Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Pingan Peng"'
Publikováno v:
IEEE Access, Vol 12, Pp 106685-106694 (2024)
In mine operations, the safe operation of transportation equipment is crucial to ensure the safety of miners and the efficiency of mine production. However, it is notable that there is little research on perception technology for unstructured environ
Externí odkaz:
https://doaj.org/article/41d9dbcb005c4289a0e193af676dfa8d
Publikováno v:
Mathematics, Vol 12, Iss 1, p 130 (2023)
Microseismic P- and S-phase segmentation is an influential step that limits the accuracy of event location, parameter inversion, and mechanism analysis. Therefore, an improved Unet named PSSegNet is proposed to intelligently segment the P- and S-phas
Externí odkaz:
https://doaj.org/article/a64d7c8929ea423582f28930008a9c40
Autor:
Buqing Xu, Gan Zhang, Örjan Gustafsson, Kimitaka Kawamura, Jun Li, August Andersson, Srinivas Bikkina, Bhagawati Kunwar, Ambarish Pokhrel, Guangcai Zhong, Shizhen Zhao, Jing Li, Chen Huang, Zhineng Cheng, Sanyuan Zhu, Pingan Peng, Guoying Sheng
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Isotope fingerprinting is used to track precursor sources and formation pathways of aqueous SOA, such as oxalic acid, finding that fossil fuel precursors contributions have largely been underestimated.
Externí odkaz:
https://doaj.org/article/0ba538826dc049899a83c9869477e32a
Publikováno v:
IEEE Access, Vol 8, Pp 17863-17876 (2020)
The identification of suspicious microseismic events is the first crucial step in processing microseismic data. In this paper, we present an automatic classification method based on a deep learning approach for classifying microseismic records in und
Externí odkaz:
https://doaj.org/article/63b6174514d84767bdfc5d7e9f39ef18
Publikováno v:
IEEE Access, Vol 8, Pp 141733-141747 (2020)
The accuracy of P-wave arrival picking is essential for seismic analysis. The improvement in the accuracy of P-wave arrival picking is generally achieved through improved algorithms and the processing of waveforms. Therefore, we propose a method that
Externí odkaz:
https://doaj.org/article/e9836c4ce9c743cf90f109de6eed3d77
Publikováno v:
Remote Sensing, Vol 15, Iss 2, p 309 (2023)
Reactive navigation is the most researched navigation technique for underground vehicles. Local path planning is one of the main research difficulties in reactive navigation. At present, no technique can perfectly solve the problem of local path plan
Externí odkaz:
https://doaj.org/article/dc0eb5da6f6d478986d4206e5031be7e
Publikováno v:
Applied Sciences, Vol 12, Iss 13, p 6470 (2022)
Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience
Externí odkaz:
https://doaj.org/article/51f90c6fe767434ab8269cca4f19437b
Autor:
Pingan Peng, Liguan Wang
Publikováno v:
PLoS ONE, Vol 14, Iss 2, p e0212881 (2019)
The accurate location of induced seismicity is a problem of major interest in the safety monitoring of underground mines. Complexities in the seismic velocity structure, particularly changes in velocity caused by the progression of mining excavations
Externí odkaz:
https://doaj.org/article/b0840ec733b44207a98a1b8c62a999ac
Publikováno v:
Shock and Vibration, Vol 2019 (2019)
In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various eve
Externí odkaz:
https://doaj.org/article/232a70e5302d49539f764cb308ca1c91
Publikováno v:
Applied Sciences, Vol 10, Iss 19, p 6763 (2020)
The accurate localization of mining-induced seismicity is crucial to underground mines. However, the constant velocity model is used by traditional location methods without considering the great difference in wave velocity between rock mass and under
Externí odkaz:
https://doaj.org/article/f9c6082f661a49ff841d5dd94852c405