Showing 341 - 360 results of 4,060 for search 'significant ((((shape decrease) OR (step decrease))) OR (((mean decrease) OR (teer decrease))))', query time: 0.49s Refine Results
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    Generated spline library. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  5. 345

    Correlation coefficient matrix. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  6. 346

    RMSE versus learning rate. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  7. 347

    RMSE versus training parameters. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  8. 348

    Assembly process of machine recognition form. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  9. 349

    Process of steel truss incremental launching. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  10. 350

    CGAN and AutoML stacking device. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  11. 351

    U-wave estimates versus R-matrix noise variance. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  12. 352

    Sliding window process. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  13. 353

    Assembly error angle of a single spline. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  14. 354

    Original record form of error matrix. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  15. 355

    Form for machine recognition. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  16. 356

    RMSE versus architectural parameters. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  17. 357

    Kalman process. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
  18. 358

    Attention mechanism. by Zhe Hu (787283)

    Published 2025
    “…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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