Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80443
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dc.contributor.authorRen, MJen_US
dc.contributor.authorCheung, CFen_US
dc.contributor.authorXiao, GBen_US
dc.date.accessioned2019-03-26T09:17:12Z-
dc.date.available2019-03-26T09:17:12Z-
dc.date.issued2018-
dc.identifier.citationSensors, Nov. 2018, v. 18, no. 11, 4069, p. 1-12en_US
dc.identifier.urihttp://hdl.handle.net/10397/80443-
dc.description.abstractThis paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.en_US
dc.description.sponsorshipChinese Mainland Affairs Officeen_US
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.relation.ispartofSensorsen_US
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Ren, M. J., Cheung, C. F., & Xiao, G. B. (2018). Gaussian process based bayesian inference system for intelligent surface measurement. Sensors, 18(11), 4069, 1-12 is available at https://dx.doi.org/10.3390/s18114069en_US
dc.subjectSurface measurementen_US
dc.subjectMulti-sensor measurementen_US
dc.subjectSurface modellingen_US
dc.subjectData fusionen_US
dc.subjectGaussian processen_US
dc.titleGaussian process based Bayesian inference system for intelligent surface measurementen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage12-
dc.identifier.volume18-
dc.identifier.issue11-
dc.identifier.doi10.3390/s18114069-
dc.identifier.isiWOS:000451598900479-
dc.identifier.scopus2-s2.0-85057137457-
dc.identifier.pmid30469404-
dc.identifier.eissn1424-8220-
dc.identifier.artn4069-
dc.description.validate201903 bcrc-
dc.description.oapublished_final-
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