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21 July 1998 Fiber optic sensor-based system to estimate stress in smart structures
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This paper describes the development of an approach to estimate the applied stress sensed from a set of multimode fiber optic sensors which are laid on the surface of a smart structure. The estimation of the applied stress is based upon the discrimination between speckle patterns produced by different strain signals. Three approaches have been formulated to estimate/classify the applied stress from the speckle patterns: (a) neural network estimation, (b) Markov random field model classification, and (c) signature-based classification. In order to develop the neural network estimator which is trained to output an estimate of the applied strain signal vector, the dimension of the original input speckle vector is first reduced by estimating the entropy of each pixel and selecting the set of pixels which carry the most information in the training set. A statistical based clustering approach is formulated to reduce the dimension further by combining highly correlated pixels in the selected set. In the Markov random field model based approach, a Markovian model for texture is assumed to fit the speckle patterns. The model parameters, as estimated using maximum likelihood techniques, are used in conjunction with a nearest neighbor rule to classify the speckle images. The signature-based classification approach is a method which incorporates both dimensionality reduction and classification directly for the case when the reference speckle images from highly representative strain vectors are available.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mohammed R. Sayeh, R. Viswanathan, Lalit Gupta, D. Kagaris, and D. Kanneganti "Fiber optic sensor-based system to estimate stress in smart structures", Proc. SPIE 3330, Smart Structures and Materials 1998: Sensory Phenomena and Measurement Instrumentation for Smart Structures and Materials, (21 July 1998);


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