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Your search keyword '"Bissessur, Y"' showing total 16 results
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16 results on '"Bissessur, Y"'

3. Combined Approach for Solving the Electromagnetic Induction Imaging Problem

Author

Bissessur, Y

Subjects
Electromagnetic induction, inverse problem, image reconstruction
Abstract

Inverse electromagnetic induction is an imaging technique for reconstructing the conductivity and permeability distributions in a region of interest from measurements of impedance made at its boundary. In general, there are two approaches to the reconstruction problem: the pixel-based approach and the parameter-based approach. This paper describes how both approaches may be combined for a more systematic reconstruction.Keywords: Electromagnetic induction, inverse problem, image reconstruction.

Published
2016

4. Modelling the nonlinearity of piezoelectric actuators in active vibration cancellation systems using neural networks

Author

Bissessur, Y

Subjects
Piezoelectric actuators, active vibration cancellation, system identification, neural networks
Abstract

Piezoelectric actuators have great capabilities as elements of intelligent structures for active vibration cancellation. One problem with this type of actuator is its nonlinear behaviour. In active vibration control systems, it is important to have an accurate model of the control branch. This paper demonstrates the ability of neural networks to model the nonlinearity of a piezoelectric actuator in the control branch.Keywords : Piezoelectric actuators, active vibration cancellation, system identification, neural networks.

Published
2016

16. Buried Plant Detection: A Volterra Series Modelling Approach Using Artificial Neural Networks.

Author

Naguib RN and Bissessur Y

Abstract

This paper discusses the embedding of artificial neural networks (ANNs) into the framework of the Volterra series for modelling the problem of detecting buried pipes. This problem is formulated as a classification task whereby it is necessary to discriminate between the ground surface and an actual pipe reflection buried in noise in the return signal from ground probing radar. The objective is to filter out the unwanted surface reflection to enable improved mapping of the site being surveyed. Since the ANN correctly maps out a real test site, it can be viewed as having modelled the system transfer function relating the training patterns to their respective classes. Using the weights learnt by the ANN and its nodal functions, this transfer function is mathematically formulated. It is shown that the latter leads to a Volterra series representation of the pipe detection problem and effectively lends itself to the extraction of the Volterra kernels for this particular system. Copyright 1996 Elsevier Science Ltd

Published
1996
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