Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91614
DC FieldValueLanguage
dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorWan, HPen_US
dc.creatorDong, GSen_US
dc.creatorLuo, Yen_US
dc.creatorNi, YQen_US
dc.date.accessioned2021-11-19T05:58:16Z-
dc.date.available2021-11-19T05:58:16Z-
dc.identifier.issn0888-3270en_US
dc.identifier.urihttp://hdl.handle.net/10397/91614-
dc.language.isoenen_US
dc.publisherAcademic Pressen_US
dc.subjectBayesian compression sensingen_US
dc.subjectComplex-valued domainen_US
dc.subjectMulti-task learningen_US
dc.subjectDiscrete Fourier basisen_US
dc.subjectStructural health monitoring (SHM)en_US
dc.titleAn improved complex multi-task Bayesian compressive sensing approach for compression and reconstruction of SHM dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume167en_US
dc.identifier.doi10.1016/j.ymssp.2021.108531en_US
dcterms.abstractThe long-term structural health monitoring (SHM) provides massive data, leading to a high demand for data transmission and storage. Compressive sensing (CS) has great potential in alleviating this problem by using less samples to recover the complete signals utilizing the sparsity. Vibration data collected by an SHM system is usually sparse in the frequency domain, and the peaks in their Fourier spectra most often correspond to the same frequencies. This underlying commonality among the signals can be utilized by multi-task learning technique to improve the computational efficiency and accuracy. While being real-valued originally, the data after discrete Fourier transformation are in general complex-valued. In this paper, an improved complex multi-task Bayesian CS (CMT-BCS) method is developed for compression and reconstruction of SHM data requiring a high sampling rate. The novelty of the proposed method is twofold: (i) it overcomes the invalidity of the conventional CMT-BCS approach in dealing with the ‘incomplete’ CS problem, and (ii) it improves the computational efficiency of conventional CMT-BCS approach. The former is achieved by restructuring the CMT-BCS formulation, and the latter is realized by sharing a common sampling matrix across all tasks of concern. The improved CMT-BCS is evaluated using the shaking table test data of a scale-down frame model and the real-world SHM data acquired from a supertall building. A comparison with several existing BCS methods that enable to deal with complex values is also provided to demonstrate the effectiveness of the proposed method.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationMechanical systems and signal processing, 15 Mar. 2022, v. 167, part A, 108531en_US
dcterms.isPartOfMechanical systems and signal processingen_US
dcterms.issued2022-03-15-
dc.identifier.isiWOS:000711311500004-
dc.identifier.scopus2-s2.0-85117862128-
dc.identifier.eissn1096-1216en_US
dc.identifier.artn108531en_US
dc.description.validate202111 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1074-n01-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by the Zhejiang Provincial Key Research and Development Program (2021C03154), the National Natural Science Foundation of China (Grant Nos. 51878235 and 51778568), the Fundamental Research Funds for the Central Universities (Grant Nos. 2020QNA4015 and 2020XZZX005-04), and the Research Grants Council of the Hong Kong Special Administrative Region, China (Grant No. PolyU 152014/18E).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2024-03-15en_US
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