蒲源源
作者: 佚名 发布时间: 2023年02月13日 12:20 浏览次数:

蒲源源

                                                                                 

职称职务

副教授


 

 

 

联系电话


Email

yuanyuanpu@cqu.edu.cn

联系地址

重庆市沙坪坝区沙正街174
 
我校A区beat365中国在线体育/400030

研究方向

 1、岩石信号学与煤岩动力灾害 2、矿山大数据与数据驱动方法

个人简历

 

 2022.9~ 我校,beat365中国在线体育,副教授

 2019.12~2022.9 我校,beat365中国在线体育,讲师

2019.9~2019.12 University of Alberta, 土木与环境工程学院,Research associate

2016.8~2019.9 University of Alberta, 土木与环境工程学院,博士

 2013.9~2016.6 中国矿业大学,矿业工程学院,硕士

 2009.9~2013.6 中国矿业大学,孙越崎学院,本科

 

 

 

代表性研究项目

1、国家自然科学基金青年项目“渗流-应力耦合作用下岩体裂隙时序演化规律及全波形空间定位机制”(主持)

2、中央高校基本业务费“尾波岩石信号学基础与工程应用研究”(主持)

3、、企业委托项目“煤矿富水区智能分级监测及涌突水灾害预警”(主持)

4、国家重点研发计划“煤矿深部开采煤岩动力灾害防控技术研究”课题二(主研)

5、加拿大NSERC项目“Rock burst evaluation and prediction in Diavik diamond mine”(主研)

6、重庆市渝北区科技计划项目“基于3S多源数据驱动和深度学习的滑坡预警关键技术研究与应用”(主研)

 

 

 

代表性获奖

 12020年度重庆市科技进步一等奖“矿区滑坡灾害诱发机制及智能化监测预警技术与应用”(8/15

22021年重庆市科技进步一等奖“含瓦斯地层动力灾害智能监测-预警-防控一体化技术与应用”(4/15

 

 

 

 

代表性专利

1Dynamic and Static Coupling Method for Evaluating Coal Mine   Hazards Based on Bayesian Method (排名2

2Method of Obtaining Elastic Strain Energy Based on Initial   Point of Rock Fissure (排名3

3、支架动态压力差指标实时确定与优化方法(排名2

 

代表性专著、教材

 

 

 

代表性论文

1. Pu, Y*., Chen, J., Jiang, D. et al. (2022). Improved Method for   Acoustic Emission Source Location in Rocks Without Prior Information. Rock   Mech Rock Eng 55, 5123–5137

2. Chen, J., Ye, Y., Pu, Y*., Xu, W., & Mengli, D. (2022).   Experimental study on uniaxial compression failure modes and acoustic   emission characteristics of fissured sandstone under water saturation. Theoretical   and Applied Fracture Mechanics, 119, 103359.

3. Chen, J., Zhu, C., Du, J., Pu,   Y*., Pan, P., Bai, J., & Qi,   Q. (2022). A quantitative pre-warning for coal burst hazardous zones in a   deep coal mine based on the spatio-temporal forecast of microseismic events. Process   Safety and Environmental Protection, 159, 1105-1112.

4. Du, J., Chen, J., Pu, Y*., Jiang, D., Chen, L., &   Zhang, Y. (2021). Risk assessment of dynamic disasters in deep coal mines   based on multi-source, multi-parameter indexes, and engineering application. Process   Safety and Environmental Protection, 155, 575-586.

5. Pu, Y., Chen, J., & Apel, D. B.   (2021). Deep and confident prediction for a laboratory earthquake. Neural   Computing and Applications, 33(18), 11691-11701.

6. Pu, Y., Apel, D. B., Liu, V.,   & Mitri, H. (2019). Machine learning methods for rockburst   prediction-state-of-the-art review. International Journal of Mining   Science and Technology, 29(4), 565-570.

7. Pu, Y., Apel, D. B., Szmigiel, A., & Chen, J.   (2019). Image recognition of coal and coal gangue using a convolutional neural   network and transfer learning. Energies, 12(9), 1735.

8. Pu, Y., Apel, D. B., & Xu, H. (2019). Rockburst prediction in kimberlite   with unsupervised learning method and support vector classifier. Tunnelling   and Underground Space Technology, 90, 12-18.

9. Pu, Y*., Apel, D. B., & Hall, R. (2020). Using   machine learning approach for microseismic events recognition in underground   excavations: Comparison of ten frequently-used models. Engineering Geology,   268, 105519.

10. 陈结,杜俊生,蒲源源,.冲击地压“双驱动”智能预警架构与工程应用[J]. 煤炭学报,2022,47(2):791-806.


 
     
     

 

 

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