Search alternatives:
property optimization » process optimization (Expand Search), policy optimization (Expand Search), robust optimization (Expand Search)
well optimization » wolf optimization (Expand Search), whale optimization (Expand Search), field optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
lines based » lens based (Expand Search), genes based (Expand Search), lines used (Expand Search)
based well » based cell (Expand Search), based web (Expand Search), based all (Expand Search)
Showing 1 - 20 results of 59 for search '(( binary based property optimization algorithm ) OR ( lines based well optimization algorithm ))', query time: 0.43s Refine Results
  1. 1
    Datasets and their properties.

    Datasets and their properties. by Olaide N. Oyelade (14047002)

    Published 2023
    Save to List
    Saved in:
  2. 2
    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results

    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  3. 3
    Complex traffic line and track point diagram.

    Complex traffic line and track point diagram. by Mingkang Sun (6498791)

    Published 2024
    Save to List
    Saved in:
  4. 4
    Complex traffic line and track point diagram.

    Complex traffic line and track point diagram. by Mingkang Sun (6498791)

    Published 2024
    Save to List
    Saved in:
  5. 5
    Image_1_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_1_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
    Save to List
    Saved in:
  6. 6
    Image_5_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_5_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
    Save to List
    Saved in:
  7. 7
    Image_2_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_2_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
    Save to List
    Saved in:
  8. 8
    Image_3_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_3_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
    Save to List
    Saved in:
  9. 9
    Image_4_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_4_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
    Save to List
    Saved in:
  10. 10
    PSO algorithms flowchart.

    PSO algorithms flowchart. by Mohammed F. Elnaggar (18297954)

    Published 2024
    Save to List
    Saved in:
  11. 11
    Table1_Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.DOCX

    Table1_Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.DOCX by Åsmund Flobak (789984)

    Published 2023
    Save to List
    Saved in:
  12. 12
    The <i>D</i><sub><i>98</i></sub> in the CTV and the for the nominal scenario optimized using the SC algorithm in homogeneous and heterogeneous phantoms.

    The <i>D</i><sub><i>98</i></sub> in the CTV and the for the nominal scenario optimized using the SC algorithm in homogeneous and heterogeneous phantoms. by Masashi Yagi (3186909)

    Published 2023
    Save to List
    Saved in:
  13. 13
    Supplementary file 1_Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field.pdf

    Supplementary file 1_Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field.pdf by Shuangshuang Zhang (701020)

    Published 2025
    “…Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. After comparison and verification, it is proved to be accurate and reliable. …”
    Save to List
    Saved in:
  14. 14
    If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions.

    If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

    Published 2024
    Save to List
    Saved in:
  15. 15
    Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades.

    Network propagation identifies ageing-associated genes as well as genes distinguishing prostate tumors of different grades. by Konstantina Charmpi (9901053)

    Published 2021
    Save to List
    Saved in:
  16. 16
    Simulation of dense parameter pairs and optimal model output.

    Simulation of dense parameter pairs and optimal model output. by Yen-Ju Chen (354876)

    Published 2025
    Save to List
    Saved in:
  17. 17
    mAP@0.5 line charts for different models.

    mAP@0.5 line charts for different models. by Meiling Shi (13148944)

    Published 2024
    Save to List
    Saved in:
  18. 18
    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF

    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF by Maya Khatun (7437011)

    Published 2019
    “…This method is implemented in PyAR (https://github.com/anooplab/pyar) program. The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. …”
    Save to List
    Saved in:
  19. 19
    Error function description and test optimization results.

    Error function description and test optimization results. by Timothy Rumbell (7419269)

    Published 2019
    Save to List
    Saved in:
  20. 20
    Optimal control and policy review for realistic simulated epidemics.

    Optimal control and policy review for realistic simulated epidemics. by Sandor Beregi (22177739)

    Published 2025
    Save to List
    Saved in:

Search Tools: