The performance of S-YOFEO model on MOT17. by Wenshun Sheng (21485393)

“…Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. …”

Five multi-target tracking evaluation indexes. by Wenshun Sheng (21485393)

“…Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. …”

Partial tracking results of MOT17 dataset. by Wenshun Sheng (21485393)

“…Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. …”

Improved detection layer. by Wenshun Sheng (21485393)

“…Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. …”

The performance of S-YOFEO model on MOT16. by Wenshun Sheng (21485393)

“…Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. …”

The matching process of EIOU. by Wenshun Sheng (21485393)

“…Changes due to weather or lighting conditions, as well as the presence of numerous visually similar objects, can lead to target ID switching and tracking loss, thus affecting the system’s reliability. …”

Supporting data for “<b>Development and Clinical Validation of Novel Biomarkers for Early Diagnosis of Advanced Liver Fibrosis</b>” by Pei Wan (9769043)

“…<p dir="ltr">This study encompasses two principal objectives. The primary objective is to generate high-affinity monoclonal antibody pairs with optimal specificity for the establishment of a robust and reproducible immunoassay platform targeting human thrombospondin-2 (TSP2/THBS2) and CDCP1 (CUB domain-containing protein 1). …”

Active Control of Laminar and Turbulent Flows Using Adjoint-Based Machine Learning by Xuemin Liu (20372739)

“…The end-to-end sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the objective function, which we construct using algorithmic differentiation applied to the flow solver. …”

Structural diagram of PPCS. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Comparison between NSGA-II and RPGA. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Parameter value. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Summary of BBSDP-related studies. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Symbol description. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Nominal model solution results. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Machine Learning-Assisted Track Morphology Prediction in μ-LPBF with Spray-Based Powder Spreading-data.docx by min zheng (21496832)

“…SHapley Additive exPlanations (SHAP) are usedto quantify the contribution of each process parameter. A multi-objective optimization framework is then established, targeting surface quality and track uniformity, with the NSGA-II algorithm used to identify Pareto-optimal process parameter sets. …”

CAHA test analysis. by Sixia Fan (18309305)

“…To address high site selection costs from unbalanced fast-slow charging ratios and multi-party cost allocation issues, we propose a four-objective optimization model based on a three-party cost game (suppliers, users, power grid). …”

CAHA test analysis. by Sixia Fan (18309305)

“…To address high site selection costs from unbalanced fast-slow charging ratios and multi-party cost allocation issues, we propose a four-objective optimization model based on a three-party cost game (suppliers, users, power grid). …”

Problem hypothesis. by Sixia Fan (18309305)

“…To address high site selection costs from unbalanced fast-slow charging ratios and multi-party cost allocation issues, we propose a four-objective optimization model based on a three-party cost game (suppliers, users, power grid). …”

May 1st −7st Metro Line 2 OD Statistics Table. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”

Mode choice under rail transit disruption. by Yicheng Liu (2179626)

“…A case study of a bidirectional disruption during the 08:00–10:00 on the section of Xi’an Metro Line 2 demonstrates that: (1) The proposed model exhibits stronger robustness under demand uncertainty, achieving a reduction of 3 dispatched vehicles and a cost saving of 9,439 RMB by moderately increasing passenger costs by 850 RMB and extending bridging time; (2) The RPGA algorithm outperforms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Reinforcement Learning-based NSGA-II (RLNSGA-II), and Multi-objective Particle Swarm Optimization Algorithm (MOPSO) in hypervolume (HV), generational distance (GD), and non-dominated ratio (NDR); (3) Increasing the rated passenger capacity within a certain range can reduce average passenger delays but correspondingly raises transportation costs. …”