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Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system / Elahe S. Abdolkarimi in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system Type de document : Article/Communication Auteurs : Elahe S. Abdolkarimi, Auteur ; Mohammad-Reza Mosavi, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] coût [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GPS-INS [Termes descripteurs IGN] imprécision des données [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] transformation en ondelettes [Termes descripteurs IGN] vitesse de déplacement Résumé : (auteur) The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage. Numéro de notice : A2020-084 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0951-y date de publication en ligne : 11/01/2020 En ligne : https://doi.org/10.1007/s10291-020-0951-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94654 [article]

Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 149 - 166 Titre : Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference Type de document : Article/Communication Auteurs : Heidar Rastiveis, Auteur ; Alireza Shams, Auteur ; Wayne A. Sarasua, Auteur ; Jonathan Li, Auteur Année de publication : 2020 Article en page(s) : pp 149 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] autoroute [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] extraction de points [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] lidar mobile [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transformation de Hough Résumé : (Auteur) Mobile LiDAR systems (MLS) are rapid and accurate technologies for acquiring three-dimensional (3D) point clouds that can be used to generate 3D models of road environments. Because manual extraction of desirable features such as road traffic signs, trees, and pavement markings from these point clouds is tedious and time-consuming, automatic information extraction of these objects is desirable. This paper proposes a novel automatic method to extract pavement lane markings (LMs) using point attributes associated with the MLS point cloud based on fuzzy inference. The proposed method begins with dividing the MLS point cloud into a number of small sections (e.g. tiles) along the route. After initial filtering of non-ground points, each section is vertically aligned. Next, a number of candidate LM areas are detected using a Hough Transform (HT) algorithm and considering a buffer area around each line. The points inside each area are divided into “probable-LM” and “non-LM” clusters. After extracting geometric and radiometric descriptors for the “probable-LM” clusters and analyzing them in a fuzzy inference system, true-LM clusters are eventually detected. Finally, the extracted points are enhanced and transformed back to their original position. The efficiency of the method was tested on two different point cloud datasets along 15.6 km and 9.5 km roadway corridors. Comparing the LMs extracted using the algorithm with the manually extracted LMs, 88% of the LM lines were successfully extracted in both datasets. Numéro de notice : A2020-047 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.009 date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94558 [article]

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Spatial distribution of coal quality parameters with respect to production requirements: an adaptive neuro-fuzzy application for the Can coal field (Turkey) / Ali Kayabasi in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)  

Public [article]  inGeocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 193-209 Titre : Spatial distribution of coal quality parameters with respect to production requirements: an adaptive neuro-fuzzy application for the Can coal field (Turkey) Type de document : Article/Communication Auteurs : Ali Kayabasi, Auteur ; Dilek Turer, Auteur ; Nurgul Yesiloglu-Gultekin, Auteur Année de publication : 2016 Article en page(s) : pp 193-209 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] carte géologique [Termes descripteurs IGN] grotte [Termes descripteurs IGN] impact sur l'environnement [Termes descripteurs IGN] inférence [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] mine de charbon [Termes descripteurs IGN] outil d'aide à la décision [Termes descripteurs IGN] pollution atmosphérique [Termes descripteurs IGN] répartition géographique Résumé : (Auteur) Determination of spatial distribution of coal quality parameters can ease management of the operations in coal mines. In this study, in order to provide guidance for the excavations, Can coal mine production map showing regions having suitable coal parameters as feed coals for a power plant and also for public sale was prepared using adaptive neuro-fuzzy inference system tool. Statistical relationships among calorific value, ash content and sulphur content were evaluated using the data obtained from boreholes opened in the mine between 2006 and 2009. According to the obtained production map, coals of Can mine are not suitable for public sale because of their high sulphur content and hence they should be blended with low sulphur coals to meet the requirements, before sale. Numéro de notice : A2016-107 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1041564 date de publication en ligne : 20/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1041564 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80002 [article]

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Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images / S. Al-Kheder in International journal of geographical information science IJGIS, vol 22 n°11-12 (november 2008)

Public [article]  inInternational journal of geographical information science IJGIS > vol 22 n°11-12 (november 2008) . - pp 1271 - 1293 Titre : Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images Type de document : Article/Communication Auteurs : S. Al-Kheder, Auteur ; Jing Wang, Auteur ; J. Shan, Auteur Année de publication : 2008 Article en page(s) : pp 1271 - 1293 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] étalonnage des données [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] Indianapolis [Termes descripteurs IGN] Inférence floue [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] moteur d'inférence [Termes descripteurs IGN] urbanisation Résumé : (Auteur) This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighbourhood development level, which is taken by cellular automata as initial approximation for its transition rules. Such approximations are updated through spatial calibration over townships and temporal calibration with multi-temporal satellite images. Assessment of the modelling results is based on three evaluation measures: fitness and Type I and Type II errors. The approach is applied to model the growth of the city of Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100 + 20% with 30% average errors can be achieved for 80% of the townships in urban-growth prediction. Copyright Taylor & Francis Numéro de notice : A2008-403 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29395 [article]

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