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Showing 81 - 100 results of 10,260 for search '(((( algorithm setup function ) OR ( algorithm model function ))) OR ( algorithm python function ))', query time: 0.45s Refine Results
  1. 81
    Main parameters of steering system.

    Main parameters of steering system. by Honglei Pang (22693724)

    Published 2025
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  2. 82
    HIL test results of low-adhesion road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    HIL test results of low-adhesion road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

    Published 2025
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  3. 83
    Co-simulation architecture.

    Co-simulation architecture. by Honglei Pang (22693724)

    Published 2025
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  4. 84
    Overall framework diagram of the study.

    Overall framework diagram of the study. by Honglei Pang (22693724)

    Published 2025
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  5. 85
    Vehicle parameters.

    Vehicle parameters. by Honglei Pang (22693724)

    Published 2025
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  6. 86
    HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

    Published 2025
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  7. 87
    Comparison of average reward values in various simulation scenarios.

    Comparison of average reward values in various simulation scenarios. by Honglei Pang (22693724)

    Published 2025
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  8. 88
    HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

    Published 2025
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  9. 89
    Calibration and sample phase objects for the JL setup.

    Calibration and sample phase objects for the JL setup. by Benedict Diederich (4903576)

    Published 2019
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  10. 90
    Results of searching performance of different algorithm models on the Sphere function and Griewank function.

    Results of searching performance of different algorithm models on the Sphere function and Griewank function. by Rui Zhang (13940)

    Published 2021
    “…<p>Results of searching performance of different algorithm models on the Sphere function and Griewank function.…”
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  11. 91
    A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data

    A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

    Published 2022
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  12. 92
    Modeling with uniform <i>σ</i> values.

    Modeling with uniform <i>σ</i> values. by Giacomo Janson (8138517)

    Published 2019
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  13. 93
    Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function.

    Results of searching performance of different algorithm models on the Rosenbrock function, Ackley function, and Rastrigin function. by Rui Zhang (13940)

    Published 2021
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  14. 94
    Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements

    Python implementation of the Trajectory Adaptive Multilevel Sampling algorithm for rare events and improvements by Pascal Wang (10130612)

    Published 2021
    “…<div>This directory contains Python 3 scripts implementing the Trajectory Adaptive Multilevel Sampling algorithm (TAMS), a variant of Adaptive Multilevel Splitting (AMS), for the study of rare events. …”
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  15. 95
    Algorithm flow of the CNN + LSTM model.

    Algorithm flow of the CNN + LSTM model. by Zhongzhong Guo (12133402)

    Published 2022
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  16. 96
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  17. 97
    The analytic pipeline for EGG analysis and biomarker identification.

    The analytic pipeline for EGG analysis and biomarker identification. by Mahmoud Zeydabadinezhad (12289570)

    Published 2023
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  18. 98
    List of time- and frequency-domain features used for EGG analysis.

    List of time- and frequency-domain features used for EGG analysis. by Mahmoud Zeydabadinezhad (12289570)

    Published 2023
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  19. 99
    This illustration compares the AUC for a Random Forest classifier trained using various feature sets.

    This illustration compares the AUC for a Random Forest classifier trained using various feature sets. by Mahmoud Zeydabadinezhad (12289570)

    Published 2023
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  20. 100
    Fig 1 -

    Fig 1 - by Mahmoud Zeydabadinezhad (12289570)

    Published 2023
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