Search

Proximate analysis of coal is of profound significance for understanding coal quality and promoting rational utilization of coal resources. Traditional coal proximate analysis mainly uses chemical analysis methods, which have the disadvantages of slow speed and high cost. This paper proposed an approach combining reflectance spectroscopy with deep learning (DL) for rapid proximate analysis of coal. First, 80 sets of coal spectral data are enhanced by data augmentation, outlier detection, and dimensional transformation to improve the number and quality of samples. Then, an analytical model combining dilated convolution, multi-level residual connection, and a two-hidden-layer extreme learning machine (TELM), named DR_TELM, was proposed. The model extracted effective features from coal spectral data by a convolutional neural network (CNN) and utilized TELM as a regressor to achieve feature identification and content prediction. The experimental results showed that DR_TELM achieved coefficients of determination (R 2 ) of 0.981, 0.989, 0.990, 0.985, 0.989 and root mean square errors (RMSE) of 0.533, 1.833, 1.111, 1.808, 0.723 for the content prediction of moisture, ash, volatile matter, fixed carbon and higher heating value (HHV), respectively. And while ensuring high accuracy, the test time is only 0.034 s. It is fully demonstrated that DR_TELM can rapidly and accurately analyze coal., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Image interpretation algorithms based on deep learning are becoming increasingly important in land cover information acquisition. We propose CSSwin-unet, a network designed for the semantic segmentation of remote sensing images. CSSwin-unet is based on Swin-unet, follows the U-shaped codec structure of U-Net, but utilizes Swin transformer blocks with superior global modelling capabilities to constitute the codec. In addition, we design a parallel branch in the encoder with a context aggregation module (CAM) to enhance contextual information extraction and alleviate the semantic ambiguity problem resulting from occlusion. To address the problem of semantic information mismatch between codecs and improve the model's ability to extract spatial information, we constructed a space extraction module (SEM) in the skip connections, which replaces the direct copying of encoder features in Swin-unet. To reduce information loss during the downsampling process and strengthen the segmentation capacity of the network, we designed a feature shrinkage module (FSM) in the downsampling session. We conducted comprehensive ablation experiments on a dataset we produced ourselves and compared the results with other advanced methods. The test results showed significant improvement, with mIoU, mF1, and OA values improving by 2.83%, 2.47%, and 2.05%, respectively, compared to the second best performing model, Swin-unet. The above results prove the excellent performance of CSSwin-unet. [ABSTRACT FROM AUTHOR]

Deep dictionary learning (DDL) can mine deeper representations of data more effectively than single-layer dictionary learning. However, existing DDL methods with specific sparse regularizers lead to designated deep sparse representations. Existing DDL optimization methods have limited feature extraction capability because their nonlinear functions must satisfy the condition that the functions are invertible. This paper presents a new DDL model with a learned sparsity constraint and a noninvertible soft-thresholding (ST) function, where the learned sparsity constraint can obtain a data-driven sparse representation and the ST (that produces a sparse output as a nonlinear function) can enhance its the feature extraction capability. To solve the optimization problem efficiently with the noninvertible and learned sparsity constraints, we propose to employ a lifted proximal operator machine to transform the proposed DDL problem into a series of subproblems that include sparsity-regularized and convex minimizations. For the sparsity-regularized minimization, we derive the parameterized proximal operator of the sparse regularizer, which is considered as the activation function used to construct the network. The parameters of the activation function are trained by backpropagation, thus obtaining the proximal operator of the learnable sparse regularizer simultaneously with a sparse solution. The convex minimization for the dictionaries and the coefficients can be obtained via the accelerated proximal gradient and the optimal condition, respectively. In the numerical classification and reconstruction experiments, the proposed algorithm outperformed existing DDL algorithms in terms of classification accuracy, image reconstruction, and noise immunity. [ABSTRACT FROM AUTHOR]