Showing 2,181 - 2,200 results of 9,520 for search 'significantly ((((((less decrease) OR (largest decrease))) OR (mean decrease))) OR (we decrease))', query time: 0.61s Refine Results
  1. 2181

    Analysis of subgroups. by Da Huang (1306407)

    Published 2025
    “…Normally presenting with symptoms such as dyspnea, decreased exercise tolerance, decreased maximal heart rate, and decreased arterial oxygen saturation. …”
  2. 2182
  3. 2183

    Battery parameters. by Peijian Jin (22265437)

    Published 2025
    “…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  4. 2184

    The aging parameters of each group of batteries. by Peijian Jin (22265437)

    Published 2025
    “…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  5. 2185

    Minimal data set. by Peijian Jin (22265437)

    Published 2025
    “…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  6. 2186

    Experimental lithium-ion batteries. by Peijian Jin (22265437)

    Published 2025
    “…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  7. 2187

    Schematic diagram of two time intervals. by Peijian Jin (22265437)

    Published 2025
    “…The acoustic emission waveforms exhibited a dual-peak characteristic throughout the entire charging and discharging cycles. We observed a pattern in which the time intervals between the waveforms decreased rapidly at first and then stabilized. …”
  8. 2188

    Supplementary file 1_Identifying early blood glucose trajectories in sepsis linked to distinct long-term outcomes: a K-means clustering study with external validation.docx by Huan Ma (713125)

    Published 2025
    “…</p>Results<p>Among 3,655 sepsis patients from the FAH-SYSU dataset, we identified 5 distinct clusters of BG trajectories, which were significantly associated with 1-year mortality risk. …”
  9. 2189

    The overall framework of CARAFE. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  10. 2190

    KPD-YOLOv7-GD network structure diagram. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  11. 2191

    Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  12. 2192

    Comparison of different pruning rates. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  13. 2193

    Comparison of experimental results at ablation. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  14. 2194

    Result of comparison of different lightweight. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  15. 2195

    DyHead Structure. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  16. 2196

    The parameters of the training phase. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  17. 2197

    Structure of GSConv network. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  18. 2198

    Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  19. 2199

    Improved model distillation structure. by Zhongjian Xie (4633099)

    Published 2025
    “…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”
  20. 2200