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CAMPUS GOIÁS ( CG) 198
REGIÃO METROPOLITANA DE GOIÂNIA (RMG) 19581
Campus Samambaia, Campus Colemar Natal e Silva, Campus Aparecida de Goiânia.

Submissões Recentes

Mean square relative displacements in a flat graphene monolayer
(2025) Rodrigues, Clóves Gonçalves; Calixto, Wesley Pacheco; Rabelo, José Nicodemos Teixeira
The mean square relative atomic displacements (MSRD) in a flat graphene monolayer are investigated in the approximation of weak anharmonicity. Numerical results, for not very high temperatures, are calculated using a parametric interatomic potential constructed specifically for graphene. In summary, our results show an overall increase in the MSRD as the interatomic distances increase, the MSRD in the direction of the straight line connecting two pairs of atoms are smaller than perpendicular ones, and in comparison with other twodimensional lattices, the MSRD in graphene lattice are greater than those in the hexagonal and square lattices.
Control and stabilization of quadcopters subjected to propeller failures
(2025) Bulhões, Júnio Santos; Martins, Cristiane Lopes; Pacheco, Viviane Margarida Gomes; Magalhães, Alana da Silva; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This work develops an auxiliary control system based on sliding mode control in order to stabilize quadcopters in the event of propeller failures, a significant challenge in the operation of unmanned aircraft. The proposed approach includes the implementation of modern control techniques, extensively tested in both a nonlinear simulator and a testing platform, allowing the reproduction of scenarios with up to 30% power loss in one of the motors. The combination of detailed simulations and practical experiments demonstrates the efficiency of sliding mode control, which is able to mitigate the effects of failures by reducing deviations in the 𝜙 and 𝜃 angles by more than 80% at the initial moments and maintaining partial stability of the angle 𝜓. In addition to surpassing other approaches in terms of efficiency, the proposed method preserves the aircraft’s autonomy, offering a robust and practical solution for application in real operational environments, ensuring greater safety and reliability in quadcopter control.
Multidimensional robustness analysis for optimizing complex systems
(2025) Paiva, João Ricardo Braga de; Pacheco, Viviane Margarida Gomes; Bulhões, Júnio Santos; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This work proposes the development of a metric for the analysis of operational robustness in systems, focusing on performance, complexity, and stability as key components. The methodology integrates these factors, enabling the assessment of the system’s ability to meet its design requirements, its internal dynamics and external interactions, and its capacity to return to equilibrium after disturbances. The metric is applied in three case studies: an intensive care unit, process scheduling in operating systems, and traction and braking in electric vehicles. The results show that, in scenarios with higher robustness, the contributions of performance, complexity and stability are balanced, with performance contributing around 30% and complexity and stability each contributing approximately 35%. In contrast, scenarios with lower robustness exhibit greater variation in the contributions of these components. These findings suggest that the proposed metric is an efficient tool for both quantitative and qualitative analyses, providing more detailed perspectives for decision making in complex systems.
Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms
(2025) Silva, Carlos Leandro Borges da; Pires, Thyago Gumeratto; Silva Filho, Antonio Marcelino da; Bulhões, Júnio Santos; Belo, Orlando Manuel Oliveira; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50 Hz and 60 Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities.
Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: pilot study
(2025) Nogueira, Solange Amorim; Luz, Fernanda Ambrogi Barbosa da; Camargo, Thiago Fellipe Ortiz de; Oliveira, Júlio César Silveira; Campos Neto, Guilherme de Carvalho; Carvalhaes, Felipe Brazão Farinha; Reis, Márcio Rodrigues da Cunha; Santos, Paulo Victor dos; Mendes, Giovanna de Souza; Loureiro, Rafael Maffei; Calixto, Wesley Pacheco
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images. The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. The internal and external validation of the model corroborates its future applicability in real clinical scenarios, offering a new perspective in the analysis of myocardial perfusion scintigraphy images. The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction.