Geologists also classify coal types according to the organic debris, called macerals, from which the coal is formed. Macerals (microscopic organic constituents found in coal) are identified (microscopically) by reflected light - the reflective or translucent properties of the coal indicating the individual component macerals
WhatsApp: +86 18221755073Coal mining - Underground mining: In underground coal mining, the working environment is completely enclosed by the geologic medium, which consists of the coal seam and the overlying and underlying strata. Access to the coal seam is gained by suitable openings from the surface, and a network of roadways driven in the seam then …
WhatsApp: +86 18221755073This 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 …
WhatsApp: +86 18221755073The mine pressure appearance of the mining roadway is much more complicated than that of the single roadway in the solid coal that is not affected by the mining [1].Its maintenance depends on not ...
WhatsApp: +86 18221755073It is vital to differentiate between coal and gangue in coal mining effectively. In recent years, classification methods for images of coal‐ and gangue‐based convolutional neural networks have ...
WhatsApp: +86 18221755073Coal is classified as a biogenic sedimentary rock within the group of sedimentary hydrocarbons. It is a combustible black rock consisting mainly of carbon. Coal is formed …
WhatsApp: +86 18221755073A new superpixel segmentation algorithm that combines color, spatial position, and texture in clustering is proposed, which integrates texture information into SLIC algorithm, which has better segmentation effect. ABSTRACT In coal production, it is necessary to separate waste rock from raw coal. With the development of computer …
WhatsApp: +86 18221755073A 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 type ...
WhatsApp: +86 18221755073Request PDF | Monitoring ecosystem restoration of multiple surface coal mine sites in China via Landsat images on Google Earth Engine | The restoration of surface mining sites is key to meeting ...
WhatsApp: +86 18221755073Fig. 1 compares the surface state differences of coal and gangue in various situations based on the proposed model. In the ideal laboratory environment, the light …
WhatsApp: +86 18221755073Efficient 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 ...
WhatsApp: +86 18221755073Time series remote sensing image is an important resource for dynamic monitoring of resources and environment, and its abundant time spectrum information can be used to characterize the dynamic change of vegetation coverage. This paper proposes a comprehensive clustering and pixel classification method for extracting the vegetation …
WhatsApp: +86 18221755073Opencast (coal) mining activities significantly affect the society and environment in several aspects, including land-useland-cover (LULC) alteration. The present study aims to quantify the alteration in LULC patterns in every 4 year from 2006 to 2018 in the Jharsuguda coal mining region in Odisha, India. The study has used the …
WhatsApp: +86 18221755073Abstract. This chapter describes the process of coalification, which gradually turns plant debris into coal, involving heat, pressure and the effects of time. Chemical …
WhatsApp: +86 18221755073DOI: 10.1016/j.infrared.2019.103070 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 …
WhatsApp: +86 18221755073The coal mine image dataset produced in this work is of great significance for the application of deep learning object detection algorithm for the intelligent identification and classification of ...
WhatsApp: +86 18221755073Fig. 1 compares the surface state differences of coal and gangue in various situations based on the proposed model. In the ideal laboratory environment, the light intensity is high, the coal and gangue image acquisition process is simple, and the camera receives more light signals, so it is easy to distinguish coal and gangue; however, in the …
WhatsApp: +86 18221755073The classification of rock from coal on rib images has been studied with machine learning techniques to assist for the automated rib stability analysis and enables the shearer to adjust the drums without human intervention. Classification of rock and coal is one preliminary problem for fully automated or intelligent mining. It assists for the …
WhatsApp: +86 18221755073The 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 area ...
WhatsApp: +86 18221755073The mean NDVI for coal mining areas was 0.252 km2, and for areas of reclamation, it was 0.292 km2 in 2020, while in 2019, the value for coal mining sites was 0.133 km2, and 0.163 km2 for ...
WhatsApp: +86 18221755073The continuous miner cutter (CMC) sleeve is an integral part of the mining tools, including the boxes and picks. It is designed to allow free rotation of the pick in its bore while cutting the coal.
WhatsApp: +86 18221755073Scientists and researchers performed various approaches for coal classification. Image segmentation and classification-based approaches are applied to identify the maceral components of the coal ...
WhatsApp: +86 18221755073Accurate and rapid recognition of coal and gangue is an important prerequisite for safe and efficient mining in top coal caving face. In this paper, a novel coal-gangue recognition method is put ...
WhatsApp: +86 18221755073Obtaining real-time, objective, and high-precision distribution information of surface cracks in mining areas is the first task for studying the development regularity of surface cracks and evaluating the risk. The complex geological environment in the mining area leads to low accuracy and efficiency of the existing extracting cracks methods from …
WhatsApp: +86 18221755073An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To …
WhatsApp: +86 18221755073This method has certain advantages, which can significantly improve the overall brightness of the image, reduce the noise, and is more in line with the human visual perception, and meets the needs of the construction of intelligent mine video monitoring system. In order to solve the problems of low illumination and poor image quality in video monitoring …
WhatsApp: +86 18221755073Coal 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 …
WhatsApp: +86 18221755073Coal mining, particularly surface mining, inevitably disturbs land. According to Landsat images acquired over Xilingol grassland in 2005, 2009 and 2015, land uses were divided into seven classes ...
WhatsApp: +86 182217550731.1 Radiology practices involved in acquiring digital chest X-ray images for coal mine workers are required to comply with all relevant State or Territory and Commonwealth legislation. 2. RANZCR and DIAS standards . Radiology practices and the personnel involved in acquiring digital chest X-ray images for coal mine workers are required to:
WhatsApp: +86 182217550731.15 Classification of coal mines by size of overall output: ownerwise 33 1.16 Average daily employment and output by type of mine workings: statewise 34 1.17 Average daily employment and output by type of mine workings: ownerwise 39 1.18 Productivity in coal mines: statewise 42 ...
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