Neural network for pattern association in electrical capacitance tomography

Nooralahiyan, Amir Y.; Hoyle, B.S.; Bailey, Nick

doi:10.1049/ip-cds:19941190

Cited by 24 publications

(21 citation statements)

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“…This has led to inaccurate oil fraction estimations. Since then, researchers have keenly developed various image reconstruction algorithms such as the Landweber [ 32 – 34 ] and Tikhonov [ 19 , 23 , 35 ] algorithms to produce better tomograms. Research works in image-based ECT measurement using an Artificial Neural Network (ANN) have also been carried out to improve the inaccuracy of the tomograms.…”

Section: Introductionmentioning

confidence: 99%

“…Research works in image-based ECT measurement using an Artificial Neural Network (ANN) have also been carried out to improve the inaccuracy of the tomograms. The work by Noralahiyan et al has shown that it is feasible to use the ANN technique to reconstruct more accurate images [ 23 – 26 ], however, the image reconstruction process is time consuming due to the requirement for intensive computations of hundreds of image pixels and hence, it is only suitable for offline data processing [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data

Zainal-Mokhtar

Mohamad–Saleh

2013

Sensors

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This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data

Zainal-Mokhtar

Mohamad–Saleh

2013

Sensors

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“…The ANN is a proven functional method for ECT image reconstruction. Several worldwide researchers have already reported promissory results by using this technique [8] [9] [11]. Experiments displayed in this paper have reached 0.24 maximum error image results.…”

Section: Discussionmentioning

confidence: 56%

An Application of Neural Networks for Image Reconstruction in Electrical Capacitance Tomography Applied to Oil Industry

Flores

Kuri-Morales

Gamio

2006

Lecture Notes in Computer Science

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The article presents a possible solution to a typical tomographic images generation problem from data of an industrial process located in a pipeline or vessel. These data are capacitance measurements obtained non-invasively according to the well known ECT technique (Electrical Capacitance Tomography). Every 313 pixels image frame is derived from 66 capacitance measurements sampled from the real time process. The neural nets have been trained using the backpropagation algorithm where training samples have been created synthetically from a computational model of the real ECT sensor. To create the image 313 neuronal nets, each with 66 inputs and one output, are used in parallel. The resulting image is finally filtered and displayed. The different ECT system stages along with the different tests performed with synthetic and real data are reported. We show that the image resulting from our method is a faster and more precise practical alternative to previously reported ones.

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“…The relationship between capacitance measurement values and pixel points of reconstruction image is described in Figure 4 [5], where the left is ECT capacitance transducers and the right is reconstruction image field.…”

Section: Design Of Input Layer and Output Layermentioning

confidence: 99%

Reconstruction Algorithms Based on Artificial Neural Network for Electrical Capacitance Tomography

Chen

Dapeng

Letian

2008

2008 Second International Symposium on Intelligent Information Technology Application

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For the non-linear characteristic and "soft filed" feature of electrical capacitance tomography system, in this paper, based on the analysis of neural network basic algorithm principles, optimizes parameters in the activation function and error function for BP network are optimized. The simulation experiment shows that the learning speed and convergence rate of the BP network has been improved. It brings many excellent capacities for the new reconstruction algorithm such as fast image reconstructing, easy system implementing and strong noise resisting etc, and it supplies a new idea for electrical capacitance tomography image reconstructing.

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Neural network for pattern association in electrical capacitance tomography

Cited by 24 publications

References 0 publications

An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data

An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data

An Application of Neural Networks for Image Reconstruction in Electrical Capacitance Tomography Applied to Oil Industry

Reconstruction Algorithms Based on Artificial Neural Network for Electrical Capacitance Tomography

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