Cargando…
Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network
The application of laser-induced fluorescence (LIF) combined with machine learning methods can make up for the shortcomings of traditional hydrochemical methods in the accurate and rapid identification of mine water inrush in coal mines. However, almost all of these methods require preprocessing suc...
Autores principales: | Hu, Feng, Zhou, Mengran, Yan, Pengcheng, Li, Datong, Lai, Wenhao, Bian, Kai, Dai, Rongying |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9061159/ https://www.ncbi.nlm.nih.gov/pubmed/35521194 http://dx.doi.org/10.1039/c9ra00805e |
Ejemplares similares
-
Gray Evaluation of Water Inrush Risk in Deep Mining
Floor
por: Qu, Xingyue, et al.
Publicado: (2021) -
Risk assessment of coal mine water inrush based on PCA-DBN
por: Zhang, Ye, et al.
Publicado: (2022) -
Optimal location of water level sensors for monitoring mine water inrush based on the set covering model
por: Wu, Qiang, et al.
Publicado: (2021) -
Microseismic Precursors of Coal Mine Water Inrush Characterized by Different Waveforms Manifest as Dry to Wet Fracturing
por: Yu, Rui, et al.
Publicado: (2022) -
Classification of Food Additives Using UV Spectroscopy and One-Dimensional Convolutional Neural Network
por: Potărniche, Ioana-Adriana, et al.
Publicado: (2023)