Showing 18,861 - 18,880 results of 125,061 for search '(( 5 ((026 decrease) OR (a decrease)) ) OR ( a ((point decrease) OR (mean decrease)) ))', query time: 1.08s Refine Results
  1. 18861

    YOLOv8n-BWG model structure diagram. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  2. 18862

    BiFormer structure diagram. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  3. 18863

    YOLOv8n-BWG detection results diagram. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  4. 18864

    GSConv module structure diagram. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  5. 18865

    Performance comparison of three loss functions. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  6. 18866

    Network loss function change diagram. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  7. 18867

    Comparative diagrams of different indicators. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  8. 18868

    YOLOv8n structure diagram. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  9. 18869

    Geometric model of the binocular system. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  10. 18870

    Enhanced dataset sample images. by Yaojun Zhang (389482)

    Published 2025
    “…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”
  11. 18871
  12. 18872

    A New Highly Sensitive Method to Assess Respiration Rates and Kinetics of Natural Planktonic Communities by Use of the Switchable Trace Oxygen Sensor and Reduced Oxygen Concentrati... by Laura Tiano (524349)

    Published 2014
    “…This method provides continuous real time measurements, allowing for a number of diverse possibilities, such as modeling the rate of oxygen decrease to obtain kinetic parameters. …”
  13. 18873
  14. 18874
  15. 18875
  16. 18876

    CDK2/Speedy A localization in <i>Cdk2</i><sup><i>−/−</i></sup> and <i>Speedy A</i><sup><i>−/−</i></sup> spermatocytes. by Nathan Palmer (264897)

    Published 2020
    “…Highlighted by dashed lines are intervals in which telomeres were found to have a 50%–74% decrease in signal and a 75%–100% decrease in signal. …”
  17. 18877

    Tunable Electrical Properties of Embossed, Cellulose-Based Paper for Skin-like Sensing by Tongfen Liang (9601461)

    Published 2020
    “…The largest increase in conductivity from 8.38 × 10<sup>–6</sup> to 2.5 × 10<sup>–3</sup> S/m by a factor of ∼300 occurred at a percolation threshold of 3.8 wt % (or 0.36 vol %) with the composite paper plastically compressed by 410 MPa, which caused a decrease of porosity from 88% to 42% on average. …”
  18. 18878

    Tunable Electrical Properties of Embossed, Cellulose-Based Paper for Skin-like Sensing by Tongfen Liang (9601461)

    Published 2020
    “…The largest increase in conductivity from 8.38 × 10<sup>–6</sup> to 2.5 × 10<sup>–3</sup> S/m by a factor of ∼300 occurred at a percolation threshold of 3.8 wt % (or 0.36 vol %) with the composite paper plastically compressed by 410 MPa, which caused a decrease of porosity from 88% to 42% on average. …”
  19. 18879
  20. 18880