WEBJul 1, 2022 · The results showed that the detection method based on the deep learning model accurately loed coal gangue, and the accuracy rate reached %. This paper studies the appliion of YOLOv4 detection algorithm in deep learning in the intelligent detection of coal gangue to reduce the impact of noise and radiation on workers.
WEBNov 1, 2020 · In order to achieve rapid and simultaneous quantitative detection of C, H, N in coal, a hybrid model combining laserinduced breakdown spectroscopy (LIBS) with machine learning algorithm is proposed. The detailed operations are as follows: A Nd: YAG laser with wavelength of 1064 nm is selected as excitation source, 14 kinds of national .
WEBJul 29, 2023 · The underground coal mines (UCM) exhibit many lifethreatening hazards for mining workers. In contrast, gas hazards are among the most critical challenges to handle. This study presents a comparative study of the sensor fusion methodologies related to UCM gas hazard prediction and classifiion. The study provides a brief theoretical .
WEBDec 28, 2023 · Monitoring heavy metals (HMs) across source distance and depth distribution near coal mining sites is essential for preventing environmental pollution and health risks. This study investigated the distribution of selected HMs, cadmium (Cd2+), chromium (Cr2+), copper (Cu2+), manganese (Mn2+), nickel (Ni2+), lead (Pb2+), and .
WEBEquipment Approval and Certifiion. Federal regulations require the approval or certifiion of certain mining products for use in underground coal and gassy underground metal mines. Technical experts from MSHA's Approval and Certifiion Center (ACC) evaluate and test equipment, instruments, and materials for compliance.
WEBApr 21, 2021 · A simulated annealingsupport vector machine (SASVM) prediction model was proposed to reflect the complex nonlinear mapping between characteristic gases and the coal temperature. The risk degree of CSC was estimated in the time domain, and the model was verified by using in situ data from an actual working face.
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WEBNov 2, 2018 · Alongside CMM in coal mines there are other gases such as CO 2 which may disrupt the CMM gas detection process. Therefore the selectivity to CMM gas is an important parameter for sensor using in ...
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WEBThe Matrix M5 is a significant improvement over existing machinemounted methane monitors available in the US coal market. It's ideally suited for coal or gassy mine or tunneling operations. Because of its unique sensor design, system software and beacon sensor monitoring technology, the M5 is more precise and maintains no "zero drift ...
WEBJan 30, 2014 · A new coal identifiion system based on support vector machine (SVM) was proposed. • Coal identifiion was achieved under various combustion conditions. The averaged prediction accuracy was up to %. • The prediction accuracy of the coal identifiion system was up to % under "unstudied" operation cases. •
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WEBNov 1, 2019 · An intelligent dry sorting system type GDRT using a γray gangue detection sensor and a highpressure air gun as an actuator was successfully applied in six coal mining companies in China [4, 5]. However, machine vision methods gain increased attention as alternatives to radioactive methods due to restrictions in using a radioactive .
WEBMay 15, 2023 · The appliion in the coal mining industry shows the effectiveness of the proposed conveyor belt damage detection method. Harsh underground environment for coal mining leads to poor image quality, which affects the performance of conveyor belt damage detection based on machine vision. In the future, we will focus on algorithm .
WEBMay 23, 2019 · MachineMounted Detectors. According to federal regulations, all coal mining operations are required to have mining detector technology in place that monitors for methane and oxygen levels that may be at dangerously low levels. Because of this, detection equipment is placed on all drilling equipment, and is calibrated specifically to .
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WEBMay 27, 2021 · To detect coal content in gangue, a novel approach based on image analysis and particle swarm optimization–support vector machine (PSOSVM) was presented. First, 15 features that included four sizes and 11 density parameters were extracted from coal and gangue regions of sample pictures, respectively.
WEBAug 1, 2021 · This study aims to develop a prediction model using IoT and hybrid CNNLSTM for forecasting underground mine hazards. The forecasting model predicts temperature, humidity, and gases based on the sensors' data collected from underground coal mines (Mardonova and Choi, 2018).The prediction model effectively improves .
WEBAug 8, 2012 · The device gives you an output of redstone so long as the furnace minecart has coal in it. By itself, the machine is not difficult to produce, but since the machine is silent, you need a device to give the impression that the generator is actually doing something. The machine itself requires two rails, one detector rail, and some blocks.
WEBOct 15, 2023 · This paper proposes a new method to predict the unbalanced load distribution at coalloaded wagons based on geometrical features by utilizing extreme learning machines. The proposed method is varied at various hyperparameter values and activation functions, also compared with back propagation artificial neural networks .
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