Vehicle parameters. by Honglei Pang (22693724)

Subjects:

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)

Subjects:

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

Subjects:

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)

Subjects:

Speed limits and gradients from RJ to WYJ. by Yunhu Huang (21402795)

“…On the basis of EITO_E, we propose EITO_P algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …”

Speed limits and gradient from SJZ to XHM. by Yunhu Huang (21402795)

“…On the basis of EITO_E, we propose EITO_P algorithm using the PPO algorithm to optimize multiple objectives by designing reinforcement learning strategies, rewards, and value functions. …”

Dice score for various <i>α</i> values (from the Eq 8) for the tracking of the CST and for the iFOD and PTT algorithms. by Thomas Durantel (20933812)

Parameter settings for metaheuristic algorithms. by Junhao Wei (6816803)

“…<div><p>Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. …”

PFC value of algorithms on two datasets. by Xing Chen (140292)

“…<div><p>In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. …”

Results from the performance-based assessments of hand function for PALS and the age/sex matched healthy subgroup. by Conor D. Hayden (20855587)

Loss function and accuracy curve comparison. by Dayu Zhang (710629)

If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

“…Both versions are evaluated on the same synthetic datasets with a 3-group ground truth. Each algorithm return a set of coarsened <i>variables</i> (a grouping of species into three groups) and a <i>model</i> that uses these variables to predict the function. …”

The algorithm is designed to identify patterns in the treatment dataset. by Belén Otero-Carrasco (21262360)

“…<p>This process involves two key functions. First, from lines 1 to 8, the algorithm identifies amino acids that meet the occurrence threshold, starting with patterns of length 1. …”

Standard benchmark functions [42]. by Junhao Wei (6816803)

“…<div><p>Whale Optimization Algorithm (WOA) suffers from issues such as premature convergence, low population diversity in the later stages of iteration, slow convergence rate, low convergence accuracy, and an imbalance between exploration and exploitation. …”

Statistical results of various algorithms. by ZeSheng Lin (20501356)

“…Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. …”

F4 function and convergence curve graph. by Jiangli Yu (4601986)

F5 function and convergence curve graph. by Jiangli Yu (4601986)

F1 function and convergence curve graph. by Jiangli Yu (4601986)

F6 function and convergence curve graph. by Jiangli Yu (4601986)

F7 function and convergence curve graph. by Jiangli Yu (4601986)