Showing 1 - 20 results of 6,885 for search '(((( element method algorithm ) OR ( effect modeling algorithm ))) OR ( neural coding algorithm ))', query time: 0.38s Refine Results
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    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”
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    Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

    Published 2024
    “…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”
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    Convergence curve of the DBO algorithm. by Ma Haohao (22177538)

    Published 2025
    “…The experimental results show that the root mean square error (RMSE) accuracy of the established roughness prediction model reaches 0.0001 µm, the proposed force control method is more stable, and the surface roughness is reduced by 20.79% on average compared to the baseline method, which proves the effectiveness of the framework in achieving high precision and high efficiency of robot polishing.…”
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    The run time for each algorithm in seconds. by Edward Antonian (21453161)

    Published 2025
    “…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …”
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    Overview of the C&CG method. by Tianhe Sun (21145341)

    Published 2025
    “…Furthermore, incorporating energy storage planning achieves additional significant reductions of 27% and 1.1% in these metrics, verifying its effectiveness. Comparative analysis confirms the superiority of the proposed robust-probabilistic hybrid model over traditional uncertainty quantification methods in balancing computational efficiency, risk control, and curtailment reduction.…”
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    GA pseudo-code. by Jianpeng Zhang (528185)

    Published 2025
    Subjects:
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