Velocity measurement of granular flow with a Hopfield network

Author

Lee, Jingeol ; Principe, Jose C. ; Hanes, Daniel M.

Author_Institution

Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA

fYear

1995

fDate

31 Aug-2 Sep 1995

Firstpage

380

Lastpage

387

Abstract

The transport of granular flow is common to many industrial processes. This paper discusses a methodology to measure the velocity of dry granular solids down an inclined chute using high speed digital images. Acrylic particles have been used as granular solids in our experiment. First, particles are located using normalized correlation. A technique for measuring the velocities of individual acrylic particles is developed based on a Hopfield network to solve the particle correspondence problem between successive images. A new, rigidity constraint is applied to the Hopfield energy function, and the results show better performance than the conventional cost function

Keywords

Hopfield neural nets; channel flow; correlation methods; granular materials; motion estimation; neural nets; two-phase flow; velocity measurement; Hopfield energy function; Hopfield network; acrylic particles; granular flow; high-speed digital images; inclined chute; industrial processes; normalized correlation; rigidity constraint; velocity measurement; Brightness; Conducting materials; Cost function; Digital images; Material storage; Particle measurements; Pattern recognition; Sea measurements; Solids; Velocity measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-2739-X

Type

conf

DOI

10.1109/NNSP.1995.514912

Filename

514912