Showing papers by "Sivaji Chakravorti published in 2005"

Wavelet analysis of optical signal extracted from a non-contact fibre-optic vibration sensor using an extrinsic Fabry–Perot interferometer

Abstract: Interferometric optical fibre sensors have proved to be many orders of magnitude more sensitive than their electrical counterparts, but they suffer from limitations in signal demodulation caused by phase ambiguity and complex fringe counting when the output phase difference exceeds one fringe period and for multiple fringes. This paper presents a novel signal decoding technique based on the wavelet transform of optical data extracted from a non-contact vibration sensor using an extrinsic Fabry-Perot interferometer (EFPI) implemented using single-mode fibre. The EFPI cavity has been used to generate an optical interference signal between two parallel, highly reflective surfaces separated by a variable distance. Firstly, a few recorded experimental results of the interference fringe formation due to vibration are presented in this paper. Then the wavelet transform is used for decoding the vibration signature for three major purposes of the data analyses, namely elimination of noise from the optical signals collected in real time, identification of the frequency breakdown points of the signal efficiently and automatic counting of the interference fringes. In turn, the wavelet transform is successfully employed to decode the vibration signature from the non-stationary output signal of an EFPI sensor.

A novel approach based on Simulated annealing coupled to artificial neural network for 3-D electric-field optimization

Abstract: This paper deals with the optimization of stress distribution on and around electrode-spacer arrangements for ensuring economical and higher reliability of gas-insulated systems. The optimization technique adopted in this paper is the artificial neural network-aided simulated-annealing (SA) algorithm. By coupling a trained neural net with the annealing algorithm, the execution speed of the latter is greatly enhanced to evaluate the optimum values for the design parameters of the electrode-spacer arrangements compared to calculating the cost function via the entire process for field calculation at every move of the optimization algorithm. The convergence of the optimization algorithm has also been compared statistically with the genetic algorithm. The results presented in this paper show that optimized electrode-spacer contours have been obtained with an acceptable degree of accuracy using the neural-network-aided SA algorithm. The statistical analysis shows a promising result for the proposed method.