Pengde Lai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Pengde Lai has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Anping Shi
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Jun Ma
China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China
1 shared publication - Lv Zhou
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication - Wenyi Zhao
Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, China
1 shared publication - Xin Li
College of Geology Engineering and Geomatics, Changan University, Xian, Shaanxi, China
1 shared publication - Yanzhao Yang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication
Latest Publications
- Open Access
A Noise Reduction Method for GB-RAR Bridge Monitoring Data Based on CEEMD-WTD and PCA
The ground-based real aperture radar (GB-RAR), with its non-contact, high-precision, continuous monitoring capabilities, is widely used in bridge safety. To reduce noise interference in GB-RAR monitoring, a denoising method based on complementary ensemble empirical mode decomposition (CEEMD), wavelet threshold denoising (WTD), and principal component analysis (PCA) was applied to the safety monitoring of the East Lake High-tech Bridge in Wuhan. The method involved CECEEMD of GB-RAR data, WTD for high-frequency noise Intrinsic Mode Function (IMF) components, and PCA for low-frequency IMF power spectrum matrices to remove coloured noise. PCA shows a symmetric balance between noise removal and signal retention. The experimental results show that the proposed denoising method ensures the integrity of the reconstructed signal by symmetrically processing the IMF of high and low frequencies and improves the signal-to-noise ratio (SNR) of the three piers to 8.30, 19.87 and 15.06, respectively, and the Root Mean Square Errors (RMSE) are 0.10 mm, 0.06 mm and 0.09 mm, respectively. Noise removal reduced uncertainty by 42.3%, 35.8%, and 33.1%, demonstrating the method’s effectiveness in enhancing deformation monitoring precision.
Open AccessA Noise Reduction Method for GB-RAR Bridge Monitoring Data Based on CEEMD-WTD and PCA
The ground-based real aperture radar (GB-RAR), with its non-contact, high-precision, continuous monitoring capabilities, is widely used in bridge safety. To reduce noise interference in GB-RAR monitoring, a denoising method based on complementary ensemble empirical mode decomposition (CEEMD), wavelet threshold denoising (WTD), and principal component analysis (PCA) was applied to the safety monitoring of the East Lake High-tech Bridge in Wuhan. The method involved CECEEMD of GB-RAR data, WTD for high-frequency noise Intrinsic Mode Function (IMF) components, and PCA for low-frequency IMF power spectrum matrices to remove coloured noise. PCA shows a symmetric balance between noise removal and signal retention. The experimental results show that the proposed denoising method ensures the integrity of the reconstructed signal by symmetrically processing the IMF of high and low frequencies and improves the signal-to-noise ratio (SNR) of the three piers to 8.30, 19.87 and 15.06, respectively, and the Root Mean Square Errors (RMSE) are 0.10 mm, 0.06 mm and 0.09 mm, respectively. Noise removal reduced uncertainty by 42.3%, 35.8%, and 33.1%, demonstrating the method’s effectiveness in enhancing deformation monitoring precision.