.M. Moreira (2018)Simulated Dispersion of the Gas Released by the SpaceX Falcon9 Rocket Explosion, In: Journal of spacecraft and rockets55(6)pp. 1528-1536 Amer Inst Aeronautics Astronautics

This Paper provides a qualitative analysis of the contaminant dispersion caused by the SpaceX Falcon 9 rocket accident at Cape Canaveral Air Force Station on 1 September 2016. To achieve this, the Model for Simulating Rocket Exhaust Dispersion and its modeling system were applied to simulate the dispersion of the contaminants emitted during the explosion of the Falcon 9 rocket. This modeling system is a modern tool for risk management and environmental analysis for the evaluation of normal and aborted rocket launch events, being also suitable for the assessment of explosion cases. It deals with the representation of the source term (formation, rising, expansion, and stabilization of the exhaust cloud), the simulation of the short-range dispersion (in the scale from minutes to a couple of hours), and the long-range and chemical transport modeling by integrating with the Community Multiscale Air Quality model and reading meteorological input data from the Weather Research and Forecast model. The results showed that the modeling system captured satisfactorily the phenomenon inside the planetary boundary layer.

Pedro Junior Zucatelli, ERICK GIOVANI SPERANDIO NASCIMENTO, A.M.G. Arce, Davidson Martins Moreira (2019)Short-Range Wind Speed Predictions in Subtropical Region Using Artificial Intelligence, In: Journal of systemics, cybernetics and informatics17(4)pp. 18-25 International Institute of Informatics and Cybernetics

Short-range wind speed predictions for subtropical region is performed by applying Artificial Neural Network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations to Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), a deep learning algorithm based method, are applied for each site and height. A quantitative analysis is conducted and the statistical results are evaluated to select the configuration that best predicts the real data. These methods have lower computational costs than other techniques, such as numerical modelling. The proposed method is an important scientific contribution for reliable large-scale wind power forecasting and integration into existing grid systems in Uruguay. The best results of the short-term wind speed forecasting was for MLP, which performed the forecasts using a hybrid method based on recursive inference, followed by LSTM, at all the anemometer heights tested, suggesting that this method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.

L Campos, Peterson Nogueira, Davidson Martins Moreira, ERICK GIOVANI SPERANDIO NASCIMENTO (2019)An Empirical Analysis of the Influence of Seismic Data Modeling for Estimating Velocity Models with Fully Convolutional Networks, In: Journal of systemics, cybernetics and informatics17(4)pp. 26-32 International Institute of Informatics and Cybernetics

Seismic modeling is the process of simulating wave propagations in a medium to represent underlying structures of a subsurface area of the earth. This modeling is based on a set of parameters that determine how the data is produced. Recent studies have demonstrated that deep learning methods can be trained with seismic data to estimate velocity models that give a representation of the subsurface where the seismic data was generated. Thus, an analysis is made on the impact that different sets of parameters have on the estimation of velocity models by a fully convolutional network (FCN). The experiments varied the number of sources among four options (1, 10, 25 or 50 shots) and used three different ranges of peak frequencies: 4, 8 and 16 Hz. The results demonstrated that, although the number of sources have more influence on the computational time needed to train the FCN than the peak frequency, both changes have significant impact on the quality of the estimation. The best estimations were obtained with the experiment of 25 sources with 4 Hz and increasing the peak frequency to 8 Hz improved even more the results, especially regarding the FCN's loss function.

José Vicente Cardoso Santos, Palmira Maria De Santana Acioli, Georgynio Yossimar Rosales Aylas, Chrislaine Do Bomfim Marinho, Davidson Martins Moreia, Marcelo Albano Moret, Erick Giovani Sperandio Nascimento (2019)Avaliação de potencial eólico e solar com correlações de longo alcance em séries temporais em Salvador, In: Revista Mundi Engenharia, Tecnologia e Gestão4(5 - Edição especial - XXI ENMC (Encontro Nacional de Modelagem Computacional) e IX ECTM (Encontro de Ciência e Tecnologia dos Materiais))

Este trabalho mostra a existência de correlações de longo alcance das séries históricas e temporais de velocidade do vento e radiação solar na cidade de Salvador (Bahia) provenientes de dados medidos em estações meteorológicas, além de simulações com o modelo de mesoescala WRF (Weather Research and Forecasting), através do método DFA (Detrended Fluctuation Analysis). Resultados preliminares indicam que as séries de dados locais são caracterizadas com persistência na velocidade do vento e radiação solar de forma satisfatória para a geração de energia, o que indica viabilidade da participação destas respectivas matrizes na matriz energética local. This work shows the positive correlation between the historical and temporal series of velocity and solar energy in the city of Salvador (Bahia). Research and Forecasting) through the DFA (Detrended Fluctuation Analysis) method. Preliminary results indicate that the data series are characterized with persistence in speed and solar energy in a satisfactory way for an energy generation, which indicates the feasibility of the learning of matrices of matrices in the local energy

Taciana Toledo de Almeida Alburquerque, JB WEST, Maria de F. Andrade, RY Ynoue, Willian Lemker Andreão, Fabio S. dos Santos, Felipe Marinho Maciel, Rizzieri Pedruzzi, Vitor de O. Mateus, Jorge A. Martins, Leila D. Martins, Erick Giovani Sperandio Nascimento, Davidson Martins Moreira (2019)Analysis of PM2.5 concentrations under pollutant emission control strategies in the metropolitan area of Sao Paulo, Brazil, In: Environmental Science and Pollution Research26(32)pp. 33216-33227 Springer

Great efforts have been made over the years to assess the effectiveness of air pollution controls in place in the metropolitan area of Sao Paulo (MASP), Brazil. In this work, the community multiscale air quality (CMAQ) model was used to evaluate the efficacy of emission control strategies in MASP, considering the spatial and temporal variability of fine particle concentration. Seven different emission scenarios were modeled to assess the relationship between the emission of precursors and ambient aerosol concentration, including a baseline emission inventory, and six sensitivity scenarios with emission reductions in relation to the baseline inventory: a 50% reduction in SO2 emissions; no SO2 emissions; a 50% reduction in SO2, NOx, and NH3 emissions; no sulfate (PSO4) particle emissions; no PSO4 and nitrate (PNO3) particle emissions; and no PNO3 emissions. Results show that ambient PM2.5 behavior is not linearly dependent on the emission of precursors. Variation levels in PM2.5 concentrations did not correspond to the reduction ratios applied to precursor emissions, mainly due to the contribution of organic and elemental carbon, and other secondary organic aerosol species. Reductions in SO2 emissions are less likely to be effective at reducing PM2.5 concentrations at the expected rate in many locations of the MASP. The largest reduction in ambient PM2.5 was obtained with the scenario that considered a reduction in 50% of SO2, NOx, and NH3 emissions (1 to 2 mu g/m(3) on average). It highlights the importance of considering the role of secondary organic aerosols and black carbon in the design of effective policies for ambient PM2.5 concentration control.

Davidson Martins Moreira, Paulo Xavier, Anderson Palmeira, Erick Giovani Sperandio Nascimento (2019)New approach to solving the atmospheric pollutant dispersion equation using fractional derivatives, In: International Journal of Heat and Mass Transfer144118667 Elsevier

In atmospheric environments, traditional differential equations do not adequately describe the problem of turbulent diffusion because the usual derivatives are not well defined in the non-differentiable behaviour introduced by turbulence, where the fractional calculation has become a very useful tool for studying anomalous dispersion and other transportation processes. Considering a new direction, this paper presents an analytical series solution of a three-dimensional advection-diffusion equation of fractional order, in the Caputo sense, applied to the dispersion of atmospheric pollutants. The solution is obtained by applying the generalised integral transform technique (GITT), solving the transformed problem by the Laplace decomposition method (LDM), and considering the lateral and vertical turbulent diffusion dependence on the longitudinal distance from the source, as well as a fractional parameter. The fractional solution is more general than the traditional solution in the sense that consideration of the integer order of the fractional parameter yields the traditional solution. The solution considers the memory effect in eddy diffusivity and in the fractional derivative, and it is simple, easy to implement, and converges rapidly. Numerical simulations were conducted to compare the performance of the proposed fractional solution to the traditional solution using an experimental dataset and other models, which also made it possible to find a better parametrisation for use in Gaussian models. The best results are obtained with the fractional order of the derivative. (C) 2019 Elsevier Ltd. All rights reserved.

Erick Giovani Sperandio Nascimento, Noéle Bissoli Perini de Souza, Davidson Martins Moreira (2019)Evaluating the Impact of HCL Atmospheric Dispersion caused by an Aborted Rocket Launch in different Stability Conditions, In: International Journal of Advanced Engineering Research and Science6(7)pp. 577-585

An aborted rocket launch may occur because of explosions during pre-launch operations or launch performance, which generates a huge cloud near ground level comprising hot buoyant exhaust products. This action occur within a few minutes, and populated areas near the launch centre may be exposed to high levels of hazardous pollutant concentrations within a short time scale — from minutes to a couple of hours. Although aborted rocket launch events do not occur frequently, the occurrence rate has increased in the past few years, making it mandatory to perform short and long-range assessments to evaluate the impact of such operations on the air quality of a region. In this work, we use a modern approach based on the Model for Simulating the Rocket Exhaust Dispersion (MSRED) and its modelling system to report the simulated impact of a hydrogen chloride (HCl) exhaust cloud, formed during a hypothetical aborted rocket launch, on the atmosphere near the earth’s surface at the Alcantara Launch Center, Brazil’s space-port. The results show that when a launch occurs under stable atmospheric conditions, the HCl concentrations near the ground can reach levels that are extremely hazardous to human health.

Pedro Junior Zucatelli, Erick Giovani Sperandio Nascimento, G.Y.R. Aylas, N.B.P. Souza, Yasmin Kaore Lago Kitagawa, A.A.B. Santos, A.M.G. Arce, D.M. Moreira (2019)Short-term wind speed forecasting in Uruguay using computational intelligence, In: Heliyon5(5)pp. e01664-e01664 Elsevier

Short-term wind speed forecasting for Colonia Eulacio, Soriano Department, Uruguay, is performed by applying an artificial neural network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results are evaluated to select the configuration that best predicts the real data. This method has lower computational costs than other techniques, such as numerical modelling. For integrating wind power into existing grid systems, accurate short-term wind speed forecasting is fundamental. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in Uruguay. The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested, suggesting that the method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.

Rizzieri Pedruzzi, Bok H. Baek, Barron H. Henderson, Nikolle Aravanis, Janaina Antonino Pinto, Igor B. Araujo, Erick Giovani Sperandio Nascimento, Neyval C. Reis Junior, D.M. Moreira, Taciana Toledo de Almeida Albuquerque (2019)Performance evaluation of a photochemical model using different boundary conditions over the urban and industrialized metropolitan area of Vitoria, Brazil, In: Environmental Science and Pollution Research26(16)pp. 16125-16144 Springer

Metropolitan areas may suffer with increase of air pollution due to the growth of urbanization, transportation, and industrial sectors. The Metropolitan Area of Vitoria (MAV) in Brazil is facing air pollution problems, especially because of the urbanization of past years and of having many industries inside the metropolitan area. Developing air quality system is crucial to understand the air pollution mechanism over these areas. However, having a good input dataset for applying on photochemical models is hard and requires quite of research. One input file for air quality modeling which can play a key role on results is the lateral boundary conditions (LBC). This study aimed to investigate the influence of LBC over CMAQ simulation for particulate matter and ozone over MAV by applying four different methods as LBC during August 2010. The first scenario (M1) is based on a fixed, time-independent boundary conditions with zero concentrations for all pollutants; the second scenario (M2) used a fixed, time-independent concentration values, with average values from local monitoring stations; the third CMAQ nesting scenario (M3) used the nested boundary conditions varying with time from a previous simulation with CMAQ over a larger modeling domain, centered on MAV; and finally, the fourth GEOS-Chem scenario (M4) used the boundary conditions varying with time from simulations of global model GEOS-Chem. All scenarios runs are based on the same meteorology conditions and pollutant emissions. The air quality simulations were made over a domain 61x79km centered on coordinates -20.25 degrees S, -40.28 degrees W with a resolution of 1km. The results were evaluated with the measured data from the local monitoring stations. Overall, significant differences on concentrations and number of chemical species between the LBC scenarios are shown across all LBC scenarios. The M3 and M4 dynamic LBC scenarios showed the best performances over ozone estimates while M1 and M2 had poor performance. Although no LBC scenarios do not seem to have a great influence on total PM10 and PM2.5 concentrations, individual PM2.5 species like Na, NO3-, and NH(4)(+)concentrations are influenced by the dynamic LBC approach, since those hourly individual PM2.5 species from CMAQ nesting approach (M3) and GEOS-Chem model (M4) were used as an input to LBC.

Ilan Sousa Figueirêdo, Wenisten Jose Dantas Da Silva, Ubatan A. Miranda, Ricardo Emanuel Vaz Vargas, Erick Giovani Sperandio Nascimento, Tássio Farias Carvalho, Leonildes Soares De Melo Filho (2020)Multivariate anomaly detection in rotating machinery of the oil and gas industry using unsupervised machine learning, In: Trabalhos técnicos da Rio Oil & Gas 2020 - Technical papers20

In the industrial environment, the health status of critical machinery is constantly monitored, consequently generating a large amount of data that needs to be analyzed by experts. However, it becomes unfeasible to a human to verify and correlate all real time data, especially in annotating and classifying the interesting patterns present in the data - such as normal or abnormal/failure - which is valuable in researches that involve the development of predictive and classification models using Artificial Intelligence. This paper presents a comparative study between methods of detecting interesting patterns and anomalies based on unsupervised machine learning, aiming to automate the data annotation process between normal or abnormal classes (or failures), in order to further detect the failures in industrial machinery. Multivariate real data acquired from 21 sensors coupled to a gearbox of a turbo generator were used. The results revealed that unsupervised learning methods effectively detected normal and anomalous behaviors without the need of prior labeling or classification by experts, with emphasis on the C-AMDATS algorithm. In fact, the use of real data proves that the proposed approach is suitable for unsupervised anomaly detection. Therefore, it is possible to conclude that unsupervised machine learning algorithms are able to assist experts and managers in decision making and preparing labeled data for later use in supervised machine learning algorithms for prediction and classification purposes, providing greater reliability in maintenance.

Erick Giovani Sperandio Nascimento, Adhvan Furtado, Roberto Badaró, Luciana Knop (2020)The New Technologies in the Pandemic Era, In: Journal of Bioengineering and Technology Applied to Health3(2)pp. 134-164

The pandemic of the new coronavirus affected people’s lives by an unprecedented scale. Due to the need for isolation and the treatments, drugs, and vaccines, the pandemic amplified the digital health technologies, such as Artificial Intelligence (AI), Big Data Analytics (BDA), Blockchain, Telecommunication Technology (TT) as well as High-Performance Computing (HPC) and other technologies, to historic levels. These technologies are being used to mitigate, facilitate pandemic strategies, and find treatments and vaccines. This paper aims to reach articles about new technologies applied to COVID-19 published in the main database (PubMed/Medline, Elsevier Science Direct, Scopus, Isi Web of Science, Embase, Excerpta Medica, UptoDate, Lilacs, Novel Coronavirus Resource Directory from Elsevier), in the high-impact international scientific Journals (Scimago Journal and Country Rank - SJR - and Journal Citation Reports - JCR), such as The Lancet, Science, Nature, The New England Journal of Medicine, Physiological Reviews, Journal of the American Medical Association, Plos One, Journal of Clinical Investigation, and in the data from Center for Disease Control (CDC), National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID) and World Health Organization (WHO). We prior selected meta-analysis, systematic reviews, article reviews, and original articles in this order. We reviewed 252 articles and used 140 from March to June 2020, using the terms coronavirus, SARS-CoV-2, novel coronavirus, Wuhan coronavirus, severe acute respiratory syndrome, 2019-nCoV, 2019 novel coronavirus, n-CoV-2, covid, n-SARS-2, COVID-19, corona virus, coronaviruses, New Technologies, Artificial Intelligence, Telemedicine, Telecommunication Technologies, AI, Big Data, BDA, TT, High-Performance Computing, Deep Learning, Neural Network, Blockchain, with the tools MeSH (Medical Subject Headings), AND, OR, and the characters [,“,; /., to ensure the best review topics. We concluded that this pandemic lastly consolidates the new technologies era and will change the whole way of the social life of human beings. Also, a big jump in medicine will happen on procedures, protocols, drug designs, attendances, encompassing all health areas, as well as in social and business behaviors.

Pedro Junior Zucatelli, Erick Giovani Sperandio Nascimento, A.Á.B. Santos, D.M. Moreira (2020)Nowcasting prediction of wind speed using computational intelligence and wavelet in Brazil, In: International Journal for Computational Methods in Engineering Science and Mechanics21(6)pp. 343-369 Taylor & Francis

This work presents a novel investigation on the nowcasting prediction of wind speed for three sites in Bahia, Brazil. For this, it was applied the computational intelligence by supervised machine learning using different artificial neural network technique, which was trained, validated, and tested using time series are derived from measurements that are acquired in towers equipped with anemometers at heights of 100.0, 120.0 and 150.0 m. To define the most efficient ANN, different topologies were tested using MLP and RNN, applying Wavelet packet decomposition (bior, coif, db, dmey, rbior, sym). The best statistical analysis was RNN + discrete Meyer wavelet.

José Vicente Cardoso Santos, Noéle Bissoli Perini, Marcelo Albano Moret, Erick Giovani Sperandio Nascimento, D.M. Moreira (2021)Scaling behavior of wind speed in the coast of Brazil and the South Atlantic Ocean: The crossover phenomenon, In: Energy217119413 Elsevier

Meteorological data collected using ocean buoys are very important for weather forecasting. In addition, they provide valuable information on ocean–atmosphere interaction processes that have not yet been explored. Accordingly, data collection using ocean buoys is well established around the world. In Brazil, ocean buoy data are obtained by the Brazilian Navy through a monitoring network on the Brazilian coast, which has high potential for wind power generation. In this context, the present study aimed to analyze the scaling behavior of wind speed on the Brazilian coast (continental shelf), South Atlantic Ocean and coast of Africa in order to determine long-range correlations and acquire more information on the crossover phenomenon at various scales. For this purpose, the detrended fluctuation analysis technique and numerical simulation with the Weather Research and Forecasting mesoscale model were used. The results from buoys show that wind speed exhibits a scaling behavior, but without the crossover phenomenon in the Brazilian coast, South Atlantic Ocean and coast of Africa, indicating the dependence of the phenomenon by the terrestrial surface, suggesting influence on the wind power generation. Buoy data from the South Atlantic Ocean and coast Africa showed a subdiffusive behavior (α≥1), whereas those from the Brazilian coast indicated persistence (0.5

Pedro Junior Zucatelli, Erick Giovani Sperandio Nascimento, A.Á.B. Santos, A.M.G. Arce, D.M. Moreira (2021)An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay, In: Energy230120842 Elsevier

Large variations in wind energy production over a period of minutes to hours is a challenge for electricity balancing authorities. The use of reliable tools for the prediction of wind power and wind power ramp events is essential for the operator of the electrical system. The main objective of this work is to analyze the wind power and wind power ramp forecasting at Brazil and Uruguay. To achieve this goal the wavelet decomposition applying 48 different mother wavelet functions and deep learning techniques are used. The recurrent neural network was trained to perform the forecasting of 1 h ahead, and then, using it, the trained network was applied to recursively infer the forecasting for the next hours of the wind speed. After this computational procedure, the wind power and the wind power ramp were predicted. The results showed good accuracy and can be used as a tool to help national grid operators for the energy supply. The wavelet discrete Meyer family (dmey) demonstrates greater precision in the decomposition of the wind speed signals. Therefore, it is proven that the wavelet dmey is the most accurate in the decomposition of temporal wind signals, whether using signals from tropical or subtropical regions. •Nowcasting wind prediction in tropical and subtropical sites using AI and Wavelet.•An ANN approach for the estimation of wind power ramp using deep learning.•Modeling of wind using atmospheric factors in tropical and subtropical sites.•Wind power and wind power ramp forecasting applying 48 mother Wavelet functions.

Andre Luis da Cunha Dantas Lima, Vitor Moraes Aranha, Caio Jordao de Lima Carvalho, Erick Giovani Sperandio Nascimento (2021)Smart predictive maintenance for high-performance computing systems: a literature review, In: The Journal of Supercomputing77pp. 13494-13513 Springer

Predictive maintenance is an invaluable tool to preserve the health of mission critical assets while minimizing the operational costs of scheduled intervention. Artificial intelligence techniques have been shown to be effective at treating large volumes of data, such as the ones collected by the sensors typically present in equipment. In this work, we aim to identify and summarize existing publications in the field of predictive maintenance that explore machine learning and deep learning algorithms to improve the performance of failure classification and detection. We show a significant upward trend in the use of deep learning methods of sensor data collected by mission critical assets for early failure detection to assist predictive maintenance schedules. We also identify aspects that require further investigation in future works, regarding exploration of life support systems for supercomputing assets and standardization of performance metrics.

Noéle Bissoli Perini de Souza, Erick Giovani Sperandio Nascimento, Alex Alisson Bandeira Santos, Davidson Martins Moreira (2022)Wind mapping using the mesoscale WRF model in a tropical region of Brazil, In: Energy240122491 Elsevier

This study details an evaluation of the onshore and offshore wind speed field in the state of Bahia, northeastern Brazil, using the WRF (Weather Research and Forecasting) mesoscale model, version 4.0. The simulations were run for a period of five years—between 2015 and 2020—with a horizontal resolution of 3 km, and were compared with data from 41 automatic surface stations for the onshore case. For the offshore case, data from a surface station located in the Abrolhos Archipelago were used. The winter period presents higher values of wind speed for the onshore region (9–14 m/s), and the northern and southwestern regions of the state stand out for the generation of wind energy. In the offshore case, spring presents the highest averages for wind speed (7–8 m/s), followed by the summer season, highlighting the maritime coast in the extreme south of the state (7–10 m/s). Furthermore, the nocturnal wind regime is more intense than the daytime one, indicating a great complementarity with solar energy. The year 2017 had the highest average values of wind speed in the region, being considered one of the warmest years without the influence of the El Nino phenomenon recorded globally since 1850. The hourly averages of onshore and offshore winds for the state of Bahia demonstrated the great wind potential of the region, with high and medium speeds at altitude, which were in accordance with the minimum attractiveness thresholds for investments in wind energy generation.

Stephanie Lima Jorge Galvão , Júnia Cristina Ortiz Matos Ortiz Matos, Yasmin Kaore Lago Kitagawa, Flávio Santos Conterato, Davidson Martins Moreira, Prashant Kumar, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)Particulate Matter Forecasting Using Different Deep Neural Network Topologies and Wavelets for Feature Augmentation, In: Atmosphere13(9)1451 MDPI

The concern about air pollution in urban areas has substantially increased worldwide. One of its main components, particulate matter (PM) with aerodynamic diameter of ≤2.5 µm (PM2.5), can be inhaled and deposited in deeper regions of the respiratory system, causing adverse effects on human health, which are even more harmful to children. In this sense, the use of deterministic and stochastic models has become a key tool for predicting atmospheric behavior and, thus, providing information for decision makers to adopt preventive actions to mitigate air pollution impacts. However, stochastic models present their own strengths and weaknesses. To overcome some of disadvantages of deterministic models, there has been an increasing interest in the use of deep learning, due to its simpler implementation and its success on multiple tasks, including time series and air quality forecasting. Thus, the objective of the present study is to develop and evaluate the use of four different topologies of deep artificial neural networks (DNNs), analyzing the impact of feature augmentation in the prediction of PM2.5 concentrations by using five levels of discrete wavelet transform (DWT). The following types of deep neural networks were trained and tested on data collected from two living lab stations next to high-traffic roads in Guildford, UK: multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolutional neural network (1D-CNN) and a hybrid neural network composed of LSTM and 1D-CNN. The performance of each model in making predictions up to twenty-four hours ahead was quantitatively assessed through statistical metrics. The results show that wavelets improved the forecasting results and that discrete wavelet transform is a relevant tool to enhance the performance of DNN topologies, with special emphasis on the hybrid topology that achieved the best results among the applied models.

José Roberto Dantas da Silva Júnior, Rizzieri Pedruzzi, Filipe Milani de Souza, Patrick Silva Ferraz, Daniel Guimarães Silva, Carolina Sacramento Vieira, Marcelo Romero de Moraes, Erick Giovani Sperandio Nascimento, Davidson Martins Moreira (2021)Feasibility analysis on the construction of a web solution for hydrometeorological forecasting considering water body management and indicators for the SARS-COV-2 pandemic, In: AI Perspectives3(1) Springer

The current scenario of a global pandemic caused by the virus SARS-CoV-2 (COVID19), highlights the importance of water studies in sewage systems. In Brazil, about 35 million Brazilians still do not have treated water and more than 100 million do not have basic sanitation. These people, already exposed to a range of diseases, are among the most vulnerable to COVID-19. According to studies, places that have poor sanitation allow the proliferation of the coronavirus, been observed a greater number of infected people being found in these regions. This social problem is strongly related to the lack of effective management of water resources, since they are the sources for the population's water supply and the recipients of effluents stemming from sanitation services (household effluents, urban drainage and solid waste). In this context, studies are needed to develop technologies and methodologies to improve the management of water resources. The application of tools such as artificial intelligence and hydrometeorological models are emerging as a promising alternative to meet the world's needs in water resources planning, assessment of environmental impacts on a region's hydrology, risk prediction and mitigation. The main model of this type, WRF-Hydro Weather Research and Forecasting Model), represents the state of the art regarding water resources, as well as being the object of study of small and medium-sized river basins that tend to have less water availability. hydrometeorological data and analysis. Thus, this article aims to analyze the feasibility of a web tool for greater software usability and computational cost use, making it possible to use the WRF-Hydro model integrated with Artificial Intelligence tools for short and medium term, optimizing the time of simulations with reduced computational cost, so that it is able to monitor and generate a predictive analysis of water bodies in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), constituting an instrument for water resources management. The results obtained show that the WRF-Hydro model proves to be an efficient computational tool in hydrometeorological simulation, with great potential for operational, research and technological development purposes, being considered viable to implement the web tool for analysis and management of water resources and consequently, assist in monitoring and mitigating the number of cases related to the current COVID-19 pandemic. This research are in development and represents a preliminary results with future perspectives.

Roberto M. Souza, ERICK GIOVANI SPERANDIO NASCIMENTO, Ubatan A. Miranda, Wenisten J. D. Silva, Herman A. Lepikson (2021)Deep learning for diagnosis and classification of faults in industrial rotating machinery, In: Computers & industrial engineering153107060 Elsevier

Application of deep-learning techniques has been increasing, which redefines state-of-the-art technology, especially in industrial applications such as fault diagnosis and classification. Therefore, implementing a system that can automatically detect faults at an early stage and recommend stopping of a machine to avoid unsafe conditions in the process and environment has become possible. This paper proposes the use of Predictive Maintenance model with Convolutional Neural Network (PdM-CNN), to classify automatically rotating equipment faults and advise when maintenance actions should be taken. This work uses data from only one vibration sensor installed on the motor-drive end bearing, which is the most common layout present in the industry. This work was developed under controlled ambient varying rotational speeds, load levels and severities, in order to verify whether it is possible to build a model capable of classifying such faults in rotating machinery using only one set of vibration sensors. The results showed that the accuracies of the PdM-CNN model were of 99.58% and 97.3%, when applied to two different publicly available databases. This demonstrates the model's ability to accurately detect and classify faults in industrial rotating machinery. With this model companies can improve the financial performance of their rotating machine monitoring through reducing sensor acquisition costs for fault identification and classification problems, easing their way towards the digital transformation required for the fourth industrial revolution.

Adhvan Furtado, Carlos Alberto Campos da Purificação, Roberto Badaró, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)A Light Deep Learning Algorithm for CT Diagnosis of COVID-19 Pneumonia, In: Diagnostics (Basel)12(7)1527

A large number of reports present artificial intelligence (AI) algorithms, which support pneumonia detection caused by COVID-19 from chest CT (computed tomography) scans. Only a few studies provided access to the source code, which limits the analysis of the out-of-distribution generalization ability. This study presents Cimatec-CovNet-19, a new light 3D convolutional neural network inspired by the VGG16 architecture that supports COVID-19 identification from chest CT scans. We trained the algorithm with a dataset of 3000 CT Scans (1500 COVID-19-positive) with images from different parts of the world, enhanced with 3000 images obtained with data augmentation techniques. We introduced a novel pre-processing approach to perform a slice-wise selection based solely on the lung CT masks and an empirically chosen threshold for the very first slice. It required only 16 slices from a CT examination to identify COVID-19. The model achieved a recall of 0.88, specificity of 0.88, ROC-AUC of 0.95, PR-AUC of 0.95, and F1-score of 0.88 on a test set with 414 samples (207 COVID-19). These results support Cimatec-CovNet-19 as a good and light screening tool for COVID-19 patients. The whole code is freely available for the scientific community.

Raphael Souza de Oliveira, Amilton Sales Reis, Jr, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)Predicting the number of days in court cases using artificial intelligence, In: PloS one17(5)e0269008

Brazilian legal system prescribes means of ensuring the prompt processing of court cases, such as the principle of reasonable process duration, the principle of celerity, procedural economy, and due legal process, with a view to optimizing procedural progress. In this context, one of the great challenges of the Brazilian judiciary is to predict the duration of legal cases based on information such as the judge, lawyers, parties involved, subject, monetary values of the case, starting date of the case, etc. Recently, there has been great interest in estimating the duration of various types of events using artificial intelligence algorithms to predict future behaviors based on time series. Thus, this study presents a proof-of-concept for creating and demonstrating a mechanism for predicting the amount of time, after the case is argued in court (time when a case is made available for the magistrate to make the decision), for the magistrate to issue a ruling. Cases from a Regional Labor Court were used as the database, with preparation data in two ways (original and discretization), to test seven machine learning techniques (i) Multilayer Perceptron (MLP); (ii) Gradient Boosting; (iii) Adaboost; (iv) Regressive Stacking; (v) Stacking Regressor with MLP; (vi) Regressive Stacking with Gradient Boosting; and (vii) Support Vector Regression (SVR), and determine which gives the best results. After executing the runs, it was identified that the adaboost technique excelled in the task of estimating the duration for issuing a ruling, as it had the best performance among the tested techniques. Thus, this study shows that it is possible to use machine learning techniques to perform this type of prediction, for the test data set, with an R2 of 0.819 and when transformed into levels, an accuracy of 84%.

Tiago Palma Pagano, Victor Rocha Santos, Yasmin da Silva Bonfim, José Vinícius Dantas Paranhos, Lucas Lemos Ortega, Paulo Henrique Miranda Sá, Lian Filipe Santana Nascimento, Ingrid Winkler, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)Machine Learning Models and Videos of Facial Regions for Estimating Heart Rate: A Review on Patents, Datasets, and Literature, In: Electronics (Basel)11(9)1473

Estimating heart rate is important for monitoring users in various situations. Estimates based on facial videos are increasingly being researched because they allow the monitoring of cardiac information in a non-invasive way and because the devices are simpler, as they require only cameras that capture the user’s face. From these videos of the user’s face, machine learning can estimate heart rate. This study investigates the benefits and challenges of using machine learning models to estimate heart rate from facial videos through patents, datasets, and article review. We have searched the Derwent Innovation, IEEE Xplore, Scopus, and Web of Science knowledge bases and identified seven patent filings, eleven datasets, and twenty articles on heart rate, photoplethysmography, or electrocardiogram data. In terms of patents, we note the advantages of inventions related to heart rate estimation, as described by the authors. In terms of datasets, we have discovered that most of them are for academic purposes and with different signs and annotations that allow coverage for subjects other than heartbeat estimation. In terms of articles, we have discovered techniques, such as extracting regions of interest for heart rate reading and using video magnification for small motion extraction, and models, such as EVM-CNN and VGG-16, that extract the observed individual’s heart rate, the best regions of interest for signal extraction, and ways to process them.

Lucas Lisboa dos Santos, Ingrid Winkler, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)RL-SSI Model: Adapting a Supervised Learning Approach to a Semi-Supervised Approach for Human Action Recognition, In: Electronics (Basel)11(9)1471

Generally, the action recognition task requires a vast amount of labeled data, which represents a time-consuming human annotation effort. To mitigate the dependency on labeled data, this study proposes Semi-Supervised and Iterative Reinforcement Learning (RL-SSI), which adapts a supervised approach that uses 100% labeled data to a semi-supervised and iterative approach using reinforcement learning for human action recognition in videos. The JIGSAWS and Breakfast datasets were used to evaluate the RL-SSI model, because they are commonly used in the action segmentation task. The same applies to the performance metrics used in this work-F-Score (F1) and Edit Score-which are commonly applied for such tasks. In JIGSAWS tests, we observed that the RL-SSI outperformed previously developed state-of-the-art techniques in all quantitative measures, while using only 65% of the labeled data. When analysing the Breakfast tests, we compared the effectiveness of RL-SSI with the results of the self-supervised technique called SSTDA. We have found that RL-SSI outperformed SSTDA with an accuracy of 66.44% versus 65.8%, but RL-SSI was surpassed by the F1@10 segmentation measure, which presented an accuracy of 67.33% versus 69.3% for SSTDA. Despite this, our experiment only used 55.8% of the labeled data, while SSTDA used 65%. We conclude that our approach outperformed equivalent supervised learning methods and is comparable to SSTDA, when evaluated on multiple datasets of human action recognition, proving to be an important innovative method to successfully building solutions to reduce the amount of fully labeled data, leveraging the work of human specialists in the task of data labeling of videos, and their respectives frames, for human action recognition, thus reducing the required resources to accomplish it.

Adhvan Furtado, Leandro Andrade, Diego Frias, Thiago Maia, Roberto Badaró, ERICK GIOVANI SPERANDIO NASCIMENTO (2022)Deep Learning Applied to Chest Radiograph Classification—A COVID-19 Pneumonia Experience, In: Applied sciences12(8)3712 MDPI

Due to the recent COVID-19 pandemic, a large number of reports present deep learning algorithms that support the detection of pneumonia caused by COVID-19 in chest radiographs. Few studies have provided the complete source code, limiting testing and reproducibility on different datasets. This work presents Cimatec_XCOV19, a novel deep learning system inspired by the Inception-V3 architecture that is able to (i) support the identification of abnormal chest radiographs and (ii) classify the abnormal radiographs as suggestive of COVID-19. The training dataset has 44,031 images with 2917 COVID-19 cases, one of the largest datasets in recent literature. We organized and published an external validation dataset of 1158 chest radiographs from a Brazilian hospital. Two experienced radiologists independently evaluated the radiographs. The Cimatec_XCOV19 algorithm obtained a sensitivity of 0.85, specificity of 0.82, and AUC ROC of 0.93. We compared the AUC ROC of our algorithm with a well-known public solution and did not find a statistically relevant difference between both performances. We provide full access to the code and the test dataset, enabling this work to be used as a tool for supporting the fast screening of COVID-19 on chest X-ray exams, serving as a reference for educators, and supporting further algorithm enhancements.

Noéle Bissoli Perini de Souza, José Vicente Cardoso dos Santos, ERICK GIOVANI SPERANDIO NASCIMENTO, Alex Alisson Bandeira Santos, Davidson Martins Moreira (2022)Long-range correlations of the wind speed in a northeast region of Brazil, In: Energy (Oxford)243122742 Elsevier Ltd

The objective of this work is to analyze the scaling behavior of wind speed in the region of the state of Bahia, northeastern Brazil, in search of long-range correlations and other information about the crossover phenomena. Thus, data from 41 automatic surface stations were used for a period of five years—between 2015 and 2020—for onshore reading. For offshore readings, data from a surface station located in the Abrolhos Archipelago were used. To achieve this goal, the DFA (detrended fluctuation analysis) technique was used in the analysis of measured data at the stations, along with numerical simulations using the WRF (weather research and forecasting) mesoscale model. The results of the analysis of hourly average wind speed from the measured and simulated data show the existence of scale behavior with the appearance, in most cases, of a double crossover—onshore and offshore. This suggests the phenomenon's dependence on the time period of the analyzed data, and also on the geographic location, showing a strong correlation with the Atlantic and Pacific oscillations (La Niña and El Niño), indicating the influence of local, mesoscale, and macroscale effects in the region of study. For the offshore case, the measured data and simulations presented a subdiffusive behavior (α≥1) before the first crossover, and persistence (0.5