Performance evaluation of a deep learning based wet coal image

Request PDF Performance evaluation of a deep learning based wet coal image classification Moisture is one of the important influencing factors on machine vision based mineral image

Coal and gangue classification in actual environment of mines

Therefore this paper establishes deep learning based RGB image classification models for the classification tasks of various coal particles with two density level < g/cm³ & > g/cm³ in

Identification and Monitoring of Surface Elements in Open Pit Coal Mine

Coal mining has brought a series of environmental problems Local government departments have issued relevant governance policies but the premise of scientific prevention and control is to correctly grasp the actual distribution of various ground objects in the mining area Using classification methods to extract ground object information based on remote

Deep convolutional neural networks for surface coal mines

This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task using Convolutional Neural Networks CNN and finds the VGG network combined with transfer learning to be an optimal model for this task ABSTRACT Coal is a principal source of energy

Intelligent Identification of Maceral Components of Coal Based on Image

Scientists and researchers performed various approaches for coal classification Image segmentation and classification based approaches are applied to identify the maceral components of the coal

Coal Resource Classification System of the Bureau of

sions of the Joint Geological Survey Bureau of Mines Resource Classification Agreement of November 21 1973 covering all min­ eral resources and will be used in future resource/reserve studies GLOSSARY OF COAL CLASSIFICATION TERMS Resources Concentrations of coal in such forms that economic extraction is currently or may become

Dynamic Coal Quantity Detection and Classification of

Request PDF Dynamic Coal Quantity Detection and Classification of Permanent Magnet Direct Drive Belt Conveyor Based on Machine Vision and Deep Learning Real time and accurate measurement of

Classification of clustered microseismic events in a coal mine

The effective recognition of microseismic signal is related to the accuracy of mine dynamic disaster precursor information processing which is a difficult method of microseismic data processing

PDF Research and practice of intelligent coal

With a list of known coal mine locations from various countries a training dataset of Coal Mine and No Coal Mine image patches is prepared using Sentinel 2 satellite images with 13

RecaT A CNN Transformer based network for coal rock image classification

Rapid coal‐rock identification is one of the key technologies for intelligent and unmanned coal mining Currently the existing image recognition algorithms cannot satisfy practical needs in

Improvement of coal mine roof rating classification using

The results show that the fuzzy membership degree for slicing mining method is % and % for full coal height mining % for top coal caving mining respectively which indicating

Low light images enhancement method in coal mine

At present the methods for low illumination image enhancement in coal mine mainly include the following aspects 1 histogram equalization HE and its variants [4] These kinds of methods output Figure 1 Prediction probability change results of classification prediction experiment under different temperatures Dilated convolution

Support design in underground coal mines using modified

In India CMRI ISM RMR classification is recommended for use in all the underground coal mines to evaluate the roof conditions based on RMR and designing suitable support system in development and depillaring headings Paul et al 2014 In this system the rock load from the immediate roof is determined from an adjusted RMR value using an empirical

PDF Research on Automatic Classification of Coal Mine

Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety disaster prevention and

Coal mine area monitoring method by machine learning and

DOI / Corpus ID 209988379; Coal mine area monitoring method by machine learning and multispectral remote sensing images article{He2019CoalMA title={Coal mine area monitoring method by machine learning and multispectral remote sensing images} author={Dakuo He and Ba Tuan Le and Dong Xiao and Yachun Mao and Feng Shan and Thai

Coal and Gangue Classification Based on Laser Induced

Shenfu Dongsheng mine 18 gangue and 27 coal samples from the Yanzhou mine and 16 gangue and 5 coal samples from the Pingshuo mine We utilize an Ocean Insight brand MX2500 spectrometer to collect laser induced breakdown spectral data of the samples which has a spectral range of 199−1112 nm with eight channels

Deep learning based image classification of gas coal Request PDF

Request PDF On Jan 1 2021 Zelin Zhang and others published Deep learning based image classification of gas coal Find read and cite all the research you need on ResearchGate

PDF Deep Learning Algorithm for Computer Vision with a

PDF p> To Whom It May Concern Members of The Scientific Community Dear colleagues I hope my letter finds you well The data Coal and Gangue Infrared Images in BMP file format has been

PDF Mining Methods Part I Surface mining ResearchGate

PDF Surface mining; Classification of surface mining methods together with the desired parameters/ conditions suitable for their applications; Find read and cite all the research you