Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Tieding Lu"'
Publikováno v:
Geodesy and Geodynamics, Vol 15, Iss 3, Pp 302-313 (2024)
The pressure and temperature significantly influence precipitable water vapor (PWV) retrieval. Global Navigation Satellite System (GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the
Externí odkaz:
https://doaj.org/article/dfd0984c710e4b078e5852758357aa73
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract The upper tropospheric water vapor is a key component of Earth's climate. Understanding variations in upper tropospheric water vapor and identifying its influencing factors is crucial for enhancing our comprehension of global climate change.
Externí odkaz:
https://doaj.org/article/bbe6588005744cb890a1ab20e46ff761
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9324-9336 (2024)
Point cloud registration plays a central role in various applications, such as 3-D scene reconstruction, preservation of cultural heritage and deformation monitoring. The point cloud data are usually huge. Processing such huge data is very time-consu
Externí odkaz:
https://doaj.org/article/3a3a19b53f2e45e5a1dcab0c73658367
Publikováno v:
Remote Sensing, Vol 16, Iss 21, p 3967 (2024)
This study verifies the practicality of using finite element analysis for strain and deformation analysis in regions with sparse GNSS stations. A digital 3D terrain model is constructed using DEM data, and regional rock mass properties are integrated
Externí odkaz:
https://doaj.org/article/9243c76b2dd743c3a646a4400bcf38fb
Autor:
Xiwen Sun, Tieding Lu, Shunqiang Hu, Haicheng Wang, Ziyu Wang, Xiaoxing He, Hongqiang Ding, Yuntao Zhang
Publikováno v:
Remote Sensing, Vol 16, Iss 21, p 3978 (2024)
To solve the problems of difficult to model parameter selections, useful signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf
Externí odkaz:
https://doaj.org/article/ba2216df089247b88b85778c22db8930
Autor:
Xijiang Chen, Qing An, Bufan Zhao, Wuyong Tao, Tieding Lu, Han Zhang, Xianquan Han, Emirhan Ozdemir
Publikováno v:
Drones, Vol 8, Iss 6, p 239 (2024)
The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused
Externí odkaz:
https://doaj.org/article/d98831252d3049ec8cc3a680421663a8
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 12, p 2386 (2023)
Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of sea level change prediction
Externí odkaz:
https://doaj.org/article/6d46a577206140b4b7140e94ca5a03a5
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4374 (2023)
The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions ma
Externí odkaz:
https://doaj.org/article/9b10e09913e44f46a6cb5f7575729282
Autor:
Xiwen Sun, Tieding Lu, Shunqiang Hu, Jiahui Huang, Xiaoxing He, Jean-Philippe Montillet, Xiaping Ma, Zhengkai Huang
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3572 (2023)
Accurate noise model identification for GNSS time series is crucial for obtaining a reliable GNSS velocity field and its uncertainty for various studies in geodynamics and geodesy. Here, by comprehensively considering time span and missing data effec
Externí odkaz:
https://doaj.org/article/6f530852846547c186b8d28bc5358049
Autor:
Hongkang Chen, Tieding Lu, Jiahui Huang, Xiaoxing He, Kegen Yu, Xiwen Sun, Xiaping Ma, Zhengkai Huang
Publikováno v:
Remote Sensing, Vol 15, Iss 14, p 3694 (2023)
GNSS time series prediction plays a significant role in monitoring crustal plate motion, landslide detection, and the maintenance of the global coordinate framework. Long short-term memory (LSTM) is a deep learning model that has been widely applied
Externí odkaz:
https://doaj.org/article/561ffc136a1d449498db4f0a2330d508