Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model

Lu, Zhengxiong; Guo, Wei; Meng, Fanyuan; Guo, Xiaojun; Wang, Shenquan; He, Hong

doi:10.1016/j.energy.2023.128771

Cited by 46 publications

(11 citation statements)

References 45 publications

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“…Because of the powerful representation ability of deep neural networks [34], they have achieved remarkable results on many problems; therefore, we chose ResNet as the basic framework. The core architecture consisted of four convolutional blocks and six identification blocks stacked in the order of one convolutional block and one identification block, respectively, with the remaining two blocks stacked at the end.…”

Section: Discussionmentioning

confidence: 99%

DMRNet Based Tuberculosis Screening With Cough Sound

Xu,

Yuan,

Lou

et al. 2024

IEEE Access

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Tuberculosis is the leading cause of death due to a single infection prior to the COVID-19 pandemic. Screening of tuberculosis patients in a large population is of paramount importance for disease treatment and control, especially with an economical, accurate, and easy-to-operate method. Based on cough sounds, we proposed DMRNet to distinguish between patients with tuberculosis, other respiratory diseases, and healthy individuals. DMRNet comprises four convolutional blocks and six identification blocks and incorporates dynamic convolution into the first three convolutional blocks to promote feature extraction. After the second and third dynamic convolutions, a polarized self-attention mechanism was added to reduce the information loss caused by the dimensionality reduction. Finally, a multihead self-attention layer is added to the fourth convolutional block and the last three identification blocks to enhance the aggregation of global information. Using a dataset with 1323 cough sound fragments, the results achieved the accuracy, sensitivity, and specificity of tuberculosis screening were 94.32%, 97.73%, and 99.43%, respectively. Compared with reported studies, the proposed model demonstrated better accuracy and reliability. Cough sound-based DMRNet analysis is a promising method for tuberculosis screening, especially in densely populated areas. Owing to its convenience, low equipment requirements, and low cost, it is expected to become an effective tool for community tuberculosis screening.INDEX TERMS Convolutional neural networks, cough sounds, tuberculosis screening.

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Section: Discussionmentioning

confidence: 99%

DMRNet Based Tuberculosis Screening With Cough Sound

Xu,

Yuan,

Lou

et al. 2024

IEEE Access

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“…Efficient transportation is one of the keys to efficient coal production [36]. While the development of intelligent transformation and the improvement of management levels, the occurrence rate of transportation accidents and associated risk continue to increase [37] due to the complex nature of the coal industry [38].…”

Section: Coal Mine Transportation Accidents Analysismentioning

confidence: 99%

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

He,

Pan,

Wang

et al. 2024

Mathematics

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In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

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“…Like pixels in an image, structured graphs are grids of nodes, so Graph Convolutional Networks (GCNs) [26] are GNNs applied to grids of nodes like Convolutional Neural Networks (CNNs). Based on this method, Dynamic Graph Convolution Neural Network (DGCNN) has proved efficient in the segmentation of coal mining data with the aim of reducing its environmental footprint [28]. Other GNN architecture is proving effective, such as parsimonious neighbor selection in GraphSAGE [29] or adding the attention mechanism [30] in Graph Attention Networks [31].…”

Section: Graphs Clusteringmentioning

confidence: 99%

Product Space Clustering with Graph Learning for Diversifying Industrial Production

Cortial,

Albouy-Kissi,

Chausse

2024

Applied Sciences

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During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research.

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Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model

Cited by 46 publications

References 45 publications

DMRNet Based Tuberculosis Screening With Cough Sound

DMRNet Based Tuberculosis Screening With Cough Sound

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

Product Space Clustering with Graph Learning for Diversifying Industrial Production

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