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Showing 81 - 100 results of 397 for search '(( final while model optimization algorithm ) OR ( binary step model optimization algorithm ))', query time: 0.53s Refine Results
  1. 81
    PoD of cracks by ultrasonic method.

    PoD of cracks by ultrasonic method. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  2. 82
    Time course curve of SIFs.

    Time course curve of SIFs. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  3. 83
    Configuration of the CRH3 train used in China.

    Configuration of the CRH3 train used in China. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  4. 84
    Schematic diagrams of M-shaped wave.

    Schematic diagrams of M-shaped wave. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  5. 85
    Calculated train loads.

    Calculated train loads. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  6. 86
    Crack size with three maintenance strategies.

    Crack size with three maintenance strategies. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  7. 87
    Crack expansion trajectory.

    Crack expansion trajectory. by Dapeng Wang (250459)

    Published 2023
    “…The impact of detection probability and maintenance measures on the service life of tunnel lining and the cost of detection and maintenance of cracked lining in the whole life cycle was analyzed; the optimization calculation model of tunnel lining crack detection and maintenance strategy based on genetic algorithm was established with the multi-objective optimization function of maximizing the service life of detection and maintenance and minimizing the total cost of detection and maintenance of fatigue cracks. …”
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  8. 88
    Integration of multiple models into RfaH sequence design leads to reduction of sequence bias and variance.

    Integration of multiple models into RfaH sequence design leads to reduction of sequence bias and variance. by Lu Hong (399882)

    Published 2024
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  9. 89
    Image 1_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif

    Image 1_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif by Tingkai Hou (21014762)

    Published 2025
    “…Secondly, the DE-GWO, particle swarm optimization (PSO), genetic algorithm (GA), and SVR are integrated to identify the optimal superparameters, while the nonlinear mapping relationship between inversion parameters and displacements is established. …”
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  10. 90
    Image 2_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif

    Image 2_A novel inversion method of slope rock mechanical parameters using differential evolution gray wolf algorithm to optimize support vector regression.tif by Tingkai Hou (21014762)

    Published 2025
    “…Secondly, the DE-GWO, particle swarm optimization (PSO), genetic algorithm (GA), and SVR are integrated to identify the optimal superparameters, while the nonlinear mapping relationship between inversion parameters and displacements is established. …”
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  11. 91
    Data_Sheet_1_Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.pdf

    Data_Sheet_1_Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms.pdf by Alejandro O. Blenkmann (13914651)

    Published 2022
    “…In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.…”
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  12. 92
    S1 File -

    S1 File - by Nhat H.M. Truong (17921685)

    Published 2024
    “…The results showed that the model’s predictive ability to produce a protein per mRNA reached R = 0.6660 when using six features, while the correlation of this model’s final translation rate to protein level was up to R = 0.6729. …”
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  13. 93
    Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model

    Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

    Published 2019
    “…A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …”
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  14. 94
    Loss ratio of models.

    Loss ratio of models. by Xianlin Ren (22783589)

    Published 2025
    “…Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic mutation-Sine Cosine Algorithm is utilized to optimize the hyperparameters of the stacked denoising auto-encoders network and construct a fault diagnosis model. …”
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  15. 95
    Model diagnosis results.

    Model diagnosis results. by Xianlin Ren (22783589)

    Published 2025
    “…Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic mutation-Sine Cosine Algorithm is utilized to optimize the hyperparameters of the stacked denoising auto-encoders network and construct a fault diagnosis model. …”
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  16. 96
    Surround Suppression mechanism.

    Surround Suppression mechanism. by Elnaz Nemati (21402730)

    Published 2025
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  17. 97
    The classification performance of all models.

    The classification performance of all models. by XiuZhi Qi (18555018)

    Published 2024
    “…Finally, comparative experiments were conducted between the optimized SCB-CNN, the unoptimized model, VGG-Net, and GoogleNet. …”
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  18. 98
    Configuration of the GoogleNet model.

    Configuration of the GoogleNet model. by XiuZhi Qi (18555018)

    Published 2024
    “…Finally, comparative experiments were conducted between the optimized SCB-CNN, the unoptimized model, VGG-Net, and GoogleNet. …”
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  19. 99
    Image 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf

    Image 2_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

    Published 2025
    “…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”
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  20. 100
    Table 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx

    Table 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

    Published 2025
    “…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”
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