{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:38:11Z","timestamp":1777037891605,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42264004"],"award-info":[{"award-number":["42264004"]}]},{"name":"National Natural Science Foundation of China","award":["42474026"],"award-info":[{"award-number":["42474026"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFB2600400"],"award-info":[{"award-number":["2021YFB2600400"]}]},{"name":"National Natural Science Foundation of China","award":["GUIKE AB24010144"],"award-info":[{"award-number":["GUIKE AB24010144"]}]},{"name":"National Natural Science Foundation of China","award":["GUIKE ZY24212008"],"award-info":[{"award-number":["GUIKE ZY24212008"]}]},{"name":"National Natural Science Foundation of China","award":["TICGM-2024-07"],"award-info":[{"award-number":["TICGM-2024-07"]}]},{"name":"National Natural Science Foundation of China","award":["230100018"],"award-info":[{"award-number":["230100018"]}]},{"name":"National Key Research and Development Program of China","award":["42264004"],"award-info":[{"award-number":["42264004"]}]},{"name":"National Key Research and Development Program of China","award":["42474026"],"award-info":[{"award-number":["42474026"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFB2600400"],"award-info":[{"award-number":["2021YFB2600400"]}]},{"name":"National Key Research and Development Program of China","award":["GUIKE AB24010144"],"award-info":[{"award-number":["GUIKE AB24010144"]}]},{"name":"National Key Research and Development Program of China","award":["GUIKE ZY24212008"],"award-info":[{"award-number":["GUIKE ZY24212008"]}]},{"name":"National Key Research and Development Program of China","award":["TICGM-2024-07"],"award-info":[{"award-number":["TICGM-2024-07"]}]},{"name":"National Key Research and Development Program of China","award":["230100018"],"award-info":[{"award-number":["230100018"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["42264004"],"award-info":[{"award-number":["42264004"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["42474026"],"award-info":[{"award-number":["42474026"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["2021YFB2600400"],"award-info":[{"award-number":["2021YFB2600400"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["GUIKE AB24010144"],"award-info":[{"award-number":["GUIKE AB24010144"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["GUIKE ZY24212008"],"award-info":[{"award-number":["GUIKE ZY24212008"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["TICGM-2024-07"],"award-info":[{"award-number":["TICGM-2024-07"]}]},{"name":"Guangxi Key Technologies R&amp;D Program","award":["230100018"],"award-info":[{"award-number":["230100018"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["42264004"],"award-info":[{"award-number":["42264004"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["42474026"],"award-info":[{"award-number":["42474026"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["2021YFB2600400"],"award-info":[{"award-number":["2021YFB2600400"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["GUIKE AB24010144"],"award-info":[{"award-number":["GUIKE AB24010144"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["GUIKE ZY24212008"],"award-info":[{"award-number":["GUIKE ZY24212008"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["TICGM-2024-07"],"award-info":[{"award-number":["TICGM-2024-07"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Funding Project","award":["230100018"],"award-info":[{"award-number":["230100018"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["42264004"],"award-info":[{"award-number":["42264004"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["42474026"],"award-info":[{"award-number":["42474026"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["2021YFB2600400"],"award-info":[{"award-number":["2021YFB2600400"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["GUIKE AB24010144"],"award-info":[{"award-number":["GUIKE AB24010144"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["GUIKE ZY24212008"],"award-info":[{"award-number":["GUIKE ZY24212008"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["TICGM-2024-07"],"award-info":[{"award-number":["TICGM-2024-07"]}]},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources","award":["230100018"],"award-info":[{"award-number":["230100018"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["42264004"],"award-info":[{"award-number":["42264004"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["42474026"],"award-info":[{"award-number":["42474026"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["2021YFB2600400"],"award-info":[{"award-number":["2021YFB2600400"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["GUIKE AB24010144"],"award-info":[{"award-number":["GUIKE AB24010144"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["GUIKE ZY24212008"],"award-info":[{"award-number":["GUIKE ZY24212008"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["TICGM-2024-07"],"award-info":[{"award-number":["TICGM-2024-07"]}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["230100018"],"award-info":[{"award-number":["230100018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2019s effectiveness in enhancing deformation monitoring precision.<\/jats:p>","DOI":"10.3390\/sym17040588","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T09:06:51Z","timestamp":1744621611000},"page":"588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Noise Reduction Method for GB-RAR Bridge Monitoring Data Based on CEEMD-WTD and PCA"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9349-6265","authenticated-orcid":false,"given":"Lv","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, China"},{"name":"Hubei LuoJia Laboratory, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1641-7347","authenticated-orcid":false,"given":"Pengde","family":"Lai","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzhao","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anping","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1255-9173","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geology Engineering and Geomantic, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3233\/BRS-190152","article-title":"Multi-sensor measurement of dynamic deflections and structural health monitoring of flexible and stiff bridges","volume":"15","author":"Stiros","year":"2019","journal-title":"Bridge Struct."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108303","DOI":"10.1016\/j.measurement.2020.108303","article-title":"Bridge monitoring using multi-GNSS observations with high cutoff elevations: A case study","volume":"168","author":"Xi","year":"2021","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s13349-020-00436-x","article-title":"Dynamic monitoring and data analysis of a long-span arch bridge based on high-rate GNSS-RTK measurement combining CF-CEEMD method","volume":"11","author":"Niu","year":"2021","journal-title":"J. Civ. Struct. Health Monit."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vicente, M.A., Gonzalez, D.C., Minguez, J., and Schumacher, T. (2018). A novel laser and video-based displacement transducer to monitor bridge deflections. Sensors, 18.","DOI":"10.3390\/s18040970"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1177\/14759217221083609","article-title":"A method for structural monitoring of multispan bridges using satellite InSAR data with uncertainty quantification and its pre-collapse application to the Albiano-Magra Bridge in Italy","volume":"22","author":"Farneti","year":"2023","journal-title":"Struct. Health Monit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1080\/00396265.2021.1920792","article-title":"Health monitoring and safety evaluation of bridge dynamic load with a ground-based real aperture radar","volume":"54","author":"Long","year":"2022","journal-title":"Surv. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pieraccini, M., Miccinesi, L., Abdorazzagh Nejad, A., and Naderi Nejad Fard, A. (2019). Experimental dynamic impact factor assessment of railway bridges through a radar interferometer. Remote Sens., 11.","DOI":"10.3390\/rs11192207"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, L., Guo, J., Wen, X., Ma, J., Yang, F., Wang, C., and Zhang, D. (2020). Monitoring and analysis of dynamic characteristics of super high-rise buildings using GB-RAR: A case study of the WGC under construction, China. Appl. Sci., 10.","DOI":"10.3390\/app10030808"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Suo, Z., Tian, F., Qi, L., Tao, H., and Li, Z. (2022). A novel GB-SAR system based on TD-MIMO for high-precision bridge vibration monitoring. Remote Sens., 14.","DOI":"10.3390\/rs14246383"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhao, S., Wang, P., Wang, R., and Huang, M. (2022). Improved data-driven stochastic subspace identification with autocorrelation matrix modal order estimation for bridge modal parameter extraction using GB-SAR Data. Buildings, 12.","DOI":"10.3390\/buildings12020253"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6423","DOI":"10.1109\/JSEN.2018.2825331","article-title":"Dynamic vibration characteristics monitoring of high-rise buildings by interferometric real-aperture radar technique: Laboratory and full-scale tests","volume":"18","author":"Hu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kuras, P., Ortyl, \u0141., Owerko, T., Salamak, M., and \u0141azi\u0144ski, P. (2020). GB-SAR in the diagnosis of critical city infrastructure\u2014A case study of a load test on the long tram extradosed bridge. Remote Sens., 12.","DOI":"10.3390\/rs12203361"},{"key":"ref_13","first-page":"153","article-title":"Rating for operational performance of high-speed railway bridges based on Ground-based Interferometry Radar","volume":"45","author":"Gao","year":"2023","journal-title":"J. China Railw. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhou, L., Ma, J., Shi, A., Li, X., Liu, L., Zhang, Z., and Zhang, D. (2022). GB-RAR Deformation Information Estimation of High-Speed Railway Bridge in Consideration of the Effects of Colored Noise. Appl. Sci., 12.","DOI":"10.3390\/app122010504"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hu, J., Guo, J., Xu, Y., Zhou, L., Zhang, S., and Fan, K. (2019). Differential ground-based radar interferometry for slope and civil structures monitoring: Two case studies of landslide and bridge. Remote Sens., 11.","DOI":"10.3390\/rs11242887"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105507","DOI":"10.1016\/j.engappai.2022.105507","article-title":"A novel deep convolutional image-denoiser network for structural vibration signal denoising","volume":"117","author":"Xiong","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4920809","DOI":"10.1155\/2017\/4920809","article-title":"Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet","volume":"2017","author":"Ke","year":"2017","journal-title":"Math. Probl. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, S., Li, F., Wang, J., Sangaiah, A.K., Sheng, V.S., and Ding, X. (2019). An ECG signal de-noising approach based on wavelet energy and sub-band smoothing filter. Appl. Sci., 9.","DOI":"10.3390\/app9224968"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhao, X., Liang, Y., and Luan, X. (2022). Denoising Ocean Turbulence Microstructure Signals for Application in Estimating Turbulence Kinetic Energy Dissipation Rates Based on EMD-PCA. Sensors, 22.","DOI":"10.3390\/s22124413"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. London Ser. A Math. Phys. Eng. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108901","DOI":"10.1016\/j.measurement.2020.108901","article-title":"Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM","volume":"172","author":"Chen","year":"2021","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.egypro.2016.10.026","article-title":"A new ensemble empirical mode decomposition (EEMD) denoising method for seismic signals","volume":"97","author":"Gaci","year":"2016","journal-title":"Energy Procedia"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108405","DOI":"10.1016\/j.measurement.2020.108405","article-title":"An EEMD-SVD-LWT algorithm for denoising a lidar signal","volume":"168","author":"Cheng","year":"2021","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Peng, K., Guo, H., and Shang, X. (2021). EEMD and multiscale PCA-based signal denoising method and its application to seismic P-phase arrival picking. Sensors, 21.","DOI":"10.3390\/s21165271"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1016\/j.phpro.2012.05.222","article-title":"Improved threshold denoising method based on wavelet transform","volume":"33","author":"Huimin","year":"2012","journal-title":"Phys. Procedia"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jiang, X., Lang, Q., Jing, Q., Wang, H., Chen, J., and Ai, Q. (2022). An improved wavelet threshold denoising method for health monitoring data: A case study of the Hong Kong-Zhuhai-Macao Bridge immersed tunnel. Appl. Sci., 12.","DOI":"10.3390\/app12136743"},{"key":"ref_28","first-page":"518","article-title":"Time domain signal extraction from GNSS time series with colored noise","volume":"66","author":"REN","year":"2023","journal-title":"Chin. J. Geophys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119774","DOI":"10.1016\/j.engstruct.2025.119774","article-title":"Improved damage assessment of bridges using advanced signal processing techniques of CEEMDAN-EWT and Kernal PCA","volume":"329","author":"Abdullah","year":"2025","journal-title":"Eng. Struct."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhao, S., and Wang, R. (2022). ESMD-WSST high-frequency de-noising method for bridge dynamic deflection using GB-SAR. Electronics, 12.","DOI":"10.3390\/electronics12010054"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.geog.2022.08.005","article-title":"BDS\/GPS deformation analysis of a long-span cable-stayed bridge based on colored noise filtering","volume":"14","author":"Ma","year":"2023","journal-title":"Geod. Geodyn."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Langbein, J. (2008). Noise in GPS displacement measurements from Southern California and Southern Nevada. J. Geophys. Res. Solid Earth, 113.","DOI":"10.1029\/2007JB005247"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jseaes.2018.07.025","article-title":"Accuracy enhancement of high-rate GNSS positions using a complete ensemble empirical mode decomposition-based multiscale multiway PCA","volume":"169","author":"Li","year":"2019","journal-title":"J. Asian Earth Sci."},{"key":"ref_34","first-page":"1191","article-title":"Denoising method of steel frame structure settlement data based on CEEMD and improved Wavelet Threshold method","volume":"42","author":"Wang","year":"2022","journal-title":"J. Geod. Geodyn."},{"key":"ref_35","first-page":"1148","article-title":"Seismic attenuation analysis using ensemble empirical mode decomposition and wavelet transform","volume":"51","author":"Xue","year":"2016","journal-title":"Oil Geophys. Prospect."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise-assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1109\/LGRS.2015.2415736","article-title":"GPR signal denoising and target extraction with the CEEMD method","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19416","DOI":"10.3390\/s150819416","article-title":"Ocean wave separation using CEEMD-Wavelet in GPS wave measurement","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhao, X., Liu, W., Du, J., Zhao, D., and Yu, Z. (2023). Impact-Type Sunflower Yield Sensor Signal Denoising Method Based on CEEMD-WTD. Agriculture, 13.","DOI":"10.3390\/agriculture13010166"},{"key":"ref_40","first-page":"3195492","article-title":"A new wavelet threshold function and denoising application","volume":"2016","author":"Hong","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, S., Kang, C., Li, G., and Li, C. (2020). Integration of wavelet denoising and HHT applied to the analysis of bridge dynamic characteristics. Appl. Sci., 10.","DOI":"10.3390\/app10103605"},{"key":"ref_42","first-page":"110","article-title":"A hybrid denoising method for bridge vibration signal based on EMD and wavelet threshold","volume":"66","author":"WANG","year":"2021","journal-title":"Highway"},{"key":"ref_43","first-page":"197","article-title":"Noise analysis of GPS continuous observation stations","volume":"29","author":"Huang","year":"2007","journal-title":"J. Earthq."},{"key":"ref_44","first-page":"119","article-title":"CEEMDAN-SG based blast shock wave denoising algorithm research","volume":"41","author":"Zhang","year":"2022","journal-title":"Foreign Electron. Meas. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.isatra.2021.07.011","article-title":"Improved Hilbert\u2013Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings","volume":"125","author":"Liu","year":"2022","journal-title":"ISA Trans."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:17Z","timestamp":1760029997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":45,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040588"],"URL":"https:\/\/doi.org\/10.3390\/sym17040588","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,12]]}}}