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Showing 1 - 20 results of 108 for search '(( lines based robust optimization algorithm ) OR ( binary image global optimization algorithm ))', query time: 0.39s Refine Results
  1. 1
    The confidence curve of a binary image.

    The confidence curve of a binary image. by Dingming Yang (8505990)

    Published 2022
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  2. 2
    The confidence change of the binary image after iteration.

    The confidence change of the binary image after iteration. by Dingming Yang (8505990)

    Published 2022
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  3. 3
    Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale.

    Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

    Published 2023
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  4. 4
    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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  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
    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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  7. 7
    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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  8. 8
    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
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  9. 9
    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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  10. 10
    Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  11. 11
    Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  12. 12
    Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  13. 13
    Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  14. 14
    EITO<sub>P</sub> with flexible trip time.

    EITO<sub>P</sub> with flexible trip time. 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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  15. 15
    Energy consumption in the PPO process.

    Energy consumption in the PPO process. 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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  16. 16
    Speed limits.

    Speed limits. 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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  17. 17
    Running time in the PPO process.

    Running time in the PPO process. 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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  18. 18
    Speed limits and gradients from RJ to WYJ.

    Speed limits and gradients from RJ to WYJ. 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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  19. 19
    EITO<sub>E</sub> speed distance profile.

    EITO<sub>E</sub> speed distance profile. 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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  20. 20
    Speed limits and gradient from SJZ to XHM.

    Speed limits and gradient from SJZ to XHM. 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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