WEBMay 1, 2013 · A neural network prediction method based on an improved SMOTE algorithm expanding a small sample dataset and optimizing a deep confidence network was proposed, which can be used to better predict and analyze coal mine water inrush accidents, improve the accuracy of water in rush accident prediction, and encourage the .
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WEBMay 1, 2023 · 1. Introduction. Metal, as a limited natural resource, is an essential material for global economic development (Sykes et al., 2016).For example, Al and Fe have been widely used in building construction and machinery manufacturing (Soo et al., 2019), V is an important metallic material used in the production of ferrous and nonferrous alloys (Gao .
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WEBJun 1, 2019 · Wang et al. [9], [10] proposed a coal component analysis model based on a support vector machine, a partial least squares regression algorithm and nearinfrared reflectance spectroscopy. The model analyzed six components of coal, including total moisture, inherent moisture, ash, volatile matter, fixed carbon, and sulfur.
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WEBOct 22, 2023 · The belt conveyor is a key piece of equipment for thermal power plants. Belt mistracking causes higher economic costs, lower production efficiency, and more safety accidents. The existing belt correction devices suffer from poor performance and high costs. Therefore, a design method for coal conveying belt correction devices is proposed in .
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WEBMay 4, 2023 · Spontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and other geomining factors. Hence, the .
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WEBJan 13, 2022 · Since hundreds or thousands of patches can be extracted from each image, the patch database is much larger than the rock and coal image database. The machine learning process is based on the patches. As discussed earlier, the RGB images are stored as threedimensional arrays, and the extraction of patches is accomplished by extracting .
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WEBDec 3, 2021 · Based on the above, this scheme designs the mine belt conveyor deviation fault detection system based on machine vision, uses mine camera to collect images, uses OpenCV visual library compiler software for image processing, carries on the clear processing to the coal mine image, effectively reduces the coal dust influence, .
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WEBFeb 20, 2023 · Computervisionbased separation methods for coal gangue face challenges due to the harsh environmental conditions in the mines, leading to the reduction of separation accuracy. So, rather than purely depending on the image features to distinguish the coal gangue, it is meaningful to utilize fixed coal characteristics like .
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WEBBituminous coal is the most abundant rank of coal found in the United States, and it accounted for about 46% of total coal production in 2022. Bituminous coal is used to generate electricity and is an important fuel and raw material for making coking coal for the iron and steel industry. Bituminous coal was produced in at least 16 states ...
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WEBDec 5, 2022 · Professor Shan Pengfei adopted a coalrock identifiion method based on machine deep learning FasterRCNN, which realized the accurate identifiion and loion of coal seam and rock stratum ...
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WEBSep 1, 2020 · Wang et al. [12] quickly analyzed the properties of coal based on support vector machine (SVM) classifier, improved PLS and nearinfrared reflectance the experiment, they first used the SVM classifier to construct a classifiion model for 199 coal samples, and then established a coal quality prediction .
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WEBJun 1, 2022 · Accordingly, eigenvectors of coal and rock images are computed based on thermal imaging cloud images from coal and rock cutting trials. The traditional recognition technology of coal and rock mainly adjusts the height of the drum of the coal winning machine by manually observing the state of coal and rock and listening to the sound.
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WEBSpontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and oth .
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WEBThe paper analyzed coal mine safety investment influence factors and established coal mine safety investment prediction model based on support vector machine. Finally, the paper adopted survey data of a mine in Huainan to exemplify and compare with traditional BP network, which proved the method feasibility and effectivity.
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WEBJan 30, 2014 · This paper presents a new online coal identifiion system based on support vector machine (SVM) to achieve online coal identifiion under variable combustion conditions.
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WEBDOI: / Corpus ID: ; The appliion of machine learning models based on particles characteristics during coal slime flotation article{Zhao2021TheAO, title={The appliion of machine learning models based on particles characteristics during coal slime flotation}, author={Binglong Zhao and .
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WEBDec 1, 2014 · Xu et al. propose a coalrock interface recognition method during top coal caving based on Melfrequency cepstrum coefficient (MFCC) and neural network with sound sensor fixed on the tail beam of ...
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WEBApr 12, 2022 · Machine learning prediction of calorific value of coal based on the hybrid analysis. April 2022. International Journal of Coal Preparation and Utilization 43 (1):122. DOI: / ...
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WEBSep 1, 2021 · The workflow combines physicsbased simulation, laboratory experiments, and a datadriven machine learning approach for estimating the permeability profile. As part of this workflow, several coal specimens from the study coal seam are first tested under different stresses to measure their permeability, density, and ultrasonic responses.
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WEBNov 1, 2020 · Simultaneous quantitative analysis of nonmetallic elements in coal by laserinduced breakdown spectroscopy assisted with machine learning. Author links open ... According to all data obtained in this work, it is reasonable to deduce conclude that LIBS technology based on and machine learning model could be a practical algorithm for .
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WEBDOI: / Corpus ID: ; Coal structure identifiion based on geophysical logging data: Insights from Wavelet Transform (WT) and Particle Swarm Optimization Support Vector Machine (PSOSVM) algorithms
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WEBSep 7, 2023 · [Show full abstract] the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal ...
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WEBApr 1, 2023 · In this study, we used machine learning based approach to classify fuels with the use of proximate analysis results,, fixed carbon, volatile matter and ash contents.
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WEBDec 21, 2021 · A coal gangue recognition method based on improved Support Vector Machine is proposed in this paper, and the experimental results show that the accuracy is %. In the process of coal mining, the separation of coal and gangue is a very important step. Traditional coal preparation methods include manual coal preparation, .
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WEBDec 15, 2021 · The subclass level classifiion also obtained good results with an accuracy of and F1 score of The results demonstrate the effectiveness of rapid coal classifiion systems based on DRS dataset in combination with different machine learningbased classifiion algorithms.
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WEBApr 26, 2023 · The problem of dust pollution in the openpit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the openpit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the openpit coal mine. It is of practical .
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WEBAug 25, 2021 · The appliion of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique .
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WEBJun 1, 2023 · Feng et al. (2015) proved that a support vector machine (SVM) could perform well in terms of accuracy to predict the gross calorific value (GCV) ... In this study, the GBRT model was used to predict the HHV of coal based on the proximate analysis data, and the model adopted optimal parameters selected through crossvalidation. ...
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WEBSep 1, 2018 · Conclusion. In this study, we proposed a coal proximate analysis model based on a combination of visibleinfrared spectroscopy and deep neural networks. We first collected the spectral data of 100 samples of different types and applied the deep learning CNN and ELM algorithms to construct a coal analysis model.
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WEBJan 1, 2007 · The support vector machines (SVM) model with multiinput and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM ...
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WEBA coal mine mantrip at Lackawanna Coal Mine in Scranton, Pennsylvania Coal miners exiting a winder cage at a mine near Richlands, ia in 1974 Surface coal mining in Wyoming, A coal mine in Frameries, Belgium. Coal mining is the process of extracting coal from the ground or from a mine. Coal is valued for its energy content and .
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WEBApr 2, 2019 · The machinelearningbased workflow provides a new technique for seismic structure interpretation in coal mining. Neural network model. Construction of the hyperplane: φ is the mapping function ...
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WEBApr 5, 2022 · In this section, we discuss several typical coal classifiion methods. The use of machine learning methods in combination with spectroscopy to classify coal is based mainly on ELM, random forest (RF) and support vector machine (SVM) [38], [39]. The comparison results are presented in Table 2. The proposed method outperforms these .
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