Classification-free threat detection based on material-science-informed clustering

Published

Conference Paper

© 2017 SPIE. X-ray diffraction (XRD) is well-known for yielding composition and structural information about a material. However, in some applications (such as threat detection in aviation security), the properties of a material are more relevant to the task than is a detailed material characterization. Furthermore, the requirement that one first identify a material before determining its class may be difficult or even impossible for a sufficiently large pool of potentially present materials. We therefore seek to learn relevant composition-structure-property relationships between materials to enable material-identification-free classification. We use an expert-informed, data-driven approach operating on a library of XRD spectra from a broad array of stream of commerce materials. We investigate unsupervised learning techniques in order to learn about naturally emergent groupings, and apply supervised learning techniques to determine how well XRD features can be used to separate user-specified classes in the presence of different types and degrees of signal degradation.

Full Text

Duke Authors

Cited Authors

  • Yuan, S; Wolter, SD; Greenberg, JA

Published Date

  • January 1, 2017

Published In

Volume / Issue

  • 10187 /

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9781510608757

Digital Object Identifier (DOI)

  • 10.1117/12.2262942

Citation Source

  • Scopus