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Showing 61 - 80 results of 10,310 for search '(( algorithm python function ) OR ((( algorithm etc function ) OR ( algorithm models function ))))*', query time: 0.61s Refine Results
  1. 61
    The HIL simulation data flowchart.

    The HIL simulation data flowchart. by Honglei Pang (22693724)

    Published 2025
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  2. 62
    Braking performance and lateral stability on curved road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    Braking performance and lateral stability on curved road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sidesli... by Honglei Pang (22693724)

    Published 2025
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  3. 63
    Braking performance and lateral stability on split-μ road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate.

    Braking performance and lateral stability on split-μ road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sidesl... by Honglei Pang (22693724)

    Published 2025
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  4. 64
    Simulation Results Plot of the EMB System Response: (a) Step response curve of the EMB system; (b) Sine response curve of the EMB system; (c) Steady state error curve of the EMB system.

    Simulation Results Plot of the EMB System Response: (a) Step response curve of the EMB system; (b) Sine response curve of the EMB system; (c) Steady state error curve of the EMB sy... by Honglei Pang (22693724)

    Published 2025
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  5. 65
    Hyperparameter Configurations in PPO Training.

    Hyperparameter Configurations in PPO Training. by Honglei Pang (22693724)

    Published 2025
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  6. 66
    Main parameters of steering system.

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

    Published 2025
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  7. 67
    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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  8. 68
    Co-simulation architecture.

    Co-simulation architecture. by Honglei Pang (22693724)

    Published 2025
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  9. 69
    Overall framework diagram of the study.

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

    Published 2025
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  10. 70
    Vehicle parameters.

    Vehicle parameters. by Honglei Pang (22693724)

    Published 2025
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  11. 71
    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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  12. 72
    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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  13. 73
    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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  14. 74
    Search Algorithms and Loss Functions for Bayesian Clustering

    Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

    Published 2022
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  15. 75
    Genetic Algorithm (GA) and CAGE-based personalization block diagrams.

    Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

    Published 2020
    “…<p>(<b>A</b>) Genetic algorithm schematic diagram. Initially a set of organisms is generated, each of which is determined by a random vector of scaling factors for optimized model parameters (step 1). …”
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  16. 76
    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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  17. 77
    Modeling with uniform <i>σ</i> values.

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

    Published 2019
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  18. 78
    Algorithm flow of the CNN + LSTM model.

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

    Published 2022
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  19. 79
    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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  20. 80
    EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit

    EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

    Published 2025
    “…In this paper, a new RDKit/Python implementation of the algorithm is described, that is both accurate and complete. …”
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