Direct flow process estimations from tomographic data using artificial neural systems

2nd World Congress on Industrial Process Tomography

Volumn , Issue , 2013, Pages 751-758

  Mohamad Saleh, Junita ^a   Hoyle, Brian S ^a   Podd, Frank J W ^a   Mark Spink, D ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Direct model estimation; Electrical capacitance tomography; Neural network interpretation; Principal components analysis

Indexed keywords

CAPACITANCE; ELECTRIC FIELDS; ELECTRIC IMPEDANCE TOMOGRAPHY; FINITE ELEMENT METHOD; ITERATIVE METHODS; NEURAL NETWORKS; PHASE INTERFACES; REAL TIME SYSTEMS;

ARTIFICIAL NEURAL NETWORK APPROACH; COMPUTATION INTENSIVES; CRITICAL MEASUREMENTS; DIRECT MODEL; ELECTRICAL CAPACITANCE TOMOGRAPHY; MULTI LAYER PERCEPTRON; PRINCIPAL COMPONENTS ANALYSIS; REAL-TIME RECONSTRUCTION;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84910011974     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. ABRO E., KHORYAKOV V.A., JOHANSEN G.A. and KOCBACH L. (1999), “Determination of void fraction and flow regime using neural network trained on simulated data based on gamma-ray densitometry,” Measurement Science and Technology. 10, pp. 619-630.
2. ALIMONTI C. and STECCO M. (1998), “An artificial neural network for multiphase flow metering", In Proc. ISMTMF '98 (Second International Symposium on Measuring), August 30 - September 1 1998, Beijing, China, in Development of measuring techniques for multiphase flows, Eds. Xu Yiqian and Zhang Baofen, Standards Press of China, pp.163-169.
3. BISHOP C.M. and JAMES G.D. (1993), “Analysis of multiphase flows using dual-energy gamma densitometry and neural networks,” Nuclear Instrum. Methods A.327, pp. 580-593.
4. DUGGAN P.M. and YORK T.A. (1995), “Tomographic image reconstruction using RAM-based neural networks,” In PROC. European Concerted Action on Process Tomography (ECAPT), Process Tomography -- Implementation for Industrial Processes, Bergen, Norway, pp411-419.
5. FORESEE F.D. and HAGAN M.T. (1997) “Gauss-Newton approximation to Bayesian Regularization,” In Proc. The International Joint Conference on Neural Networks, 1930-1935.
6. HAYKIN S. (1994), Neural Networks A Comprehensive Foundation, London:Macmillan College.
7. HORNIK K.M., STINCHCOMBE M. and WHITE H. (1989), “Multilayer feedforward networks are universal approximators,” Neural Networks, 2, pp. 359-366.
8. JOLLIFFE I. T. (1986), Principal Components Analysis, New York:Springer-Verlag.
9. MACKAY D.J.C. (1992) “Bayesian interpolation,” Neural Computation, 4(3), 415-447.
10. NOORALAHIYAN A.Y., HOYLE B.S. and BAILEY N.J. (1994), “Neural networks for pattern association in Electrical Capacitance Tomography,” IEE Proc.-Circuits Devices Syst., 141, pp 517-121.
11. SPINK D.M. (1996), “Direct finite element solution for the capacitance, conductance or inductance, and force in linear electrostatic and magnetostatic problems”, The International Journal for Computation and Mathematics in Electrical and Electronic Engineering,” 15, pp. 70-84.
12. WILLIAMS P.M. and YORK T.A. (1999), “Hardware implementation of RAM-based neural networks for tomographic data processing,” IEE Proc. Computers and Digital Tech., 146, pp. 114-118.
13. XIE C.G., STOTT A.L., PLASKOWSKI A. and Beck M.S. (1990), “Design of capacitance electrodes for concentration measurement of two-phase flow,” Measurement Science and Technology, 1, pp. 65-78.