Full article: Deep convolutional neural networks for surface coal
Deep convolutional neural networks for surface coal mines determination from sentinel2 images L Madhuanand,P Sadavarte,AJH Visschedijk,HAC Denier Van Der Gon This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of images with annotation 6 categories of mine An open dataset for intelligent recognition and classification of
Coal and Rock Classification with Rib Images and Machine
In this paper, the classification of rock from coal on rib images has been studied with machine learning techniques A database of rock and coal image has been In recent years, classification methods for images of coal and ganguebased convolutional neural networks Research on image classification of coal and Research on image classification of coal and gangue based on a
Coal mine area monitoring method by machine learning and
This paper proposes a new coal quality exploration method that detects coal quality in coal mining areas and explores and monitors the distribution and change Deep learning is an effective way to improve the classification accuracy of coal images for the machine visionbased coal sorting However, the related research on deep learning Deep learningbased image classification for online multicoal and
PAPER OPEN ACCESS Coal Images Database and Its Application
The paper proposes a tool database system dedicated for s upporting images processing and computer vision methods application in the mining industry The We investigated SVM classification use for change detection due to surface mining Classification accuracy for Ikonos image was higher than that for Surface coal mine area monitoring using multitemporal high
Coal rock image recognition method based on improved CLBP
The coal rock database used in the experiment includes two categories of coal and rock, a total of 67 968 jpg format (256 level) gray coal rock images with a This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks 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 Sentinel2 satellite images with 13 spectralFull article: Deep convolutional neural networks for surface coal
Demonstration of Optical Microscopy and Image Processing
Respirable coal mine dust represents a serious health hazard for miners Monitoring methods are needed that enable fractionation of dust into its primary components, and that do so in real time Near the production face, a simple capability to monitor the coal versus mineral dust fractions would be highly valuable for tracking application of image processing in Run of Mine (ROM) and aggregate analysis is growing since the 1990s The image processing was used for grain size distribution determination [1, 2], ore classification [3], coal gangue identification [4] and so on Despite the machine vision method used,PAPER OPEN ACCESS Coal Images Database and Its Application
(PDF) Petrological characterization of coal: An evolving science
For most of the 20th century optical petrography has been the primary petrological and mineralogical tool used to characterize coal The development of quantitative SEMbased techniques, egPosition list for coal mine workers Coal mine workers' position titles need to be provided by employers when completing the Employer section of the approved health assessment form The Standardised coal mine worker position list (PDF, 380KB) has a list of position titles and categories, as well as guidance on how to classify a positionStandards and resources for health assessments
The Origin and Classification of Coal SpringerLink
Abstract This chapter describes the process of coalification, which gradually turns plant debris into coal, involving heat, pressure and the effects of time Chemical changes during peatification and coalification are described, and also structural changes in coal during coalification are covered (cleats and their development)On this basis, an objectoriented random forest classification method was used to classify the aerial image (1979, early mining period), QuickBird image (2003, before the renovation of the mine geological environment), WorldView image (2011, during mine geological environment renovation), and GF2 image (2018, after mine geological Frontiers Surface Environmental Evolution Monitoring in Coal
(PDF) Image classification method of underground coal
A coal image dataset that can reflect the destruction type and a classification method that focuses on the texture features of the tectonic coal are needed for the study of coal destruction typeGas explosion, which is the main type of accidents reported in coal mines, comes with serious economic and safety consequences The present work adopts the “glass heart” model for assessing the vulnerability of coal mines to gas explosions based on a constructed gas explosion vulnerability assessment index system Coal Mine Gas Explosion Vulnerability Assessment Based on
Surface coal mine area monitoring using multitemporal high
Research Highlights We investigated SVM classification use for change detection due to surface mining Classification accuracy for Ikonos image was higher than that for Quickbird image We have found out that mine and dump area decreased by 1925 ha from 2004 to 2008 Forest area increased by 673% from 854 ha to 911 ha from 2004 presence of emphysema Image quality was graded as grade 1 good; grade 2 acceptable with no technical defect likely to impair classification for pneumoconiosis; grade 3 acceptable with some technical defect, but still adequate for classification purposes; or grade 4 unacceptable for classification purposesReview of CT Scan Classifications Performed Under the Coal Mine
Coal US Geological Survey
The US Coal Resources and Reserves Assessment Project, as part of the US Geological Survey (USGS) Energy Resources Program, conducts systematic, geologybased, regional assessments of significant coal beds in major coal basins in the United States These assessments detail the quantity, quality, location, and economic Our open database on global coal and metal mine production 12 covers worldwide mining activities of metal ores and coal, on an individual mine level It comprises 1171 mines, production data forAn open database on global coal and metal mine production
ISO 73040 Coals
Brown coals and lignites — Classification by types on the basis of total moisture content and tar yield 9060: ISO/TC 27/SC 5: ISO/TS 4667:2022 Coal — Determination of the thermal stability and thermal fragmentation 6060: ISO/TC 27/SC 5: ISO/TS 4676:2022 Coal — Determination of carboxyreactivityCoal rank: A classification of coal based on fixed carbon, volatile matter, and heating value of the coal Coal rank indicates the progressive geological alteration (coal ification) from lignite to anthracite Coal workers’ pneumoconiosis (CWP): A chronic dust disease of the lung arising from employment in a coal mineCoal Mine Dust Exposures and Associated Health Outcomes
(PDF) An Overview of Soil Pollution and Remediation Strategies in Coal
The current review summarizes progress in comprehending (1) coal mining impacts on soil pollution, (2) the potential risks of soil pollution associated with coal mining, and (3) different types ofAbstract: This paper reviewed the development process and the current status of comprehensive mechanized coal mining equipment technology in China The definition and technical connotation of the intelligent coal mine on the basis of the artificial intelligence and technology of the Internet of Things (IoT) are proposed The paper Research and Practice of China's Intelligent Coal Mines
Automated Seasonal Detection of Coal Surface Mine
The results showed that SVM classification method can effectively be utilized for high spatial resolution multispectral satellite images for identifying the changes in surface coal mine areaCoal mine intelligentisation is the core technical support for the highquality development of the adaptive adjustment is adopted to improve the accuracy of target classification to a certain extent Through data enhancement, 2000 images were obtained in the end These images comprised 1000 coal images and 1000 gangueCoal gangue detection and recognition algorithm based on
Industry Investment NSW
A fire in a coal mine is considered a hazardous event The purpose of the guideline is to control risks to the health and safety of people, (and control risks to plant, infrastructure and the environment) from the event of a fire in a coal mine by providing guidance in a) undertaking a risk management approach to fire hazards; and