Author:
Finotello Riccardo,L’Hermite Daniel,Quéré Celine,Rouge Benjamin,Tamaazousti Mohamed,Sirven Jean-Baptiste
Abstract
Laser-induced breakdown spectroscopy is a versatile technique that can be used to quickly measure the concentration of elements in ambient air. We tackle the issues of performance and trustworthiness of the statistical model used for predictions. We propose a method for improving the performance and trustworthiness of statistical models for LIBS. Our method uses deep convolutional multitask learning architectures to predict the concentration of the analyte and additional information as auxiliary outputs. We also introduce a simulation-based data augmentation process to synthesize more training samples. The secondary predictions from the model are used to characterize, quantify and validate its trustworthiness, taking advantage of the mutual dependencies of the weights of the neural networks. As a consequence, these output can be used to successfully detect anomalies, such as changes in the experimental conditions, and out-of-distribution samples. Results on different types of materials show that the proposed method improves the robustness and trueness of the predictions.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献