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Titolo:
Neural network reconstruction for tomography of a gravel-air-seawater mixture
Autore:
Teague, G; Tapson, J; Smit, Q;
Indirizzi:
Univ Cape Town, Dept Elect Engn, ZA-7701 Rondebosch, South Africa Univ Cape Town Rondebosch South Africa ZA-7701 Rondebosch, South Africa Cape Technikon, Dept Elect Engn, ZA-8000 Zonnebloem, South Africa Cape Technikon Zonnebloem South Africa ZA-8000 Zonnebloem, South Africa
Titolo Testata:
MEASUREMENT SCIENCE & TECHNOLOGY
fascicolo: 8, volume: 12, anno: 2001,
pagine: 1102 - 1108
SICI:
0957-0233(200108)12:8<1102:NNRFTO>2.0.ZU;2-U
Fonte:
ISI
Lingua:
ENG
Soggetto:
ELECTRICAL-IMPEDANCE TOMOGRAPHY;
Keywords:
impedance tomography; neural network; image reconstruction;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Engineering, Computing & Technology
Citazioni:
15
Recensione:
Indirizzi per estratti:
Indirizzo: Teague, G Univ Cape Town, Dept Elect Engn, UCT Private Bag, ZA-7701 Rondebosch, South Africa Univ Cape Town UCT Private Bag Rondebosch South Africa ZA-7701
Citazione:
G. Teague et al., "Neural network reconstruction for tomography of a gravel-air-seawater mixture", MEAS SCI T, 12(8), 2001, pp. 1102-1108

Abstract

This paper presents the first practical implementation of electrical impedance tomography for the imaging of a gravel-air-seawater mixture, wherein the image reconstruction is performed using a neural network. Although research into the use of neural networks for image reconstruction has been done.previous work has predominantly made use of simulated capacitance or impedance readings to train the networks. In contrast, this paper adopts a new practical approach whereby the networks are trained and tested using real data from an existing impedance tomography system. The practical aspects of generating this database are discussed and the network training details are given. Results indicate that the trained networks are able to discriminate among the seawater, gravel and air phases and reconstruct images of bubble configurations not included in the training database. In terms of the intended application, accurate predictions of volume fraction are required. Consequently, this paper examines an alternative solution to this problem, whereby the networks are trained to predict the volume fractions directly rather than first performing an image reconstruction as is standard. Results indicate that these volume fraction predictors outperform the image reconstruction networks in terms of the accuracies of the volume fraction predictionsfor each of the three phases.

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Documento generato il 18/01/20 alle ore 22:19:00