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dc.contributor.author | Vitale, Raffaele | es_ES |
dc.contributor.author | Zhyrova, Anna | es_ES |
dc.contributor.author | Fortuna, Joao F. | es_ES |
dc.contributor.author | De Noord, Onno E. | es_ES |
dc.contributor.author | Ferrer, Alberto | es_ES |
dc.contributor.author | Martens, Harald | es_ES |
dc.date.accessioned | 2018-07-09T06:38:42Z | |
dc.date.available | 2018-07-09T06:38:42Z | |
dc.date.issued | 2017 | es_ES |
dc.identifier.issn | 0169-7439 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/105527 | |
dc.description.abstract | [EN] A novel method and software system for rational handling of time series of multi-channel measurements is presented. This quantitative learning tool, the On-The-Fly Processing (OTFP), develops reduced-rank bilinear subspace models that summarise massive streams of multivariate responses, capturing the evolving covariation patterns among the many input variables over time and space. Thereby, a considerable data compression can be achieved without significant loss of useful systematic information. The underlying proprietary OTFP methodology is relatively fast and simple it is linear/bilinear and does not require a lot of raw data or huge cross-correlation matrices to be kept in memory. Unlike conventional compression methods, the approach allows the high-dimensional data stream to be graphically interpreted and quantitatively utilised in its compressed state. Unlike adaptive moving-window methods, it allows all past and recent time points to be reconstructed and displayed simultaneously. This new approach is applied to four different case-studies: (i) multi-channel Vis-NIR spectroscopy of the Belousov Zhabotinsky reaction, a complex, ill understood chemical process; (ii) quality control of oranges by hyperspectral imaging; (iii) environmental monitoring by airborne hyperspectral imaging; (iv) multi-sensor process analysis in the petrochemical industry. These examples demonstrate that the OTFP can automatically develop high-fidelity subspace data models, which simplify the storage/transmission and the interpretation of more or less continuous time series of high-dimensional measurements to the extent there are covariations among the measured variables. | es_ES |
dc.description.sponsorship | This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R, Shell Global Solutions International B.V. (Amsterdam, The Netherlands), Idletechs AS (Trondheim, Norway), the Norwegian Research Council (Grant 223254) through the Centre of Autonomous Marine Operations and Systems (AMOS) at the Norwegian University of Science and Technology (Trondheim, Norway) and the Ministry of Education, Youth and Sports of the Czech Republic (CENAKVA project CZ.1.05/2.1.00/01.0024 and CENAKVA II project L01205 under the NPU I program). The authors want to acknowledge Prof. Bjorn Alsberg for providing the Vis-NIR equipment and the Laboratorio de Sistemas e Tecnologia Subaquatica of the University of Porto, the Hydrographic Institute of the Portuguese Navy and the University of the Azores for carrying out the REP15 exercise, during which the hyperspectral push broom image was collected. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Chemometrics and Intelligent Laboratory Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | On-the-fly processing (OTFP) | es_ES |
dc.subject | Bilinear modelling | es_ES |
dc.subject | High-dimensional data streams | es_ES |
dc.subject | Generalised Taylor expasion | es_ES |
dc.subject | Singular value decomposition (SVD) | es_ES |
dc.subject | BIG DATA analytics | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | On-The-Fly Processing of continuous high-dimensional data streams | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.chemolab.2016.11.003 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//DPI2014-55276-C5-1-R/ES/BIOLOGIA SINTETICA PARA LA MEJORA EN BIOPRODUCCION: DISEÑO, OPTIMIZACION, MONITORIZACION Y CONTROL/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.date.embargoEndDate | 2019-06-15 | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Vitale, R.; Zhyrova, A.; Fortuna, JF.; De Noord, OE.; Ferrer, A.; Martens, H. (2017). On-The-Fly Processing of continuous high-dimensional data streams. Chemometrics and Intelligent Laboratory Systems. 161:118-129. doi:10.1016/j.chemolab.2016.11.003 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.chemolab.2016.11.003 | es_ES |
dc.description.upvformatpinicio | 118 | es_ES |
dc.description.upvformatpfin | 129 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 161 | es_ES |
dc.relation.pasarela | S\336092 | es_ES |
dc.contributor.funder | Ministerio de Economía, Industria y Competitividad | es_ES |
dc.contributor.funder | Shell Global Solutions International B.V. | es_ES |