Material species identification system using material spectral data

Abstract

A system of collating a spectral data of an arbitrary material with spectral data of existing materials to identify the kind of the arbitrary material comprises a one-dimensional CNN processor calculating a characteristic value vector based on a spectral data of a material by a one-dimensional convolution neural network algorithm, and a metric learning processor computing a probability that the kind of the material is each kind of the existing materials from the characteristic value vector by a deep metric learning algorithm. The processors learn with the spectral data of existing materials to compute a probability for the kind of each material such that the probabilities for the kinds of the respective materials inputted for data for learning becomes maximum. When the data of the arbitrary material is inputted, the kind giving the maximum probability is identified as the kind of the arbitrary material with high precision.