Weighted-averaging-based classification of laser-induced breakdown spectroscopy measurements using most informative spectral lines

In this study,efficient spectral line selection and wcightcd-avcraging-bascd processing schemes are proposed for the classification of laser-induced breakdown spectroscopy(UBS)measurements.For fast on-line classification,a set of representative spectral lines arc selected ami processed relying on the information metric,instead of the time consuming full spectrum based analysis.I he most informative spectral line sets arc investigated by the joint mutual information estimation(MIR)evaluated with the Gaussian kernel density,where dominant intensity peaks associated with the concentrated components arc not necessarily most valuable for classification.In order to further distinguish the characteristic patterns of die LIBS measured spectrum,two-dimensional spectral images are synthesized through column-wise concatenation of the peaks along with their neighbors.For fast classification while preserv ing die effect of distinctive peak patterns,column-wise Gaussian weighted averaging is applied to die synthesized images,yielding a favorable trade off between classification performance and computational complexity.To explore the applicability of the proposed schemes,two applications of alloy classification and skin cancer detection arc investigated with the multi-class and binary support vector machines classifiers,respectively.Ihc MIE measures associated with selected spectral lines in bodi applications show a strong correlation to the actual classification or detection accuracy,which enables to find out meaningful combinations of spectral lines.In addition,the peak patterns of the selected lines and their Gaussian weighted averaging with nciehbors of the selected peaks efficiently distineuish different classes of LIBS measured spectrum.

Adaptive Electrospinning System Based on Reinforcement Learning for Uniform‑Thickness Nanofiber Air Filters

Electrospinning is a simple and versatile method to produce nanofiber filters.However,owing to bending instability that occurs during the electrospinning process,electrospinning has frequently produced a non-uniform-thickness nanofiber filter,which deteriorates its air filtration.Here,an adaptive electrospinning system based on reinforcement learning(E-RL)was developed to produce uniform-thickness nanofiber filters.The E-RL accomplished a real-time thickness measurement of an electrospun nanofiber filter by measuring the transmitted light through the nanofiber filter using a camera placed at the bottom of the collector and converting it into thickness using the Beer–Lambert law.Based on the measured thickness,the E-RL detected the non-uniformity of the nanofiber filter thickness and manipulated the movable collector to alleviate the non-uniformity of the thickness by a pre-trained reinforcement learning(RL)algorithm.For the training of the RL algo-rithm,the nanofiber production simulation software based on the empirical model of the deposition of the nanofiber filter was developed,and the training process of the RL algorithm was repeated until the optimal policy was achieved.After the training process with the simulation software,the trained model was transferred to the adaptive electrospinning system.By the movement of the collector under the optimal strategy of RL algorithm,the non-uniformity of such nanofiber filters was significantly reduced by approximately five times in standard deviation and error for both simulation and experiment.This finding has great potential in improving the reliability of electrospinning process and nanofiber filters used in research and industrial fields such as environment,energy,and biomedicine.

Correction to:Adaptive Electrospinning System Based on Reinforcement Learning for Uniform‑Thickness Nanofiber Air Filters

Correction to:Advanced Fiber Materials https://doi.org/10.1007/s42765-022-00247-3 In this article the author name Dong Yong Park was incorrectly written as Dong Young Park.The original article has been corrected.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.