Search alternatives:
source » sources (Expand Search)
resource » resources (Expand Search)
resourcess » resourcesss (Expand Search)
selection » detection (Expand Search)
Showing 921 - 940 results of 2,814 for search '((\ source selection functions\ ) OR (( resource OR resourcess) selection function\ ))', query time: 0.35s Refine Results
  1. 921
  2. 922
  3. 923
  4. 924
  5. 925

    Sequential Inversion for Helicopter Time-Domain Electromagnetics Based on a Regularized Extended Kalman Filtering by Sirui Zhou, Jun Lin, Chuandong Jiang, Haigen Zhou, Yanzhang Wang

    Published 2025-01-01
    “…The helicopter time-domain electromagnetic method (HTEM) has been widely applied in challenging geological terrains, particularly in large-scale mineral exploration, underground water resource detection, and the selection of sites for underground engineering, due to its advantage of not requiring personnel to enter the detection area. …”
    Get full text
    Article
  6. 926
  7. 927
  8. 928

    TOPSIS method-based decision-making model for bipolar quadripartitioned neutrosophic environment by G. Muhiuddin, Mohamed E. Elnair, Satham Hussain S, Durga Nagarajan

    Published 2025-06-01
    “…In the domain of renewable energy, selecting the most suitable energy source involves navigating complex decision-making processes influenced by multiple criteria and inherent uncertainties. …”
    Get full text
    Article
  9. 929
  10. 930
  11. 931
  12. 932
  13. 933
  14. 934
  15. 935
  16. 936

    Estimation of Above-Ground Biomass for <italic>Dendrocalamus Giganteus</italic> Utilizing Spaceborne LiDAR GEDI Data by Huanfen Yang, Zhen Qin, Qingtai Shu, Li Xu, Jinge Yu, Shaolong Luo, Zaikun Wu, Cuifen Xia, Zhengdao Yang

    Published 2025-01-01
    “…The outcomes reveal that 1) the results showed that the power function emerged as the most efficacious model, with coefficient of determination (<italic>R</italic><sup>2</sup>) &#x003D; 0.87 and root mean square error (RMSE) &#x003D; 0.00051 Mg, in estimating the AGB of <italic>Dendrocalamus giganteus</italic>. 2) Based on the feature importance ranking of Random Forest, five variables were selected from the 40 extracted from GEDI, achieving RMSE &#x003D; 8.21 Mg&#x002F;ha and mean absolute error (MAE) &#x003D; 6.12 Mg&#x002F;ha. …”
    Get full text
    Article
  17. 937
  18. 938
  19. 939
  20. 940