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scientifique

Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs / Yang Bai in Computers & geosciences, vol 146 (January 2021)  

Public [article]  inComputers & geosciences > vol 146 (January 2021) . - n° 104626 Titre : Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs Type de document : Article/Communication Auteurs : Yang Bai, Auteur ; Maojin Tan, Auteur Année de publication : 2021 Article en page(s) : n° 104626 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] puits de carbone [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] schiste Résumé : (auteur) The total organic carbon (TOC) content is of great significance to reflect the hydrocarbon-generation potential in shale reservoirs. The well logs were always used to predict the TOC content, but some linear regression methods do not match well with complex data. The neural network method can improve prediction accuracy, but it always generates unstable prediction models. A static committee machine can reduce errors and uncertainties by combining multiple learners, but the weight of integrating learners is difficult to determine. Therefore, a dynamic committee machine with fuzzy-c-means clustering (DCMF) was proposed to predict the TOC content. Experts in the DCMF include Elman neural network, extreme learning machine, and generalized regression neural network. The fuzzy-c-means clustering algorithm was used as the gate network to perform subtasks decomposition and weights calculation based on input data. The subtasks were used to train more adaptive TOC content prediction models, and the weights were transferred to the combiner to integrate all experts’ outputs into final results. The DCMF was applied in two wells located in the Jiumenchong formation in the Qiannan depression, China. The TOC prediction results using the DCMF method are more accurate than the linear regression method, three individual intelligent algorithms, and the static committee machine. The DCMF also provides a new method for weight calculation by mining potential information of input data. Numéro de notice : A2021-019 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2020.104626 date de publication en ligne : 17/10/2020 En ligne : https://doi.org/10.1016/j.cageo.2020.104626 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96512 [article]

Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation / George Grekousis in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 1 (January 2021) . - pp 152 - 174 Titre : Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation Type de document : Article/Communication Auteurs : George Grekousis, Auteur Année de publication : 2021 Article en page(s) : pp 152 - 174 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] données démographiques [Termes descripteurs IGN] New York (Etats-Unis ; ville) [Termes descripteurs IGN] optimisation par essaim de particules [Termes descripteurs IGN] pondération [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] santé [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] voisinage (topologie) Résumé : (auteur) Fuzzy geographically weighted clustering has been proposed as an approach for improving fuzzy c-means algorithm when applied to geodemographic analysis. This clustering method allows a spatial entity to belong to more than one cluster with varying degrees, namely, membership values. Although fuzzy geographically weighted clustering attempts to create geographically aware clusters, it partially fails to trace spatial dependence and heterogeneity because, as a global metric, the membership values are calculated across the entire set of spatial entities. Here we introduce the first local version of fuzzy geographically weighted clustering, ‘local fuzzy geographically weighted clustering.’ In local fuzzy geographically weighted clustering, the membership values of a spatial entity are updated only according to the membership values of the spatial entities within its neighborhood and not across the entire set of entities, as originally proposed by the global metric. Additionally, we apply particle swarm optimization meta-heuristic to overcome the random initialization problem regarding the fuzzy c-means algorithm. To evaluate our method we compare local fuzzy geographically weighted clustering to global fuzzy geographically weighted clustering using a cancer incident benchmark dataset for Manhattan, New York. The results show that local fuzzy geographically weighted clustering outperforms the global version in all experimental settings. Numéro de notice : A2021-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1808221 date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1080/13658816.2020.1808221 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96525 [article]

Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1930 - 1952 Titre : Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones Type de document : Article/Communication Auteurs : Xun Liang, Auteur ; Xiaoping Liu, Auteur ; Guangliang Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1930 - 1952 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] zone d'activité économique Résumé : (auteur) Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs. Numéro de notice : A2020-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1741591 date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1741591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95668 [article]

A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments / Elahe S. Abdolkarimi in GPS solutions, Vol 24 n° 4 (October 2020)  

Public [article]  inGPS solutions > Vol 24 n° 4 (October 2020) . - 19 p. Titre : A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments Type de document : Article/Communication Auteurs : Elahe S. Abdolkarimi, Auteur ; Mohammad-Reza Mosavi, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GPS-INS [Termes descripteurs IGN] microsystème électromécanique [Termes descripteurs IGN] modèle d'incertitude [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost low-precision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements. Numéro de notice : A2020-521 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01023-9 date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1007/s10291-020-01023-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95692 [article]

Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246 Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données altimétriques [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] figuré du terrain [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] montagne [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] sommet (relief) [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870 [article]

Hyperspectral image clustering with Albedo recovery Fuzzy C-Means / P. Azimpour in International Journal of Remote Sensing IJRS, vol 41 n°16 (01-10 May 2020)  

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Multi-factor of path planning based on an ant colony optimization algorithm / Mingchang Wang in Annals of GIS, vol 26 n° 2 (April 2020)  

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Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n°1 (2020)  

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INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)  

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Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

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Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) / Haowei Xu in GPS world, vol 28 n° 3 (March 2017)

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The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

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Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information / Alexis Comber in Geoinformatica [en ligne], vol 20 n° 3 (July - September 2016)  

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An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

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