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Showing 1 - 20 results of 50 for search '(( binary image scale optimization algorithm ) OR ( lines based field optimization algorithm ))', query time: 0.53s Refine Results
  1. 1
    The losses of the training process before and after the optimization of loss function.

    The losses of the training process before and after the optimization of loss function. by Peihan Wen (6324315)

    Published 2022
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  2. 2
    mAP@0.5 line charts for different models.

    mAP@0.5 line charts for different models. by Meiling Shi (13148944)

    Published 2024
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  3. 3
    Supplementary file 1_Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field.pdf

    Supplementary file 1_Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field.pdf by Shuangshuang Zhang (701020)

    Published 2025
    “…Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. After comparison and verification, it is proved to be accurate and reliable. …”
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  4. 4
    Validation of image processing steps and metric results using images of a microfluidic device.

    Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

    Published 2021
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  5. 5
    Improved LMedS-based for sag measurement accuracy of transmission lines via PSO method

    Improved LMedS-based for sag measurement accuracy of transmission lines via PSO method by Xingzhi Ren (21400870)

    Published 2025
    “…Furthermore, a transmission line sag measurement method based on an improved Least Median of Squares (LMedS) algorithm is introduced. …”
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  6. 6
    tUbenet Foundation Model Weights

    tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

    Published 2025
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  7. 7
    Flowchart of software workflow.

    Flowchart of software workflow. by Gavrielle R. Untracht (11820311)

    Published 2021
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  8. 8
    Table 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx

    Table 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx by Hongwei Li (17402)

    Published 2025
    “…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”
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  9. 9
    Table 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx

    Table 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.docx by Hongwei Li (17402)

    Published 2025
    “…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”
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  10. 10
    Image 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif

    Image 1_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

    Published 2025
    “…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”
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  11. 11
    Image 3_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif

    Image 3_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

    Published 2025
    “…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”
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  12. 12
    Image 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif

    Image 2_Pixel-wise navigation line extraction of cross-growth-stage seedlings in complex sugarcane fields and extension to corn and rice.tif by Hongwei Li (17402)

    Published 2025
    “…Nevertheless, in field management of crop seedlings, numerous available studies involving navigation line extraction mainly focused on specific growth stages of specific crop seedlings so far, lacking a generalizable algorithm for addressing challenges under complex cross-growth-stage seedling conditions. …”
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  13. 13
    Fig 11 -

    Fig 11 - by Masashi Yagi (3186909)

    Published 2023
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  14. 14
    Fig 14 -

    Fig 14 - by Masashi Yagi (3186909)

    Published 2023
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  15. 15
    Fig 9 -

    Fig 9 - by Masashi Yagi (3186909)

    Published 2023
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  16. 16
    Fig 6 -

    Fig 6 - by Masashi Yagi (3186909)

    Published 2023
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  17. 17
    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

    Published 2019
    “…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”
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  18. 18
    Experimental observation of gastruloids and shape quantification using LOCO-EFA.

    Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

    Published 2024
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  19. 19
    Fig 3 -

    Fig 3 - by Ali Ahmed (2567584)

    Published 2020
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  20. 20
    Graphical implementation of the links.

    Graphical implementation of the links. by Thibault Rolland (14833626)

    Published 2023
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