Luc Merchez¹ , Hélène Mathian² , Julie Le Gall³

Revue Internationale de Géomatique, Vol.30, No.1, pp. 85-104, 2020, DOI:10.3166/rig.2020.00103

Abstract La question de l’alimentation et de la caractérisation des environnements alimentaires a déjà fait l’objet de nombreuses études et développements méthodologiques pour rendre compte des différentiels d’accessibilité. Aux Etats-Unis, essentiellement à l’aune de questions sur la santé, ces études ont conduit à identifier des « déserts alimentaires . Cette question éminemment spatiale, qui repose sur la notion d’accessibilité, est souvent approchée par des enquêtes et entretiens ou des approches quantitatives basées sur des calculs d’accessibilités géographiques. Dans la lignée de ces travaux, nous proposons d’explorer la transférabilité de cette notion de « désert » à un espace métropolitain français. La… More >

Thierry Ramadier

Revue Internationale de Géomatique, Vol.30, No.1, pp. 13-35, 2020, DOI:10.3166/rig.2020.00101

Abstract Cet article s’appuie sur un ensemble de recherches sur la cognition spatiale afin de montrer, d’une part, que des résultats encore éparses plaident un faveur d’une construction sociale des « cartes mentales , et d’autre part, que c’est en conjuguant l’analyse des significations sociales de l’espace géographique (cognition environnementale) avec celle des distributions topologiques des éléments géographiques intériorisées par les individus (cognition spatiale) qu’il est possible de rendre compte de cette socialisation de la cartographie cognitive. L’auteur interroge ainsi les raisons qui font obstacle à une analyse socio-cognitive des « cartes mentales , alors que, par ailleurs, les représentations cartographiques… More >

Christine Plumejeaud-Perreau¹ , Lucie Nahassia² , Julie Gravier²

Revue Internationale de Géomatique, Vol.31, No.1, pp. 47-80, 2022, DOI:10.3166/RIG31.47-80

Abstract À travers trois exemples issus d’études des dynamiques de peuplement en France sur le temps long, intra et interurbaines, cet article montre que l’introduction d’ontologies « ahistoriques » a facilité la mise en place d’analyses numériques quantifiant les dynamiques spatiales. Cependant il démontre que ces ontologies ne sont pas neutres, qu’elles ne dissolvent pas la spécificité des sources mobilisées, et constituent au contraire des savoirs situés. Mais au-delà de ces critiques, les auteures tendent à argumenter que le processus de construction et de dialogue établi autour d’ontologie a-historique est très bénéfique à la recherche, qu’il est un socle pour une… More >

Wenbo Li, Qi Wang^*, Shang Gao

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 921-938, 2023, DOI:10.32604/iasc.2023.038257

Abstract Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise… More >

Zimeng Yang, Qiulan Wu, Feng Zhang^*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558

Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter),… More >

Jun Lee¹, Hyun Kwon^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 369-384, 2023, DOI:10.32604/iasc.2023.036052

Abstract Enhancing the sense of presence of participants is an important issue in terms of security awareness for remote collaboration in extended reality. However, conventional methods are insufficient to be aware of remote situations and to search for and control remote workspaces. This study proposes a spatial multi-presence system that simultaneously provides multiple spaces while rapidly exploring these spaces as users perform collaborative work in an extended reality environment. The proposed system provides methods for arranging and manipulating remote and personal spaces by creating an annular screen that is invisible to the user. The user can freely arrange remote participants and… More >

Song Yu^*, Chen Wang

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162

Abstract PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and… More >

Ravikan Nonkhuntod, Suchuan Yu^*

International Journal of Mental Health Promotion, Vol.25, No.5, pp. 613-626, 2023, DOI:10.32604/ijmhp.2023.025648

Abstract Due to peer pressure playing a crucial role in the decision to drink, people who have a more fragile temperament might be expected to be at higher risk. Moreover, many studies have investigated the influence of peer pressure on alcohol consumption, but few have examined the relationship between heavy drinking and peer pressure via a spatial autoregressive model (SAR) in low/middle-income countries, such as Thailand. This paper investigated the connection between heavy drinkers over the age of 15 years who drink more than or equal to 60 grams of unmixed alcohol at least once per month based on the Thai… More >

Ahmed Fahim^1,2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3695-3712, 2023, DOI:10.32604/cmc.2023.036820

Abstract Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so… More >

Xiaoyan Zhao^1,*, Lin Zhou², Yue Xie¹, Ying Tong¹, Jingang Shi³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3087-3100, 2023, DOI:10.32604/iasc.2023.030180

Abstract Speech separation is an active research topic that plays an important role in numerous applications, such as speaker recognition, hearing prosthesis, and autonomous robots. Many algorithms have been put forward to improve separation performance. However, speech separation in reverberant noisy environment is still a challenging task. To address this, a novel speech separation algorithm using gate recurrent unit (GRU) network based on microphone array has been proposed in this paper. The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost. The proposed algorithm extracts the sub-band steered response power-phase transform (SRP-PHAT) weighted… More >