Fast and Nondestructive Proximate Analysis of Coal from Hyperspectral Images with Machine Learning and Combined Spectra-Texture Features

Fast and Nondestructive Proximate Analysis of Coal from Hyperspectral Images with Machine Learning and Combined Spectra-Texture Features

Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods invo...

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Autors principals: Jihua Mao, Hengqian Zhao, Yu Xie, Mengmeng Wang, Pan Wang, Yaning Shi, Yusen Zhao
Format: Article
Idioma:English
Publicat: MDPI AG 2024-09-01
Col·lecció:Applied Sciences
Matèries:
hyperspectral detection
coal proximate analysis
feature extraction
texture information
prediction modeling
Accés en línia:https://www.mdpi.com/2076-3417/14/17/7920

Internet

https://www.mdpi.com/2076-3417/14/17/7920

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