Showing 121 - 140 results of 150 for search '(( binary primate codon optimization algorithm ) OR ( less based complex optimization algorithm ))', query time: 0.59s Refine Results
  1. 121

    RSF Components of the best five individuals. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  2. 122

    Open loop simulation. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  3. 123

    Average wind test fitness. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  4. 124

    Internal process of a policy gradient block. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  5. 125

    Training process of a DDPG individual. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  6. 126

    PbGA search phases to find the best individuals. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  7. 127

    Previous usages of DRL in solving PDG problems. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  8. 128

    Internal process of a critic gradient block. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  9. 129

    Best Individuals from the mapping phase. by Larasmoyo Nugroho (18078260)

    Published 2024
    “…<div><p>The PbGA-DDPG algorithm, which uses a potential-based GA-optimized reward shaping function, is a versatiledeep reinforcement learning/DRLagent that can control a vehicle in a complex environment without prior knowledge. …”
  10. 130

    Schematic diagram of weld surface defects. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  11. 131

    Improved YOLOv7 network structure. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  12. 132

    Renderings of data enhancements. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  13. 133

    Number and size of marked defects. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  14. 134

    Loss function curve. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  15. 135

    Precision-Recall curve. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  16. 136

    Comparison experiment results. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  17. 137

    Ablation experiment results. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  18. 138

    Deepwise separable convolution structure diagram. by Xiangqian Xu (17310895)

    Published 2024
    “…<div><p>The background of pipeline weld surface defect image is complex, and the defect size is small. Aiming at the small defect size in the weld image, which is easy to cause missed detection and false detection, a lightweight target detection algorithm based on improved YOLOv7 is proposed. …”
  19. 139

    DataSheet_2_Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results.docx by Samuel Sainz-Villegas (14137968)

    Published 2022
    “…Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. …”
  20. 140

    DataSheet_3_Predicting non-native seaweeds global distributions: The importance of tuning individual algorithms in ensembles to obtain biologically meaningful results.zip by Samuel Sainz-Villegas (14137968)

    Published 2022
    “…Inspecting the number of predictors to include in final ensembles and the selection of algorithms and its complexity have been demonstrated to be crucial for this purpose. …”