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Free decay and excitation of the chandler wobble: self-consistent estimates of the period and quality factor / Wei Chen in Journal of geodesy, vol 97 n° 4 (April 2023)  

Public [article]  inJournal of geodesy > vol 97 n° 4 (April 2023) . - n° 36 Titre : Free decay and excitation of the chandler wobble: self-consistent estimates of the period and quality factor Type de document : Article/Communication Auteurs : Wei Chen, Auteur ; Yifei Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] mouvement du pôle [Termes IGN] terme de Chandler Résumé : (auteur) The period TCW and quality factor QCW of the Chandler wobble (CW) as well as polar motion (PM) transfer functions are all determined by the Earth’s layered structure, mass distribution, elasticity, rheology and energy dissipation, via the Earth’s dynamic figure parameters and complex degree-2 Love numbers. However, most previous studies used geophysical excitations derived from real-valued PM transfer functions to invert for TCW and QCW, thus leading to results that are not self-consistent. By separating the observed PM into the freely decaying CW and the excited PM, a traverse-based method is proposed to search values of TCW and QCW that can fit both sides simultaneously, yielding the self-consistent estimates of TCW = 430.4 mean solar days and QCW = 130. This implies the degree-2 tidal Love number k = 0.35011 − 0.00226i and load Love number k' =  − 0.36090 + 0.00233i, and the PM transfer functions TNL = 1.80001 − 0.00692i (non-loading) and TL = 1.15040 − 0.00023i (loading) valid at the Chandler period. Numéro de notice : A2023-176 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-023-01727-z Date de publication en ligne : 10/04/2023 En ligne : https://doi.org/10.1007/s00190-023-01727-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103287 [article]

Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica, vol 26 n° 2 (April 2022)  

Public [article]  inGeoinformatica > vol 26 n° 2 (April 2022) . - pp 429 - 447 Titre : Graph neural network based model for multi-behavior session-based recommendation Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Ruoqian Zhang, Auteur ; Wei Chen, Auteur ; Junhua Fang, Auteur Année de publication : 2022 Article en page(s) : pp 429 - 447 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] attention (apprentissage automatique) [Termes IGN] comportement [Termes IGN] consommation [Termes IGN] modèle de simulation [Termes IGN] réseau neuronal de graphes [Termes IGN] réseau sémantique [Termes IGN] service fondé sur la position Mots-clés libres : session Résumé : (auteur) Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior transition through feature-level representation learning. Besides, sequential and non-sequential patterns should be properly fused in session modeling to capture dynamic interests within the session. To this end, this paper proposes a Graph Neural Network based Hybrid Model GNNH, which enables feature-level deeper representations of multi-behavior interaction sequences for session-based recommendation. Specifically, we first construct multi-relational item graph (MRIG) and feature graph (MRFG) based on session sequences. On top of the MRIG and MRFG, our model takes advantage of GNN to capture item and feature representations, such that global item-to-item and feature-to-feature relations are fully preserved. Afterwards, each multi-behavior session is modeled by a seamless fusion of interacted item and feature representations, where self-attention and mean-pooling are used to obtain sequential and non-sequential patterns simultaneously. Experiments on two real datasets show that the GNNH model significantly outperforms the state-of-the-art methods. Numéro de notice : A2022-326 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-021-00439-w Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.1007/s10707-021-00439-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100489 [article]

A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping / Wei Chen in Geocarto international, vol 32 n° 4 (April 2017)  

Public [article]  inGeocarto international > vol 32 n° 4 (April 2017) . - pp 367 - 385 Titre : A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Wei Chen, Auteur ; Hamid Reza Pourghasemi, Auteur ; Zhou Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 367 - 385 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse comparative [Termes IGN] ArcGIS [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par réseau neuronal [Termes IGN] effondrement de terrain [Termes IGN] régression logistique [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (Auteur) The main aim of present study is to compare three GIS-based models, namely Dempster–Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide susceptibility mapping in the Shangzhou District of Shangluo City, Shaanxi Province, China. At First, landslide locations were identified by aerial photographs and supported by field surveys, and a total of 145 landslide locations were mapped in the study area. Subsequently, the landslide inventory was randomly divided into two parts (70/30) using Hawths Tools in ArcGIS 10.0 for training and validation purposes, respectively. In the present study, 14 landslide conditioning factors such as altitude, slope angle, slope aspect, topographic wetness index, sediment transport index, stream power index, plan curvature, profile curvature, lithology, rainfall, distance to rivers, distance to roads, distance to faults and normalized different vegetation index were used to detect the most susceptible areas. In the next step, landslide susceptible areas were mapped using the DS, LR and ANN models based on landslide conditioning factors. Finally, the accuracies of the landslide susceptibility maps produced from the three models were verified using the area under the curve (AUC). The validation results showed that the landslide susceptibility map generated by the ANN model has the highest training accuracy (73.19%), followed by the LR model (71.37%), and the DS model (66.42%). Similarly, the AUC plot for prediction accuracy presents that ANN model has the highest accuracy (69.62%), followed by the LR model (68.94%), and the DS model (61.39%). According to the validation results of the AUC curves, the map produced by these models exhibits the satisfactory properties. Numéro de notice : A2017-271 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1140824 Date de publication en ligne : 22/03/2016 En ligne : http://doi.org/10.1080/10106049.2016.1140824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85297 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2017041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Consistent estimates of the dynamic figure parameters of the Earth / Wei Chen in Journal of geodesy, vol 89 n° 2 (February 2015)  

Public [article]  inJournal of geodesy > vol 89 n° 2 (February 2015) . - pp 179 - 188 Titre : Consistent estimates of the dynamic figure parameters of the Earth Type de document : Article/Communication Auteurs : Wei Chen, Auteur ; Jiancheng Li, Auteur ; Jim Ray, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 179 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] champ de pesanteur terrestre [Termes IGN] ellipsoïde (géodésie) [Termes IGN] figure de la Terre [Termes IGN] masse de la Terre [Termes IGN] moment d'inertie Résumé : (auteur) The Earth’s dynamic figure parameters, namely the principal moments of inertia and dynamic ellipticities of the whole Earth, the fluid outer core and the solid inner core, are fundamental parameters for geodetic, geophysical and astronomical studies. This study aims to re-estimate the mass and the dynamic figure parameters of the Earth on the basis of some global gravity models (EGM2008, EIGEN-6C and EIGEN-6C2) recently released with unprecedented accuracies, as well as an improved value of the gravitational constant G recommended by the Committee on Data for Science and Technology (CODATA). With the potential coefficients of EGM2008, EIGEN-6C and EIGEN-6C2 rescaled to be consistent with the IAU (International Astronomical Union) and IAG (International Association of Geodesy) numerical standards, and other values of relevant parameters also being consistent with those numerical standards, we have obtained consistent estimates of the dynamic figure parameters of the stratified Earth using the theory described in Chen and Shen (J Geophys Res 115:B12419 2010). Our preferred principal moments of inertia for the whole Earth are A=(80,085.1±9.6)×1033 kg m2,B=(80,086.8±9.6)×1033 kg m2, and C=(80,349.0±9.6)×1033 kg m2, respectively, the accuracies being limited by the uncertainties of G and e (dynamic ellipticity of the whole Earth). Numéro de notice : A2015-333 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-014-0768-y Date de publication en ligne : 29/10/2014 En ligne : https://doi.org/10.1007/s00190-014-0768-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76658 [article]