يعرض 1 - 18 نتائج من 18 نتيجة بحث عن '(( algorithm rate function ) OR ( ((algorithm python) OR (algorithm within)) function ))~', وقت الاستعلام: 0.25s تنقيح النتائج
  1. 1
  2. 2

    Reward function related parameters. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  3. 3

    Main parameters of braking system. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  4. 4

    EMB and SBW system structure. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  5. 5

    Raw data. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  6. 6

    Code program. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  7. 7

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

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  8. 8

    Steering system model. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  9. 9

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

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  10. 10

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

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  11. 11

    Co-simulation architecture. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  12. 12

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

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  13. 13

    Braking system model. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  14. 14

    Vehicle parameters. حسب Honglei Pang (22693724)

    منشور في 2025
    "…A multi-objective reward function is formulated using stability indicators, including critical tire slip angle, critical sideslip angle, and critical yaw rate thresholds. …"
  15. 15

    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) حسب Erola Fenollosa (20977421)

    منشور في 2025
    "…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…"
  16. 16

    Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making حسب Xiaofei Zhang (16483224)

    منشور في 2025
    "…To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. …"
  17. 17

    Landscape17 حسب Vlad Carare (22092515)

    منشور في 2025
    "…Appropriate post-processing using standard tools of statistical mechanics and unimolecular rate theory enables efficient computation of observable thermodynamic and kinetic properties within well-defined approximations. …"
  18. 18

    An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب Pierre-Alexis DELAROCHE (22092572)

    منشور في 2025
    "…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …"