ROBERTO NAVARRO DE MESQUITA

ROBERTO NAVARRO DE MESQUITA

(Fonte: Lattes)

Resumo

Possui graduação em Física pela Universidade Estadual de Campinas (1987), mestrado em Física pela Universidade Estadual de Campinas (1991) e doutorado em Engenharia Mecânica pela Universidade de São Paulo (2002). Atualmente é tecnologista pleno da Comissão Nacional de Energia Nuclear. Tem experiência na área de Ciência da Computação, com ênfase em Sistemas de Inteligência Artificial, atuando principalmente nos seguintes temas: inteligência artificial, diagnóstico de defeitos em tubos, correntes parasitas (ECT), reconhecimento de padrões em imagens. (Texto extraído do Currículo Lattes em 27 dez. 2021)

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Explainable Artificial Intelligence applied to images of two-phase flow
2024 - SCHOTT, S.M.C.; MESQUITA, R.N. de
Classification of two-phase flow instability phases using convolutional neural networks
2021 - SCHOTT, S.M.C.; SILVA, M.C.B. da; MESQUITA, R.N. de
Characterization of polyacrylonitrile thermal stabilization process for carbon fiber production using intelligent algorithms
2021 - TERRA, BRUNA M.; ANDRADE, DELVONEI A. de; MESQUITA, ROBERTO N. de
Composite materials have widened their application range in recent years. The polymeric composite reinforced with carbon fibers can be described as a high-performance structural material which merges two important features: low weight and mechanical stability. Carbon fiber production which uses polyacrylonitrile as precursor is composed of many stages such as polymerization, spinning, thermal stabilization, carbonization, and surface treatment. Thermal stabilization is the critical stage of this production process, during which aromatic rings are generated, and therefore the main factor for the carbon fiber structure definition and thus for this material quality. A thermal stabilization model using intelligent algorithms was developed aiming a possible optimization of the production process and consequent cost reduction. This work was based on real experimental data obtained from a composite material production pilot plant. A qualitative analysis was initially performed using Self-Organizing Maps trained with variables of fiber production reagents and process. Thereafter, a supervised training with feedforward backpropagation neural network was used for a quantitative analysis. Based on this quantitative analysis, the carbon fiber thermal stabilization process was simulated, obtaining 2.98% and 2.48% mean errors relative to experimental results of Volumetric Density and FTIR Conversion index, respectively.
Nutritional evaluation of Brachiaria brizantha cv. marandu using convolutional neural networks
2020 - DAL PRÁ, BRUNO R.; MESQUITA, ROBERTO N. de; MENEZES, MARIO O. de; ANDRADE, DELVONEI A. de
The identification of plant nutritional stress based on visual symptoms is predominantly done manually and is performed by trained specialists to identify such anomalies. In addition, this process tends to be very time consuming, has a variability between crop areas and is often required for analysis at various points of the property. This work proposes an image recognition system that analyzes the nutritional status of the plant to help solve these problems. The methodology uses deep learning that automates the process of identifying and classifying nutritional stress of Brachiaria brizantha cv. marandu. An image recognition system was built and analyzes the nutritional status of the plant using the digital images of its leaves. The system identifies and classifies Nitrogen and Potassium deficiencies. Upon receiving the image of the pasture leaf, after a classification performed by a convolutional neural network (CNN), the system presents the result of the diagnosed nutritional status. Tests performed to identify the nutritional status of the leaves presented an accuracy of 96%. We are working to expand the data of the image database to obtain an increase in the accuracy levels, aiming at the training with a larger amount of information presented to CNN and, thus, obtaining results that are more expressive.
Development of a real-time focus estimaton software to be applied in two-phase flow imaging using intelligent processing
2019 - SILVA, MARCONES C.B. da; SCHOTT, SANDRO M.C.; MESQUITA, ROBERTO N. de
Image processing has been an increasing research area in the last decades, especially due to crescent technological growth allied with lowering production costs. Many scientific applications have searched for establishment of quality norms associated with possible information obtainment from images. A common need from different applications has been the standardization of focus quality metric. The development of new methods for measuring the focus adjustment in order to obtain image quality metric analysis has enabled more reliable and precise data in many different industry and science sectors. Some examples are industrial equipment parts inspection using computational vision to defects classification. This work presents the initial steps to develop a methodology to estimate focus in real time in two-phase flow experiments inside tube with cylindrical geometry. This methodology is initially based on a software module using artificial intelligence methods to estimate image focus. This module is developed in LabVIEW platform using Fuzzy Logic inference base in different traditional digital focus metrics and integrated with digital cameras to increment precision on focus adjustment during two-phase flow experiments. This method will be calibrated to be used on void fraction estimation through image analysis in the natural circulation loop located at the Nuclear Engineering Center (CEN) do Instituto de Pesquisas Energéticas e Nucleares (IPEN). A set of the initial developed software modules will be presented with their respective functionalities, initial results and experimental focus estimated errors.
Process sensors characterization based on noise analysis techniques and artificial inteligence
2005 - MESQUITA, R.N.; PERILLO, S.R.P.; SANTOS, R.C.
Development of a system for monitoring and diagnosis of steam generator tubes using artificial intelligence techniques on Eddy Current Test signals
2002 - MESQUITA, R.N.; TING, D.K.S.; CABRAL, E.L.L.; MARTIN LOPEZ, L.A.N.; UPADHYAYA, B.R.