Iterative reconstruction algorithm for the inverse problems in electrical capacitance tomography

Guo, Gao; Tong, Guowei; Lu, Lian; Liu, Shi

doi:10.1016/j.flowmeasinst.2018.10.010

Cited by 37 publications

(11 citation statements)

References 38 publications

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“…Suppose x is the vector of permittivity distribution, y is the vector of capacitance data, x is the distribution of reconstructed permittivity, and y is the estimated data capacitance which is calculated by the distribution of permittivity [28]. en, the autoencoder (Figure 5) (code and decode) behave like…”

Section: Deepmentioning

confidence: 99%

Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning

Zhang

Chen

2021

Complexity

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For great achievements in recent decades, image reconstruction for electrical capacitance tomography (ECT) has been considered in this study. ECT has demonstrated impressive potentials in multiprocess measurement, and the obtained images are of high resolution, which are suitable for advanced procedures in industrial and medical applications and across different tasks and domains. But the ECT system still requires improvements in the quality of image reconstruction given its importance of great significance to obtain the reliability and usefulness of measurement results. The deep neural network is used in this study to extract new features and to update the number of nodes and hidden layers in the system. Recently, deep learning exhibits suitable solutions in many flourishing fields based on different series of artificial neural networks for mapping nonlinear functions. To address the obstacles, this paper proposes an imaging method using an optimizer reconstruction model. An optimization model for imaging is generated as a powerful optimizer for building a computational model to ameliorate the reconstruction accuracy. Based on the deep learning methodology, the previous images reconstructed by using one of the imaging techniques to the required images are abstracted and stored in the deep learning machine, resulting in an error rate of 8.9%, and this is considered good on ECT. Therefore, an artificial neural network of the capacitance (ANNoC) system is introduced to estimate capacitance measurements.

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Section: Deepmentioning

confidence: 99%

Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning

Zhang

Chen

2021

Complexity

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“…Eight experimental technical documents were published on the information website [139]. Later scholars mainly studied image reconstruction algorithms, improving signal-to-noise ratio and optimizing sensors [140][141][142].…”

Section: Electrical Capacitance Tomographymentioning

confidence: 99%

“…(1) The speed of image algorithm reconstruction has been improved. The L1 regularization-based second-order total variation (LSTV) algorithm proposed by G Guo et al [140] ensures the sparsity of the reconstructed object, reduces the staircase effect brought by the first order total variation (FTV) regularization, and improves the estimation reliability. Cao et al [145] proposed Calderon's method combined with a closed-loop control strategy for reconstructed images from noise-free data.…”

Section: Electrical Capacitance Tomographymentioning

confidence: 99%

Current Trends and Perspectives of Detection and Location for Buried Non-Metallic Pipelines

Zhang

Tian

et al. 2021

Chin. J. Mech. Eng.

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Buried pipelines are an essential component of the urban infrastructure of modern cities. Traditional buried pipes are mainly made of metal materials. With the development of material science and technology in recent years, non-metallic pipes, such as plastic pipes, ceramic pipes, and concrete pipes, are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply, drainage, heat, industry, oil, and gas. The location technologies for the location of the buried metal pipeline have become mature, but detection and location technologies for the non-metallic pipelines are still developing. In this paper, current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized. Initially, this paper reviews and analyzes electromagnetic induction technologies, electromagnetic wave technologies, and other physics-based technologies. It then focuses on acoustic detection and location technologies, and finally introduces emerging technologies. Then the technical characteristics of each detection and location method have been compared, with their strengths and weaknesses identified. The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined. Finally, some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.

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“…Se detectó la importancia de esta variable en la predicción de la generación de V. Para elaborar el mejor modelo de V en función de 1/h, se fijó la variable independiente h y la I fue aleatoria. Para cada valor fijo de h se obtuvo muchos valores de V, por lo que se recurrió a estimar un valor medio de V para cada valor de h. A estos valores registrados y estimados, se les realizó la prueba de normalidad de Shapiro-Wilk [18], reflejando una distribución normal y no se requiere ajustes al modelo. La ecuación de la regresión lineal modelo lineal resultante utilizando la ecuación 1, se presenta en la siguiente ecuación (ver ecuación 4): ܸ ൌ 5.5143ሺ1/ℎሻ 0.26944…”

Section: Modelamiento De Las Respuestas De V En Función De La 1/hunclassified

Captación del campo magnético de líneas eléctricas para uso doméstico en zonas rurales de Panamá

Morales

Araúz

Atencio

et al. 2021

RIC

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En la actualidad, en los países en vías de desarrollo, hay comunidades de bajos recursos económicos, con difíciles o inexistentes vías de comunicación terrestre y que no poseen fluido eléctrico convencional. El objetivo del estudio fue desarrollar un prototipo, con un método eficiente y sostenible, para generar energía eléctrica, cuya fuente de origen sean las líneas de transmisión y distribución que atraviesan las zonas rurales. La innovación metodológica fue no convencional, sin contacto físico, con bajos riesgos y costos, aumentando la cobertura eléctrica en estas regiones, y con ello la calidad de vida de sus habitantes. El diseño integral del prototipo experimental permitió realizar las variaciones programáticas de la corriente (I), con un diseño eficiente del elevador para evaluar diferentes alturas (h) del conductor-fuente (simulando una línea eléctrica real), con la bobina receptora (dispositivo de captura del campo magnético). Los análisis de los datos registrados reflejaron que, al utilizar el regresor 1/h, se detectó la respuesta significativa en V, resultante por la ecuación V= 5.5143(1/h) + 0.26944, con R2=0.993 (p<0.01). Al detectarse residuos dispersos y faltas de ajustes al modelo lineal, se aplicó la regresión cuadrática con los predictores 1/h y 1/h2, con R2=0.999 (p<0.01), que permitieron estimar la generación de V en el prototipo. Se obtuvieron voltajes entre 1 a 10 voltios de corriente alterna (AC). Se logró el encendido de bombillos tipo leds y cargas de baja potencia eléctrica.

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Iterative reconstruction algorithm for the inverse problems in electrical capacitance tomography

Cited by 37 publications

References 38 publications

Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning

Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning

Current Trends and Perspectives of Detection and Location for Buried Non-Metallic Pipelines

Captación del campo magnético de líneas eléctricas para uso doméstico en zonas rurales de Panamá

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