Treffer: An application of neural networks for image reconstruction in electrical capacitance tomography applied to oil industry

Title:
An application of neural networks for image reconstruction in electrical capacitance tomography applied to oil industry
Authors:
FLORES, Norberto, KURI-MORALES, Angel, GAMIO, Carlos
Source:
Progress in pattern recognition, image analysis and applications (11th Iberoamerican congress in pattern recognition, CIARP 2006, Cancun, Mexico, November 14-17, 2006)0CIARP 2006. :371-380
Publisher Information:
Berlin; Heidelberg; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 11 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Affichage, Display, Visualización, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Banque image, Image databank, Banco imagen, Capteur mesure, Measurement sensor, Captador medida, Entrée sortie, Input output, Entrada salida, Industrie pétrolière, Oil industry, Industria petrolera, Modélisation, Modeling, Modelización, Processeur pipeline, Pipeline processor, Procesador oleoducto, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Reconstruction image, Image reconstruction, Reconstrucción imagen, Réseau neuronal, Neural network, Red neuronal, Temps réel, Real time, Tiempo real, Tomographie numérique, Computerized tomography, Traitement image, Image processing, Procesamiento imagen
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Instituto Mexicano del Petróleo, Eje Central Lázaro Cárdenas 152, San Bartolo Atepehuacan 07730, Distrito Federal, Mexico
Instituto Tecnológico Autónomo de México, Río Hondo 1, Progreso Tizapán 01080, Distrito Federal, Mexico
Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, United Kingdom
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19078970
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19078970
Database:
PASCAL Archive

Weitere Informationen

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.