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  1. 361

    Deep Learning Algorithm for Water Body Extraction from High-resolution Remote Sensing Images Based on Hierarchical Feature Extraction and Multi-scale Feature Fusion by SHENG Sheng, WAN Fangqi, LIN Kangling, HU Zhaoyang, CHEN Hua

    Published 2024-01-01
    “…Highly accurate water body extraction can be helpful for water resources monitoring and management.The current methods of water body extraction based on remote sensing images lack attention to boundary quality,resulting in inaccurate boundary delineation and low detail retention.To improve the boundary and detail accuracy of water body extraction for remote sensing images,this paper proposes a deep learning algorithm for water body extraction from high-resolution remote sensing images based on multi-scale feature fusion.The model includes a hierarchical feature extraction module and a stacked-connected decoder module that fuses multi-scale features.In the hierarchical feature extraction module,a channel attention structure is introduced for integrating shape,texture,and hue information of water bodies in high-resolution remote sensing images to better understand the shapes and boundaries of water bodies.In the stacked-connected decoder module that incorporates multi-scale features,the stacked connection of multi-level semantic information and enhanced feature extraction are performed.Meanwhile,broad background information and fine detail information are captured to achieve better water body extraction results.Experimental results on both self-annotated and publicly available datasets show that the model yields 98.37% and 91.23% accuracy,and extracts more complete edges of water bodies while retaining more details than existing semantic segmentation models.The proposed model improves the accuracy and generalization ability of water body extraction and provides references for water body extraction from high-resolution remote sensing images.…”
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    WE INTER-ARE: THE PANDEMIC CHALLENGE REDEFINING HUMANS AND COMMUNITIES by K. Abraham

    Published 2023-04-01
    Subjects: “…Hierarchical-problematic…”
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    A Novel Networking Scheme for Hierachical Heterogenous Cellular Network Based on the Phantom Cell Architecture by Yue Li, Mugen Peng

    Published 2013-01-01
    Subjects: “…hierarchical heterogeneous cellular network…”
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    Interfacial charge transfer on hierarchical synergistic shell wall of MXene/MoS2 on CdS nanospheres: heterostructure integrity for visible light responsive photocatalytic H2 evolution by Kugalur Shanmugam Ranjith, Ali Mohammadi, Ganji Seeta Rama Raju, Yun Suk Huh, Young-Kyu Han

    Published 2024-12-01
    “…In this paper we designed a ternary heterostructure assembly of CdS nanospheres wrapped with hierarchical shell walls of layered MXene-tagged MoS2 nanoflakes, forming intimate interfaces through an in-situ growth process. …”
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  17. 377

    Improved Photocatalytic Performance of a Novel Fe3O4@SiO2/Bi2SiO5 Hierarchical Nanostructure with Magnetic Recoverability by Xinxin Zhang, Xiaoli Dong, Baiyu Leng, Hongchao Ma, Xiufang Zhang

    Published 2015-01-01
    “…Magnetic Fe3O4@SiO2/Bi2SiO5 composites with a novel hierarchical nanostructure were synthesized by sol-gel and hydrothermal methods and were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and UV-visible diffuse reflectance spectroscopy (UV-vis DRS). …”
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  18. 378

    Synergistic Hierarchical Bi-Level Optimization of Demand Response Programs and Smart Inverter Volt/Var Control for Maximizing Hosting Capacity of Power Systems With Renewable-Based Distributed Generators by Zenhom M. Zenhom, Shady H. E. Abdel Aleem, Essam Aboul Zahab, Ziad M. Ali, Abdulaziz Almalaq, Tarek A. Boghdady

    Published 2025-01-01
    “…This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. …”
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