Two-phase flow bubble detection method applied to natural circulation system using fuzzy image processing

Bueno, Regis Cortez; Masotti, P.; Justo, J. F.; Andrade, Delvonei Alves de; Rocha, Manoel de Souza; Torres, Walmir M.; Mesquita, Roberto Navarro de

doi:10.1016/j.nucengdes.2018.05.026

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…In [99], the authors implemented image pattern classification systems based on simple features using a fuzzy inference system. The TPF regime identification system to manage heat transfer of the nuclear reactor coolant was designed in [23]. The image segmentation process was used to divide the image into sub-regions and then the fuzzy image processing was applied to detect bubbles in the natural circulation system.…”

Section: Flow Regime Identificationmentioning

confidence: 99%

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Wajman

2019

ITC

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Nowadays, the application of advanced technologies in modern production systems is the main trend of development and technological progress in many industrial sectors. It is due to the still growing trends of energy-saving and production quality enhancement. Wherever in the production process the phase mixture is transported and it is not optimal or not economic, there is a need to develop a system which would be able to prevent any construction disaster, unexpected production line stopping or situation where for reasons of bad flow parameters, the final product is defective. This paper studies various sensors, measurement techniques and computer methods for signal processing and analysis to diagnose and control two-phase flows. Due to the possibility that the invasive measurement disturbs the process and changes it parameters and behavior especially in the location just after the measurement point and simultaneously does not provide any information about these changes it is unreliable for the diagnosis or control. Therefore, the non-invasive techniques commonly used for measurement of flows parameters are described. Depending on the industrial demands many of applications examples for non-invasive two-phase flows measurement and monitoring are given. This description for identifying the flow parameters is divided into features categories of this phenomena as void fraction distribution, velocity profile and flow regime. However, from these methods the high accuracy and short processing time is expected. The continued observation and monitoring of the process abnormalities can provide valuable information about its dynamic state and allow for real-time and automatic monitoring and control. Therefore, the development of advanced process control is one of the most important challenges to keep the flow regime on the given level and for instant and long-term energy saving, quality improvement.

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Section: Flow Regime Identificationmentioning

confidence: 99%

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Wajman

2019

ITC

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“…In CNN approaches, the image is typically processed without addressing the low contrast and speckle noise problems to assess the ability of CNNs to perform edge detection without image preprocessing. Furthermore, edge detection methods based on fuzzy logic techniques were previously applied on images but not medical images [8], [9], [10] and medical image [11], [12], [13]. However, in these techniques, several grey level colors have been detected in the output image.…”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images

Jabbar

Day

Chadwick

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Edge detection in Musculoskeletal Ultrasound Imaging readily allows an ultrasound image to be rendered as a binary image. This facilitates automated measurement of geometric parameters, such as muscle thickness, circumference and cross-sectional area of the tendon. In this work, we introduced a new method of edge detection based on a fuzzy inference system and apply it to the ultrasound image. An anisotropic diffusion filter was used to reduce speckle noise before implementation of the edge detection method, which consists of three characteristic steps. The first step entailed fuzzification, for which three fuzzy membership functions were applied to the image. The parameters of these functions were selected based on an analysis of the standard deviation of grey level intensities in the image. Secondly, 12 fuzzy rules for identifying edges were constructed. Thirdly, defuzzification was carried out using the Takagi-Sugeno method. Furthermore, a reference-based edge measurement was quantitatively determined by comparing edge characteristics with a standard reference. We made two inferences from our observations. Firstly, the ability to automatically identify the important details of a musculoskeletal ultrasound image in a very short time is possible. Secondly, this method is effective compared with other methods.

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“…O estudo do fluxo bifásico foi relacionado por FU e LIU (2016) para prever o valor médio e a densidade do número de bolhas com o objetivo de mensurar a fração de vazio resultante do processo. O escoamento bifásico tem sido objeto de estudo em diversos trabalhos com base na aquisição e análise de imagens digitais (BUENO et al, 2018).…”

Section: Introductionunclassified

Detecção de borda em imagens de escoamento bifásico de um circuito experimental de circulação natural pelo método FUNED (>i<Fuzzy Number Edge Detector)>/i<

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Ao meu orientador Dr. Roberto Navarro de Mesquita, por sua paciência, auxílio e aprendizado no desenvolvimento deste projeto e a todos os servidores e colaboradores do IPEN que muito prestativamente me auxiliaram todas as vezes que precisei neste percurso. RESUMOTEIXEIRA, A. L. d. M. Detecção de borda em imagens de escoamento bifásico de um circuito experimental de circulação natural pelo método FUNED (Fuzzy Number Edge Detector). 2023. 83 f. Dissertação (Mestrado em Tecnologia Nuclear), Instituto de Pesquisas Energéticas e Nucleares, IPEN-CNEN, São Paulo.Este trabalho aplicou o método FUNED (do inglês "Fuzzy Number Edge Detector") que é baseado em números Fuzzy para detecção de contornos em um conjunto de imagens digitais de escoamento bifásico obtidas em experimentos no Circuito de Circulação Natural (CCN) do Instituto de Pesquisas Energéticas e Nucleares (IPEN). O algoritmo se baseia na utilização de cada pixel da imagem adquirida como um número Fuzzy para gerar uma imagem que permita mostrar a pertinência do pixel em relação às regiões de borda e fundo. A imagem-pertinência gerada é utilizada como base para a identificação das bordas, onde um pixel branco significa valores acima do limiar e um pixel preto indica valores abaixo desse valor, processo conhecido como binarização da imagem. Após encontrar-se o limiar ótimo, é aplicada a técnica de supressão de não-máximos para criar uma borda mais fina. As imagens finais do método são comparadas com imagens idealmente classificadas (construídas pixel a pixel por um especialista). A mesma comparação foi realizada utilizando-se o clássico detector de bordas de Canny, permitindo-se mostrar a eficiência do detector de bordas FUNED. Os resultados mostraram que o método FUNED detectou corretamente entre 19% e 25% dos pixels que foram idealmente classificados como borda pelo especialista e, de forma complementar detectou corretamente entre 96% e 98% dos pixels classificados como fundo. O detector de Canny foi aplicado nas mesmas imagens e sob a mesma análise, apresentou um percentual de acertos entre 25% e 39% na predição do que era borda e complementarmente, entre 98% e 99% do que era fundo.

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Two-phase flow bubble detection method applied to natural circulation system using fuzzy image processing

Cited by 16 publications

References 29 publications

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Computer methods for non-invasive measurement and control of two-phase flows: a review study

Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images

Detecção de borda em imagens de escoamento bifásico de um circuito experimental de circulação natural pelo método FUNED (>i<Fuzzy Number Edge Detector)>/i<

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