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180 results on '"ZHANG Shubi"'

2. Real time cycle slip detection of Beidou single frequency carrier phase based on Kalman filtering

Author

MA Xiaodong, SONG Bing, ZHANG Shubi, and LIU Xin

Subjects
mining deformation monitoring, cycle slip detection, beidou, kalman filtering, Mining engineering. Metallurgy, TN1-997
Abstract

In application of Beidou single frequency receiver in real time mining deformation monitoring, in order to improve precision of cycle slip detection in measurement preconditioning of Beidou single frequency carrier phase, real time cycle slip detection of Beidou single frequency carrier phase based on Kalman filtering was proposed, and recognition method of gross errors and cycle slips was discussed. The experiment was carried out with the Beidou single frequency carrier phase data collected in a certain mining area, the results show that the method can efficiently, sensitively and real-timely detect the location of cycle slip.

Published
2016
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3. Research of selection of reference stations in mine area deformation monitoring based on JSCORS

Author

CHEN Zhongwei, CHEN Xibin, QIU Chuan, and ZHANG Shubi

Subjects
deformation monitoring in mine area, cors reference station, regional gps network, gamit solution, reference frame, Mining engineering. Metallurgy, TN1-997
Abstract

Based on data information of Jiangsu Province continuously operating reference stations, the influence of the number and distribution of CORS reference stations to the solution accuracy of GPS network data were discussed combining with 6 stations solution of the GPS deformation monitoring network in a coal mine. The experimental results show that when processing regional GPS network, it's the best to select the CORS reference station as the reference frame, and the CORS reference station space distribution should be as uniform as possible, and the number reached 4, and there's no need to select the same side CORS reference station. The conclusion provides the basis to process GPS network data by selecting suitable CORS reference stations.

Published
2015
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9. Multiscale Damage Identification Method of Beam-Type Structures Based on Node Curvature.

Author

Ye, Kai, Zhang, Shubi, Zhang, Qiuzhao, Zhong, Rumian, and Wang, Wenda

Subjects
SINGULAR value decomposition, BENDING moment, FINITE element method, PARAMETER identification, LINEAR equations
Abstract

This paper proposes a multiscale damage identification method for beam-type structures based on node curvature. Firstly, based on the assumption that micro-damage has little effect on stress redistribution and the basic relationship between structural bending moment and curvature, combined with the denoising function of wavelet analysis, the linear matrix equation before and after node curvature damage is solved using the singular value decomposition (SVD) method. Then, the theoretical feasibility of this method is verified with laboratory tests of a simply supported beam. Finally, the damage sensitivity and noise resistance of this method are verified using field measurements of a beam bridge. The results show that the nodal curvature serves as an indicator parameter for damage identification in beam-type structures, enabling the precise localization of damage within these structures. When utilizing a multiscale finite element model for analysis, the nodal curvature enhances the ability to identify both the location and severity of damage within small-scale elements. Furthermore, this method can provide a reference for the damage identification and health monitoring of other types of bridges. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Inclusion of Side Signals on GNSS Water Vapor Tomography with a New Height Factor Model

Author

Zhang, Wenyuan, Ding, Nan, Zhang, Shubi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Sun, Jiadong, editor, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2020
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30. A New Deep-Learning-Assisted Global Water Vapor Stratification Model for GNSS Meteorology: Validations and Applications

Author

Zhang, Wenyuan, Gou, Junyang, Moller, Gregor, Zhang, Shubi, Gao, Yu, Wang, Nandi, and Soja, Benedikt

Abstract

Layer precipitable water (LPW), a water vapor product similar to precipitable water vapor (PWV), reports partial moisture content within a specified vertical range. Compared with PWV data, the latest LPW products can describe more refined distributions and variations in water vapor in the troposphere. Global Navigation Satellite Systems (GNSSs), as a powerful water vapor sensing tool, only provide the opportunity to retrieve all-weather PWV, not LPW products. To this end, we develop the first deep-learning-assisted, global water vapor stratification (GWVS) model to estimate the GNSS LPW within any given vertical range. The proposed model is trained and tested using the global radiosonde data, with the training and testing root mean square error (RMSE) of 0.94 and 1.10 mm for radiosonde LPW, indicating the excellent generalization of the GWVS model. Furthermore, the model is comprehensively validated using the data from the two regional GNSS networks and one global network. The RMSEs of the predicted GNSS LPW from the three GNSS networks compared with the co-located radiosonde LPW are 1.52, 1.80, and 1.54 mm, respectively. To study potential applications, we use the model-derived GNSS LPW products to calibrate Geostationary Operational Environmental Satellite-16 (GOES-16) LPW products and improve the GNSS water vapor tomography technique. Results show that the accuracy of three GOES-16 LPW products is improved by 31.3%, 23.3%, and 17.9%, respectively, and the RMSE of the tomography results is reduced from 2.28 to $1.67~\text {g}/\text {m}^{3}$ . Both validation and application results highlight that the GWVS model retrieves the required GNSS LPW products and provides additional value for water-vapor-related studies.

Published
2024
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34. The next wave of GNSS tropospheric sounding – perspective and retrospective

Author

Zhang, Kefei, Liu, Zhizhao, Song, Shuli, Zheng, Nanshan, Zhang, Shubi, Wu, Suqin, Zhang, Qiuzhao, Li, Zengke, Wang, Xiaoming, Li, Longjiang, Sun, Peng, Li, Haobo, Zhu, Dantong, He, Qimin, and Zhang, Minghao

Abstract

As one of the most important parameters, water vapor is very valuable for the studies of extreme weather events and climatic phenomena. The GNSS have been proven to be a relatively new technique for sounding atmospheric water vapor which has experienced unprecedented developments over the past decade due to the rapid deployment of wide-spread space- and ground-based infrastructures. This significant development and the long-term accumulation of GNSS data, has offered a strong data support and new opportunity to advance our understanding of climate change and extreme weather events. In the meanwhile, the fifth resurgence of artificial intelligence (AI) technology has empowered us in the ability of big geodetic data analytics and data mining. All these have helped us in the understanding the atmospheric dynamic process, and mining the detailed information of the formation, evolution, development and dissolution of extreme weather events and complicated atmospheric process.This contribution presents a summary of our 10-year relevant research in the area that involves a full spatial domain of ground-, air- and space-based systems and a comprehensive technological domain of GNSS/geodesy, remote sensing, meteorology and atmosphere. First, the background, current status and recent international frontier developments of the GNSS tropospheric sounding technique are summarized. Then, the theory, technical features and major advancement of the GNSS-derived atmospheric products in the applications of climate analyses and extreme weather forecasting are presented. Finally, the challenges, opportunities and future prospectives in terms of the technique and its innovative applications of national and international significance are provided., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

37. Tightly coupled water vapor tomography algorithm for combining GNSS and MODIS signals

Author

ZHANG Wenyuan, ZHANG Shubi, ZHENG Nanshan, DING Nan, LIU Xin, and MA Pengxu

Subjects
kriging interpolation, voxel node model, radiosonde, gnss water vapor tomography, Mathematical geography. Cartography, modis pwv, GA1-1776
Abstract

Due to several significant advantages, including high-precision observation data, high spatio temporal resolution, and all-weather availability, GNSS tomography technology has become one of the most potential technologies for sensing the atmospheric water vapor. Currently, fusion of multi-source atmospheric remote sensing data has gradually become a research hotspot to make up for the geometric defects of GNSS signal in the tomography model. In this paper, the disadvantage of traditional model including the MODIS signals is analyzed at first. An improved tomography method, combining the GNSS and MODIS signals, based on the voxel node model is proposed, which introduces high-resolution MODIS PWV into the tomographic model in the form of three-dimensional signals. To assess the validity of the proposed algorithm,three experimental schemes are carried out using the 15 MODIS images and the simultaneous GNSS data derived from five GNSS stations over Xuzhou region.The experimental results show that the average number of effective signals is increased by 34.15% and the mean root mean square error(RMSE) of tomography results is decreased by 25.10% with the proposed tomography approach. Furthermore, the water vapor profiles retrieved from the three schemes are assessed using the reference profiles from the radiosonde data close to the acquisition time.It is found that in the lower layers from 0 to 2 km, the improved method retrieves better 3D distribution of water vapor than the traditional approach, which highlights that the reconstruction quality of 3D water vapor field near the surface can be optimized by including the MODIS signals.

Published
2021

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