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Showing 1 - 20 results of 157 for search '(( binary image codon optimization algorithm ) OR ( lines using robust optimization algorithm ))', query time: 0.59s Refine Results
  1. 1
    Comparison of optimization algorithms and their robustness to noise.

    Comparison of optimization algorithms and their robustness to noise. by Will Xiao (8975351)

    Published 2020
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  2. 2
    Example of non-optimal behaviour of the Greedy algorithm.

    Example of non-optimal behaviour of the Greedy algorithm. by Henriette Heer (9541510)

    Published 2020
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  3. 3
    The <i>D</i><sub><i>98</i></sub> in the CTV and the for the nominal scenario optimized using the WC algorithm in homogeneous and heterogeneous phantom.

    The <i>D</i><sub><i>98</i></sub> in the CTV and the for the nominal scenario optimized using the WC algorithm in homogeneous and heterogeneous phantom. by Masashi Yagi (3186909)

    Published 2023
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  4. 4
    The <i>D</i><sub><i>98</i></sub> in the CTV and the for the nominal scenario optimized using the SC algorithm in homogeneous and heterogeneous phantoms.

    The <i>D</i><sub><i>98</i></sub> in the CTV and the for the nominal scenario optimized using the SC algorithm in homogeneous and heterogeneous phantoms. by Masashi Yagi (3186909)

    Published 2023
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  5. 5
    Optimization parameters for the three treatment plans performed for the homogeneous and heterogeneous phantoms.

    Optimization parameters for the three treatment plans performed for the homogeneous and heterogeneous phantoms. by Masashi Yagi (3186909)

    Published 2023
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  6. 6
    DMTD algorithm.

    DMTD algorithm. by Yunhu Huang (21402795)

    Published 2025
    “…Finally, we conducted comparative tests of the manual driving, intelligent driving algorithm (ITOR, STON), and the algorithms proposed in this paper, EITO, using real line data from the Yizhuang Line of Beijing Metro (YLBS). …”
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  7. 7
    Robustness of the optimization process in a real dataset.

    Robustness of the optimization process in a real dataset. by Neta Ravid Tannenbaum (2940351)

    Published 2023
    “…It can be seen that the optimization algorithm which is used in this work to fit the iCVS model is affected by the supplied start points. …”
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  8. 8
    Poisson GAMs using MCEM (solid line) and a naïve model (dashed line) fitted to the air pollution data recorded in Milan, Italy 1980–1989.

    Poisson GAMs using MCEM (solid line) and a naïve model (dashed line) fitted to the air pollution data recorded in Milan, Italy 1980–1989. by Jakub Stoklosa (3315042)

    Published 2023
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  9. 9
    Algorithm parameters.

    Algorithm parameters. by Ruyi Dong (9038174)

    Published 2025
    “…Therefore, it can be shown that LLSKSO can be used as an effective optimization algorithm and engineering assistance.…”
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  10. 10
    The variation of the DVHs of dose distributions in the patient case reassessed for 28 different perturbations of beam ranges and positions in the CTV (orange) and OAR (light blue) for conventional IMCT, the range (setup) and gradient robust IMCT, and WC IMCT.

    The variation of the DVHs of dose distributions in the patient case reassessed for 28 different perturbations of beam ranges and positions in the CTV (orange) and OAR (light blue)... by Masashi Yagi (3186909)

    Published 2023
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  11. 11
    Fig 15 -

    Fig 15 - by Masashi Yagi (3186909)

    Published 2023
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  12. 12
    Fig 7 -

    Fig 7 - by Masashi Yagi (3186909)

    Published 2023
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  13. 13
    Dose distribution comparisons.

    Dose distribution comparisons. by Masashi Yagi (3186909)

    Published 2023
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  14. 14
    The SF of HSGc-C5 cells comparison among the experiment irradiated by IMCT (blue bar), the independent calculation (orange bar), and the experiment irradiated by SFUD (gray bar).

    The SF of HSGc-C5 cells comparison among the experiment irradiated by IMCT (blue bar), the independent calculation (orange bar), and the experiment irradiated by SFUD (gray bar). by Masashi Yagi (3186909)

    Published 2023
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  15. 15
    Fig 8 -

    Fig 8 - by Masashi Yagi (3186909)

    Published 2023
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  16. 16
    The heterogeneous phantom.

    The heterogeneous phantom. by Masashi Yagi (3186909)

    Published 2023
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  17. 17
    Fig 11 -

    Fig 11 - by Masashi Yagi (3186909)

    Published 2023
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  18. 18
    Treatment plan for the biological study.

    Treatment plan for the biological study. by Masashi Yagi (3186909)

    Published 2023
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  19. 19
    Fig 9 -

    Fig 9 - by Masashi Yagi (3186909)

    Published 2023
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  20. 20
    Fig 6 -

    Fig 6 - by Masashi Yagi (3186909)

    Published 2023
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