D-P-Transformer: A Distilling and Probsparse Self-Attention Rockburst Prediction Method

Yu Zhang, Jitao Li*, Dongqiao Liu, Guangshu Chen, Jiaming Dou

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

7 引用 (Scopus)

摘要

Rockburst may cause damage to engineering equipment, disrupt construction progress, and endanger human life. To this day, the occurrence of rockburst remains complex and difficult to predict. This study proposes the D-P-Transformer algorithm to address this issue by improving the embedding structure of the Transformer for specific applications to rockburst data. To reduce the computational requirement, sparse self-attention is adopted to replace self-attention. A distilling operation and multiple layer replicas are simultaneously used to enhance the robustness and speed up the algorithm’s process. Taking all relevant rockburst factors into consideration, multiple experiments are conducted on seven large-scale rockburst datasets with different training ratios to verify the reliability of the proposed D-P-Transformer rockburst prediction algorithm. As compared to the original algorithm, the proposed algorithm shows average reductions of 24.45%, 46.56%, 17.32%, and 48.11% in the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively. The results indicate that the novel D-P-Transformer rockburst prediction algorithm is superior to the Transformer prediction algorithm, and could be used for coal mine rockburst prediction analysis.

源语言英语
文章编号3959
期刊Energies
15
11
DOI
出版状态已出版 - 1 6月 2022
已对外发布

指纹

探究 'D-P-Transformer: A Distilling and Probsparse Self-Attention Rockburst Prediction Method' 的科研主题。它们共同构成独一无二的指纹。

引用此

Zhang, Y., Li, J., Liu, D., Chen, G., & Dou, J. (2022). D-P-Transformer: A Distilling and Probsparse Self-Attention Rockburst Prediction Method. Energies, 15(11), 文章 3959. https://doi.org/10.3390/en15113959