Research

Intelligent computing applied to social and environmental sustainability

Monitoring aquatic ecosystems is essential for understanding, predicting, and mitigating negative environmental impacts, contributing to biodiversity preservation and the sustainable use of water resources. This process involves collecting, analyzing, and interpreting data on water quality, current dynamics, and pollutant presence. In light of the growing challenges posed by the global environmental crisis, the application of computational intelligence has become a strategic approach to enhance the efficiency, scalability, and accuracy of monitoring systems. This project proposes the development and validation of intelligent technological solutions for aquatic environment monitoring, integrating environmental sensors, robotics, the Internet of Things (IoT), and machine learning algorithms. The main innovation lies in the use of autonomous sailboats as sustainable platforms for data collection, equipped with intelligent navigation capabilities in real-world environments. Computational intelligence algorithms will be implemented and evaluated for navigation control and environmental data analysis. The proposal aligns with the United Nations Sustainable Development Goals (SDGs) by providing accessible tools for environmental managers and by supporting the training of qualified professionals at the technical, undergraduate, and graduate levels. Expected outcomes include generating scientific knowledge, developing technological prototypes, and transferring technology through collaboration with the productive sector.

Team: Diego Brandão, Raphael Guerra (UFF), Esteban Clua (UFF), Luiz Marcos G. Gonçalves (UFRN), Ivanovich Lache Salcedo (UFF), Eduardo Vasconcelos (UFF), Rodrigo Toso (Microsoft), Angela Ferreira (IPB/Portugal), Kennedy Fernandes (UFJF), Fernando Carvalho (Cesar School, IFPE), Cosimo Distante (Univ. Salento),  Juliano Kazienko (UFSM).

 

Machine Learning Techniques for Anomaly Detection: Applications in Renewable Energy

This research project aims to develop and evaluate advanced machine learning methods for anomaly detection. Anomalies are events or patterns that significantly deviate from expected behavior in a dataset, and efficiently detecting them is critical across applications such as cybersecurity, fraud detection, and the monitoring of critical systems, including energy generation systems. Effective monitoring and maintenance are crucial to ensuring sustainable energy generation in energy systems. Early detection of anomalies, failures, or component degradation is essential to minimize disruptions, maximize efficiency, and extend the lifespan of renewable energy assets. This project will explore various machine learning algorithms, data preprocessing techniques, and performance evaluation strategies to improve the accuracy and effectiveness of anomaly detection in renewable energy systems, with a particular focus on wind energy.

Team: Diego Brandão, Rodrigo Toso (Microsoft), Angela Ferreira (IPB/Portugal), Rafaelli Coutinho (CEFET/RJ), Gustavo Semaan (UFF)

 

MultiSoils: a Framework for Soil Security

The MultiSoils´ objective is to host, connect, and share large amounts of curated soil data and knowledge at the Brazilian and South American levels. The e-infrastructure consists of several layers of services, a curated database of soil profiles, and a cloud-based computational framework for preparing, computing, and sharing soil data, integrated with map visualization tools. MultiSoils is an open, elastic, provenance-oriented, and lightweight computational e-infrastructure that collects, stores, describes, curates, harmonizes, and directs to various soil resource types: large datasets of soil profiles, services/applications, documents, projects, and external links. The map viewer allows users to navigate through key soil data for the region.

Team: Diego Brandão, Marcos Ceddia (UFRRJ), Jorge Soares (CEFET/RJ), Renato Mauro (CEFET/RJ), Frederico Machado (Petrobras)

 

Development of Computationally Efficient Models for Science and Engineering Applications 

This project aims to develop computationally efficient models for simulating problems in Science and Engineering. The reduction in computational costs for these problems will be achieved through the application of optimization techniques, adaptive approaches, and high-performance computing methods. The first group of techniques involves representing the problem using linear, integer, or mixed-integer programming models, leveraging exact methods and/or heuristics for problem-solving. The second group employs a local refinement approach to the problem’s computational domain, with a focus on graph-based techniques. In terms of high-performance computing, parallel processing techniques will be explored to reduce the simulation time of the methods developed by the other two groups. This project incorporates heuristics, metaheuristics, exact methods, and numerical techniques. This project includes addressing problems such as network optimization, which emphasizes routing and wavelength assignment problems and divisible task scheduling; operations research, which focuses on optimization problems in the energy sector; capacitated clustering; and combinatorial optimization problems. Adaptive and High-Performance Approaches emphasize numerical methods for simulating environmental, financial, and traffic problems, damage detection, and cancer growth modeling.

Team: Diego Brandão, Laura Assis (CEFET/RJ), Kennedy Fernandes (UFSB), Pedro Henrique Gonzalez (COPPE/UFRJ), 

 

Computational Modeling Applied to Environmental Problems: Theoretical and Simulation Aspects

This project aims to develop computationally efficient models for simulating environmental problems, specifically focusing on models that describe the behavior of a pollutant in a water body. The computational costs of classical methods will be reduced through adaptive mesh refinement and parallel processing. The first set of techniques uses tree-based models to represent the computational domain. In these models, the cost of obtaining neighborhood information is higher than uniform domain representation. To address this, graph-based representation techniques are proposed to reduce this cost. Regarding parallelism, the objective is to employ the Hopmoc method with a numerical flow controller. Finally, pattern recognition techniques will be applied to determine the best parameters for calibrating the methods developed.
 
Team: Diego Brandão,  Sanderson Lincohn Gonzaga de Oliveira (Unifesp), Edcarlos dos Santos (UFSB), Carla Osthoff (LNCC)
 
 

Computational Modeling and Sensor Networks for Environmental and Engineering Problems

The main objective of this research is to develop a sensor network-based monitoring system. The data obtained through this system will be used to calibrate mathematical models that describe environmental and engineering problems. Wireless Sensor Networks (WSNs) are an emerging technology where small devices, called sensors, are primarily used to monitor hard-to-reach or inhospitable areas, such as oceans, deserts, forests, aquatic niches, and industrial complexes. The use of WSNs for monitoring has been strongly developed over the past decades. This approach is particularly appealing compared to traditional methods. For instance, data collection processes are almost manual in monitoring river water quality. The developed system will provide essential data for the simulation of computational models and for the formulation and solution of inverse problems in environmental and engineering fields.

Team: Diego Brandão, Antonio José da Silva Neto (UERJ), Jader Lugon (IFF), Raphael Guerra (UFF), Fernando Carvalho (IFPE), Juliano Kazienko (UFSM), Silvio Quincozes (Unipampa)