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Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data / Qian Fan in Journal of applied geodesy, vol 14 n° 3 (July 2020)  

Public [article]  inJournal of applied geodesy > vol 14 n° 3 (July 2020) . - pp 253 – 261 Titre : Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data Type de document : Article/Communication Auteurs : Qian Fan, Auteur ; Xiaolin Meng, Auteur ; Dinh Tung Nguyen, Auteur ; Yilin Xie, Auteur ; Jiayong Yu, Auteur Année de publication : 2020 Article en page(s) : pp 253 – 261 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie moderne [Termes IGN] apprentissage automatique [Termes IGN] combinaison linéaire [Termes IGN] données GNSS [Termes IGN] méthode fondée sur le noyau [Termes IGN] pont [Termes IGN] prévention des risques [Termes IGN] série temporelle [Termes IGN] surveillance d'ouvrage Résumé : (auteur) Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data. Numéro de notice : A2020-396 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0057 Date de publication en ligne : 27/03/2020 En ligne : https://doi.org/10.1515/jag-2019-0057 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95431 [article]

Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages / Jian Wang in Survey review, vol 49 n° 357 (December 2017)  

Public [article]  inSurvey review > vol 49 n° 357 (December 2017) . - pp 419 - 427 Titre : Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages Type de document : Article/Communication Auteurs : Jian Wang, Auteur ; Houzeng Han, Auteur ; Xiaolin Meng, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 419 - 427 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] BDS-INS [Termes IGN] correction du signal [Termes IGN] double différence [Termes IGN] filtrage du bruit [Termes IGN] filtre de Kalman [Termes IGN] GPS-INS [Termes IGN] méthode robuste Résumé : (auteur) This paper proposes a robust wavelet-based tightly coupled Global Positioning System (GPS)/ Beidou Navigation Satellite System (BDS)/Inertial Navigation System (INS) integration scheme aiming to improve the overall position accuracy during signal outages. A robust wavelet denoising model based on α-trimmed mean filter demonstrates its effectiveness on noise reduction and gross error elimination of inertial sensor raw data. Thereafter, a robust wavelet-based tightly coupled GPS/BDS/INS integration scheme is proposed, and GPS/BDS double-difference (DD) carrier-phase and pseudorange measurements are introduced to build a 27-state tightly coupled GPS/BDS/INS integration equation. The extended Kalman filter (EKF) has been designed for state estimation, and the inclusion of BDS enhances the satellite geometric strength of the whole navigation system. A field vehicle test indicates the position accuracy of five simulated GPS/BDS outages can be improved by about 7, 16, and 33% for north, east, and up components with the proposed scheme compared to standard integration scheme, and gross errors have been detected and eliminated with the new integration scheme. The authors also find that the BDS phase residual is larger than GPS and satellite dependent in the tightly coupled GPS/BDS/INS integrated navigation system. Numéro de notice : A2017-756 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/00396265.2016.1190162 En ligne : https://doi.org/10.1080/00396265.2016.1190162 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89110 [article]