Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Jianxian Cai"'
Publikováno v:
Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 7, Pp 2199-2218 (2024)
Abstract Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time
Externí odkaz:
https://doaj.org/article/e01108bba74b4a7ab23a2e0c9fd30996
Publikováno v:
Journal of Petroleum Exploration and Production Technology, Vol 14, Iss 4, Pp 883-908 (2024)
Abstract Denoising micro-seismic signals is paramount for ensuring reliable data for localizing mining-related seismic events and analyzing the state of rock masses during mining operations. However, micro-seismic signals are commonly contaminated by
Externí odkaz:
https://doaj.org/article/a48a9828d19f4ae7b9aee84a08b5e230
Publikováno v:
Applied Sciences, Vol 14, Iss 16, p 6917 (2024)
In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM n
Externí odkaz:
https://doaj.org/article/6987df6429384703bb8b8deee046fb53
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-18 (2023)
Abstract Aiming at the problems of low sensitivity and low accuracy caused by the displacement transfer mechanism of three displacement sensors used simultaneously in the 3D displacement monitoring of seismic isolation bearings, the paper has propose
Externí odkaz:
https://doaj.org/article/14c41afd81074544b09c92f50987dee4
Publikováno v:
Applied Sciences, Vol 13, Iss 3, p 1324 (2023)
To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic
Externí odkaz:
https://doaj.org/article/fa96262d90804eef957fcb4448bb0c55
Publikováno v:
Frontiers in Earth Science, Vol 9 (2021)
Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR). However, the current convolutional deep network used for seis
Externí odkaz:
https://doaj.org/article/040f9fdb53ff443ea5e2e0a3d0d83357
Publikováno v:
Atmosphere, Vol 8, Iss 1, p 10 (2017)
This study aims to develop a second order self-organizing fuzzy neural network (SOFNN) to predict the hourly concentrations of fine particulate matter (PM2.5) for the next 24 h at a regional background station called Shangdianzi (SDZ) in China from 1
Externí odkaz:
https://doaj.org/article/edc4bf82b0964dea93822bd11aed58d6
Publikováno v:
Traitement du Signal. Oct2022, Vol. 39 Issue 5, p1703-1709. 7p.
Publikováno v:
Sensor Review. 43:83-91
Purpose In response to the common low sensitivity of fiber Bragg grating (FBG) temperature sensors in measurement, an FBG temperature sensor sensitized in a substrate-type package structure is proposed. Design/methodology/approach The sensitivity of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::310fbf68b0d02a79164290c0ae99d9cc
https://doi.org/10.1108/sr-03-2022-0156
https://doi.org/10.1108/sr-03-2022-0156
Publikováno v:
Robotica. 41:690-712
We propose a hierarchical cognitive navigation model (HCNM) to improve the self-learning and self-adaptive ability of mobile robots in unknown and complex environments. The HCNM model adopts the divide and conquers approach by dividing the path plann
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::124b2604520dcc3958266977a1ffca5a
https://doi.org/10.1017/s0263574722001539
https://doi.org/10.1017/s0263574722001539