Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches-Reference-Cited by-同舟云学术

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Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Published:2023-03 Issue: Volume:190 Page:106865
ISSN:0921-3449
Container-title:Resources, Conservation and Recycling
language:en
Short-container-title:Resources, Conservation and Recycling

Author:

Díaz-Romero Dillam,Van den Eynde Simon,Zaplana Isiah,Zhou Chuangchuang,Sterkens Wouter,Goedemé Toon,Peeters Jef

Publisher

Elsevier BV

Subject

Economics and Econometrics,Waste Management and Disposal

Reference70 articles.

1. Machine learning-based calibration LIBS analysis of aluminium-based alloys;Ahmed;Eur. J. Plus,2022

2. Mapping Key Objectives and R&D Challenges Along the Aluminium Value Chain,2016

3. Segdiff: Image segmentation With Diffusion Probabilistic Models;Amit,2021

4. Uncertainty driven multi-loss fully convolutional networks for histopathology;BenTaieb,2017

5. Albumentations: fast and flexible image augmentations;Buslaev;Information,2020

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1. Modelling and optimization of an innovative facility for automated sorting of aluminium scraps;Waste Management;2024-12

2. Development of laser-induced breakdown spectroscopy based spectral tandem technology: A topical review;TrAC Trends in Analytical Chemistry;2024-08

3. Generation mechanism and empirical model of eddy current force and torque in drum-type eddy current separation;Waste Management;2024-06

4. Nestability: A deep learning oracle for nesting scrap prediction in manufacturing industry;Resources, Conservation and Recycling;2024-06

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