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Showing 1 - 20 results of 678 for search '(( elements method algorithm ) OR ((( waste processing algorithm ) OR ( level fusion algorithm ))))', query time: 0.54s Refine Results
  1. 1
    This is The amount of medical waste generated and processed in China.

    This is The amount of medical waste generated and processed in China. by Chen Hao (352436)

    Published 2025
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  2. 2
    The amount of medical waste generated and processed in China.

    The amount of medical waste generated and processed in China. by Chen Hao (352436)

    Published 2025
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  3. 3
    Automated Resource Recovery Processes for Electronic Waste Recycling

    Automated Resource Recovery Processes for Electronic Waste Recycling by Aseni Senanayake (21584965)

    Published 2025
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  4. 4
    Decoding process.

    Decoding process. by Jihui Hu (19812736)

    Published 2024
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  5. 5
    The method for determining the chemical constituents of unknown materials in the algorithm.

    The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

    Published 2025
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  6. 6
    NSGA-II process based on minimum envelope clustering.

    NSGA-II process based on minimum envelope clustering. by Chen Hao (352436)

    Published 2025
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  7. 7
    Flowchart of LFAM for feature fusion.

    Flowchart of LFAM for feature fusion. by Dinghong Mu (14204200)

    Published 2024
    Subjects: “…proposed optimization algorithm…”
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  8. 8
    The model parameters of SIFusion and comparative algorithms.

    The model parameters of SIFusion and comparative algorithms. by Song Qian (5031221)

    Published 2024
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  9. 9
    Comparison of algorithms across metrics in test problems.

    Comparison of algorithms across metrics in test problems. by Jihui Hu (19812736)

    Published 2024
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  10. 10
    The selection process of the ONDL model among other models.

    The selection process of the ONDL model among other models. by Nour Eldeen Mahmoud Khalifa (19259450)

    Published 2024
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  11. 11
    Scatter diagram of different principal elements.

    Scatter diagram of different principal elements. by Jizhong Wang (7441697)

    Published 2025
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  12. 12
    ANOVA was performed to test the differences between the algorithms for each indicator.

    ANOVA was performed to test the differences between the algorithms for each indicator. by Jihui Hu (19812736)

    Published 2024
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  13. 13
    Schematic of Learnable weights fusion module.

    Schematic of Learnable weights fusion module. by Yazhou Li (277980)

    Published 2025
    “…Additionally, the model introduces a learnable weighted feature fusion module in the neck, which enhances the expression of features at multiple scales and different levels, thus improving the focus on key features. …”
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  14. 14
    Algorithm for optimizing the model using transformer.

    Algorithm for optimizing the model using transformer. by Weiling Liu (1465480)

    Published 2025
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  15. 15
    The AP for different detection algorithms on the RSOD dataset.

    The AP for different detection algorithms on the RSOD dataset. by Guoguang Hua (22305400)

    Published 2025
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  16. 16
    Calculation algorithm implementing the proposed model.

    Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

    Published 2025
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  17. 17
    Calculation algorithm implementing the proposed model.

    Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

    Published 2025
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  18. 18
    Comparison of detection results of different algorithms in RSOD dataset.

    Comparison of detection results of different algorithms in RSOD dataset. by Guoguang Hua (22305400)

    Published 2025
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  19. 19
    The precision-recall curve of different algorithms on RSOD dataset.

    The precision-recall curve of different algorithms on RSOD dataset. by Guoguang Hua (22305400)

    Published 2025
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  20. 20
    Algorithmic experimental parameter design.

    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”
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