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Showing 1 - 20 results of 50 for search '(( elements box algorithm ) OR ((( elements mges algorithm ) OR ( neural codingn_ algorithm ))))', query time: 0.33s Refine Results
  1. 1
    Table_1_Files_Performance

    Table_1_Files_Performance by Raquel Quatrini (6161930)

    Published 2025
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  2. 2
    MOBHunter

    MOBHunter by Raquel Quatrini (6161930)

    Published 2024
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  3. 3
    Computational Procedure of the Particle Swarm Optimization (PSO) algorithm.

    Computational Procedure of the Particle Swarm Optimization (PSO) algorithm. by Meijun Shang (22806461)

    Published 2025
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  4. 4
    Depicts the schematic of the algorithm.

    Depicts the schematic of the algorithm. by Dong Hyeok Choi (12562048)

    Published 2025
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  5. 5
    Heavy-load transfer steel platform.

    Heavy-load transfer steel platform. by Meijun Shang (22806461)

    Published 2025
    “…A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”
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  6. 6
    Traditional scaffolding reinforcement system.

    Traditional scaffolding reinforcement system. by Meijun Shang (22806461)

    Published 2025
    “…A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”
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  7. 7
    Iteration curve of the optimization process.

    Iteration curve of the optimization process. by Meijun Shang (22806461)

    Published 2025
    “…A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”
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  8. 8
    (a) Geant4 modeling for calculating mass attenuation coefficients.

    (a) Geant4 modeling for calculating mass attenuation coefficients. by Dong Hyeok Choi (12562048)

    Published 2025
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  9. 9
    Ablation study visualization results.

    Ablation study visualization results. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  10. 10
    Experimental parameter configuration.

    Experimental parameter configuration. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  11. 11
    FLMP-YOLOv8 identification results.

    FLMP-YOLOv8 identification results. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  12. 12
    C2f structure.

    C2f structure. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  13. 13
    Experimental environment configuration.

    Experimental environment configuration. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  14. 14
    Ablation experiment results table.

    Ablation experiment results table. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  15. 15
    YOLOv8 identification results.

    YOLOv8 identification results. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  16. 16
    LSKA module structure diagram.

    LSKA module structure diagram. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  17. 17
    Comparison of mAP curves in ablation experiments.

    Comparison of mAP curves in ablation experiments. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  18. 18
    FarsterBlock structure.

    FarsterBlock structure. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  19. 19
    Sample augmentation and annotation illustration.

    Sample augmentation and annotation illustration. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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  20. 20
    YOLOv8 model architecture diagram.

    YOLOv8 model architecture diagram. by Xiaozhou Feng (2918222)

    Published 2025
    “…Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”
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