Spectral Characterization of Pulsed Ultrasound Using Neural Networks.

Abstract

A novel nondestructive evaluation technique that uses the spectral signature of a pulsed ultrasound signal to identify metals had recently been abandoned because of the difficulty in interpreting the results. Traditional analysis is inconvienent to apply to this type of problem because of the complicated, noisy and incomplete nature of the data. Neural networks provide a radically different approach to computation. These massively parallel systems provide a mechanism to extract pertinent information from input data while maintaining a high degree of fault tolerance. This report discusses design of a neural network system capable of accepting data from nondestructive test equipment and producing output relative to the quality of the sample being tested.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1994
Accession Number
ADA292440

Entities

People

  • Mark A. Johnson
  • Michael A. Cipollo
  • R. D. Scanlon

Organizations

  • United States Army Armament Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computations
  • Engineering
  • Fault Tolerance
  • Frequency
  • Frequency Response
  • Heat Treatment
  • Impurities
  • Military Research
  • Network Architecture
  • Neural Networks
  • Parallel Computing
  • Parallel Processing
  • Security
  • Spectrum Analyzers
  • Test Equipment
  • Training
  • Ultrasounds

Fields of Study

  • Computer science

Readers

  • Electronics Engineering
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks