Showing 1 - 17 results of 17 for search '(((( data using algorithm ) OR ( data fitting algorithm ))) OR ( element method algorithm ))~', query time: 0.38s Refine Results
  1. 1

    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  2. 2

    Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  3. 3

    Coprime array with interpolated array elements. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  4. 4

    Mean and root mean square errors of DOA estimate. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  5. 5

    Schematic diagram of maritime array arming. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  6. 6

    Variation of RMSE with the number of snapshots. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  7. 7

    Velocity of sound profile. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  8. 8

    Variation of RMSE with input SNR. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  9. 9

    Coprime array. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  10. 10

    Variation of RMSE with different grid spacing. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  11. 11

    Spatial power spectrum of compact sound source. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
  12. 12
  13. 13

    Table 1_In Vitro biomechanical study of meniscal properties in patients with severe knee osteoarthritis.xlsx by Yuqi Liu (501183)

    Published 2025
    “…Quantifying the biomechanical properties of the meniscus is essential for understanding its role in knee joint function and pathology.</p>Methods<p>This study aimed to determine the biomechanical properties of the meniscus in patients with severe KOA using experimental mechanical testing and an inverse finite element analysis (iFEA) model. …”
  14. 14

    Table 2_In Vitro biomechanical study of meniscal properties in patients with severe knee osteoarthritis.xlsx by Yuqi Liu (501183)

    Published 2025
    “…Quantifying the biomechanical properties of the meniscus is essential for understanding its role in knee joint function and pathology.</p>Methods<p>This study aimed to determine the biomechanical properties of the meniscus in patients with severe KOA using experimental mechanical testing and an inverse finite element analysis (iFEA) model. …”
  15. 15

    DataSheet1_Enhancing slope stability prediction through integrated PCA-SSA-SVM modeling: a case study of LongLian expressway.docx by Jianxin Huang (11944014)

    Published 2024
    “…Traditional slope stability analysis methods, such as the limit equilibrium method, limit analysis method, and finite element method, often face limitations due to computational complexity and the need for extensive soil property data. …”
  16. 16

    An optional formula for calculating crustal thickness using Sr/Y ratio and its application to the southeastern margin of the Central Asian Orogenic Belt by Xu Ma (45624)

    Published 2025
    “…Global lower crust-derived magmatic rock (younger than 23 Ma) data from diverse geological settings were selected and filtered using key geochemical criteria, alternating and iteratively using K-means clustering algorithm and improved Thompson tau method to exclude outliers. …”
  17. 17

    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

    Published 2025
    “…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”