Morphology and autowave metric on CNN applied to Bubble-Debris classification

István Szatmári, Abraham Schultz, Csaba Rekeczky, Tibor Kozek, Tamás Roska, Leon O. Chua

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

In this study, we present the initial results of cellular neural network (CNN)-based autowave metric to high-speed pattern recognition of gray-scale images, the application is to a problem involving separation of metallic wear deris particles from air bubbles. This problem arises in an optical-based system for determination of mechanical wear. This paper focuses on distinguishing debris particles suspended in the oil flow from air bubbles and aims to employ CNN technology to create an online fault monitoring system. For the class of engines of interest bubbles occur much more often than debris particles and the goal is to develop a classification system with an extremely low false alarm rate for missclassififed bubbles. The designed analogic CNN algorithm detects and classifies single ubbles and bubble groups using inary morphology and autowave metric. The debris particles are separated based on autowave distances computed btween bubble models and the unknown objects. Initial experiments indicate that the proposed algorithm is robust and noise tolerant and when implemented on a CNN universal chip it provides a solution in real time.

Original languageEnglish
Pages (from-to)1385-1393
Number of pages9
JournalIEEE Transactions on Neural Networks
Volume11
Issue number6
DOIs
Publication statusPublished - Dec 1 2000

Keywords

  • Autowave metric
  • Cellular neural networks (CNNs)
  • Hamming and Hausdorff distances
  • Model-based classification

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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