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Cross-validation results of ash quantification with partial least squares regression (a) and prediction set regression results (b).
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Coal is expected to be an important energy resource for some developing countries in the coming decades; thus, the rapid classification and qualification of coal quality has an important impact on the improvement in industrial production and the reduction in pollution emissions. The traditional methods for the proximate analysis of coal are time co...
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Context 1
... the factor k of partial least squares was 12, the R 2 value was the largest and the RMSECV value is the smallest, with the values of 0.942 and 1.349%, respectively, as shown in Figure 9a. Then, the spectra of the prediction set were input into the model to obtain the predicted value of ash content. ...
Context 2
... the factor k of partial least squares was 12, the R 2 value was the largest and the RMSECV value is the smallest, with the values of 0.942 and 1.349%, respectively, as shown in Figure 9a. Then, the spectra of the prediction set were input into the model to obtain the predicted value of ash content. ...
Context 3
... (b) Figure 9. Cross-validation results of ash quantification with partial least squares regression (a) and prediction set regression results (b). ...
Context 4
... the factor k of partial least squares was 12, the R 2 value was the largest and the RMSECV value is the smallest, with the values of 0.942 and 1.349%, respectively, as shown in Figure 9a. Then, the spectra of the prediction set were input into the model to obtain the predicted value of ash content. ...
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Citations
... This sensitivity allows LIBS technology to effectively distinguish between materials based on their unique spectral signatures. Consequently, it provides a viable method for the qualitative classification and identification of nonmetallic electric energy meter box materials with varying compositions [33][34][35][36]. LIBS tests were performed on samples 1, 2, and 3, yielding corresponding spectral data. ...
The electric energy meter box is an important protective unit of the power grid system, which is usually made of sheet molding compound (SMC) composites. Its quality and operational reliability have a significant impact on the safety of the power grid. However, there is currently a lack of rapid and effective detection methods for the shell material of the electric energy meter box. This paper proposes a rapid detection method for the SMCs material of the electric energy meter box based on laser-induced breakdown spectroscopy technology and develops an optimization method for the laser test parameters. For different formulations of the materials, two classification methods based on support vector machine (SVM) and self-organizing map (SOM) neural network are established. The classification accuracy of SVM reaches 99.9%, and the classification accuracies of SOM neural network are all over 90%. This method achieves rapid testing and classification of SMC composites used in energy meter boxes, providing new theoretical and practical approaches to enhance the safety and stable operation of the power grid.
... The plain Bayesian classification method is able to distinguish coal samples into different origins based on the probability distribution function. Zheng et al. [19] used this method to classify coal samples with a prediction accuracy of 96.7%. In addition to these classical machine learning algorithms, researchers have proposed many different methods, such as Piecewise Modeling [20] and Multiple-setting Spectra [21]. ...
As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification which has the ability to rapidly analyze coal compared with traditional coal analysis methods. In the practical application of LIBS, a large amount of labeling data is usually required, but it is quite difficult to obtain labeling data in industrial sites. In this paper, to address the problem of insufficient labeled data, a semi-supervised classification model (SGAN) based on adversarial neural network is proposed, which can utilize unlabeled data to improve the classification accuracy. The effects of labeled and unlabeled samples on the classification accuracy of the SGAN model are investigated, and the results show that the number of labeled and unlabeled samples are positively correlated, and the highest average classification accuracy that the model can achieve is 98.5%. In addition, the classification accuracies of SGAN and other models (e.g., CNN, RF) are also compared, and the results show that, with the same number of labeled samples in the three models, SGAN performs better after the number of unlabeled samples reaches a certain level, with an improvement of 0.7% and 2.5% compared to the CNN and RF models, respectively. This study provides new ideas for the application of semi-supervised learning in LIBS.
... China's growing economy has resulted in an increased demand for coal resources [1]. However, the drilling site environment poses unique challenges due to its complexity, including the presence of significant coal dust and the risk of gas explosions. ...
Gas explosions threaten the safety of underground coal mining. Mining companies use drilling rigs to extract the gas to reduce its concentration. Drainage depth is a key indicator of gas drainage; accidents will be caused by going too deep. Since each drill pipe has the same length, the actual extraction depth is equivalent to the number of drill pipes multiplied by the length of a single drill pipe. Unnecessary labor is consumed and low precision is achieved by manual counting. Therefore, the drill pipe counting method of YOLOv7-GFCA target detection is proposed, and the counting is realized by detecting the movement trajectory of the drilling machine in the video. First, Lightweight GhostNetV2 is used as the feature extraction network of the model to improve the detection speed. Second, the (Fasternet-Coordinate-Attention) FCA network is fused into a feature fusion network, which improves the expression ability of the rig in complex backgrounds such as coal dust and strong light. Finally, Normalized Gaussian Wasserstein Distance (NWD) loss function is used to improve rig positioning accuracy. The experimental results show that the improved algorithm reaches 99.5%, the model parameters are reduced by 2.325 × 106, the weight file size is reduced by 17.8 M, and the detection speed reaches 80 frames per second. The movement trajectory of the drilling rig target can be accurately obtained by YOLOv7-GFCA, and the number of drill pipes can be obtained through coordinate signal filtering. The accuracy of drill pipe counting reaches 99.8%, thus verifying the feasibility and practicability of the method.
During the extraction and processing of coal, a large amount of solid waste, collectively known as gangue, is produced. This gangue has a low carbon content but a high ash content, accounting for approximately 15 to 20% of the total coal yield. Before coal is used, coal and gangue must be effectively separated to reduce the gangue content in the raw coal and improve the efficiency of coal utilization. This study introduces a classification method for coal and gangue based on a combination of laser-induced breakdown spectroscopy (LIBS) and deep learning. The method employs Gramian angular summation fields (GASF) to convert 1D spectral data into 2D time-series data, visualizing them as 2D images, before employing a novel deep learning model—GASF-CNN—for coal and gangue classification. GASF-CNN enhances model focus on critical features by introducing the SimAM attention mechanism, and additionally, the fusion of various levels of spectral features is achieved through the introduction of residual connectivity. GASF-CNN was trained and tested using a spectral data set containing coal and gangue. Comparative experimental results demonstrate that GASF-CNN outperforms other machine learning and deep learning models across four evaluation metrics. Specifically, it achieves 98.33, 97.06, 100, and 98.51% in the accuracy, recall, precision, and F1 score metrics, respectively, thereby achieving an accurate classification of coal and gangue.
