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http://dx.doi.org/10.7844/kirr.2018.27.1.84

Estimated Soft Information based Most Probable Classification Scheme for Sorting Metal Scraps with Laser-induced Breakdown Spectroscopy  

Kim, Eden (School of Mechanical Engineering, Gwangju Institute of Science and Technology)
Jang, Hyemin (School of Mechanical Engineering, Gwangju Institute of Science and Technology)
Shin, Sungho (School of Mechanical Engineering, Gwangju Institute of Science and Technology)
Jeong, Sungho (School of Mechanical Engineering, Gwangju Institute of Science and Technology)
Hwang, Euiseok (School of Mechanical Engineering, Gwangju Institute of Science and Technology)
Publication Information
Resources Recycling / v.27, no.1, 2018 , pp. 84-91 More about this Journal
Abstract
In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.
Keywords
Laser-induced breakdown spectroscopy (LIBS); Metal scrap; Classification; Soft information; Radar plot;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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