Showing 1 - 20 results of 370 for search '(((( elements new algorithm ) OR ( elements present algorithm ))) OR ( neural coding algorithm ))', query time: 0.48s Refine Results
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    The percentage difference between the weight fraction of the elements reported by the algorithm and ground truth when present in the error in the mass attenuation coefficients used as an input. by Dong Hyeok Choi (12562048)

    Published 2025
    “…<p>The percentage difference between the weight fraction of the elements reported by the algorithm and ground truth when present in the error in the mass attenuation coefficients used as an input.…”
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    TIR-Learner v3: New generation TE annotation program for identifying TIRs by Tianyu Lu (18856792)

    Published 2025
    “…The old TIR suffers from slow execution on large genomes due to intense I/O operations and less efficient algorithms, it also lacks maintainability due to legacy dependency issues. …”
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    Multi-material topology optimization using scaled boundary finite element method by Mohammed Saif Zamiruddin Siddiqui (22384884)

    Published 2025
    “…<p>This work introduces the use of Scaled Boundary Finite Element Method (SBFEM) in Multi-Material Topology Optimization (MMTO) problems, which is new in the literature. …”
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    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…<div><p>This letter presents a novel approach to sparse Bayesian underwater acoustic signal direction estimation. …”
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    Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

    Published 2024
    “…<div><p>This letter presents a novel approach to sparse Bayesian underwater acoustic signal direction estimation. …”
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    Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>" by Yize Wang (19535173)

    Published 2024
    “…<p dir="ltr">Codes for "<b>A coherent power-load optimization algorithm for wind-farm-level yaw control considering wake effects via deep neural network</b>"</p>…”
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