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Proceedings Paper

Neural network method applied to particle-image velocimetry
Author(s): Ian Grant; X. Pan
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Paper Abstract

The last two decades have seen rapid developments in computing taking as their inspiration the human brain. The human brain functions in a highly parallel and distributed fashion. The adaptive structure of the brain means that learning or training can accompany decision making. This basic neural model has inspired computer hardware exhibiting a parallelism which has revolutionised processing speeds in complex task analysis. Similarly there has been substantial activity in the field of intelligent software and in particular in the area ofneural computing. The human brain may viewed as composed of approximately 1 dbasic units, the neurons. Each neuron exhibits a high degree of interconnectivity with connections to approximately 1 O other neurons. Each neuron accepts many inputs which are added or integrated in some fashion and this causes the neuron to become active or passive. The active neuron emits an output to interconnected neurons. The importance of any one input is controlled by the effectiveness of the corresponding interconnection or weight. One area that has attracted attention in the application of neural networks is pattern recognition. Here the functions of feature classification and extraction are handled by a network which receives some education or training prior to the task of recognition. A priori knowledge of expected outcomes is used as a starting point with the network being allowed to modify or enlarge its knowledge base as the task proceeds. Various models or approaches to adaptive problem solving have been developed. The pattern recognition problem considered in the present paper is the identification of image grouping in double exposure PIV images. The aim is to provide an adaptive net which, following initial training, is able to identify image partners and adapt to changing flow conditions. This latter feature is seen as essential in order that the full potential of the neural net in temporally or spatially changing flow regimes can be realised. An important class of neural network is the multi-layer perceptron. The neurons are distributed on surfaces and linked by weighted interconnections. In the present paper we demonstrate how this type of net can developed into a competitive, adaptive filter which will identify PIV image pairs in a number of commonly occurring flow types. Previous work by the authors in particle tracking analysis (1, 2) has shown the efficiency of statistical windowing techniques in flows without systematic (in time or space) variations. The effectiveness of the present neural net is illustrated by applying it to digital simulations ofturbulent and rotating flows. Work reported by Cenedese et al (3) has taken a different approach in examining the potential for neural net methods applied to PIV.

Paper Details

Date Published: 2 December 1993
PDF: 11 pages
Proc. SPIE 2005, Optical Diagnostics in Fluid and Thermal Flow, (2 December 1993); doi: 10.1117/12.163728
Show Author Affiliations
Ian Grant, Heriot-Watt Univ. (United Kingdom)
X. Pan, Heriot-Watt Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 2005:
Optical Diagnostics in Fluid and Thermal Flow
Soyoung Stephen Cha; James D. Trolinger, Editor(s)

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