Showing 221 - 240 results of 15,618 for search 'computing optimizing 4', query time: 0.26s Refine Results
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    Optimization of Dual-energy Spectral Lower-extremity Computed Tomography Venography Scanning Protocol: Phantom Study by Shigeng WANG, Renwang PU, Yijun LIU, Xin FANG, Wei WEI, Beibei LI

    Published 2025-01-01
    “…Objective: To optimize a scanning protocol for dual-energy spectral lower-extremity computed tomography venography (CTV) based on a phantom study. …”
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    Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision by Jui-Sheng Chou, Chi-Yun Liu, Pin-Jun Guo

    Published 2024-12-01
    “…To address these challenges, this study introduces a novel system integrating advanced computer vision-based deep learning, metaheuristic optimization, and Unmanned Aerial Vehicle (UAV) technology to revolutionize bridge inspections. …”
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    Multi-scale computational fluid dynamics and machine learning integration for hydrodynamic optimization of floating photovoltaic systems by Fadhil Khadoum Alhousni, Samuel Chukwujindu Nwokolo, Edson L. Meyer, Theyab R. Alsenani, Humaid Abdullah Alhinai, Chinedu Christian Ahia, Paul C. Okonkwo, Yaareb Elias Ahmed

    Published 2025-08-01
    “…The review is organized according to five main goals: (i) to publish experimental and empirical results in FPV literature; (ii) to develop a unified computational approach that combines CFD and ML; (iii) to assess system improvements through multi-scale hydrodynamic modelling and AI-driven adjustments; (iv) to introduce the Bidirectional Conceptual Feedback Loop (BCFL) as a dynamic optimization model; and (v) to develop a scalable, climate-resilient FPV model for the global energy transition. …”
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    Enhanced Prediction and Evaluation of Hydraulic Concrete Compressive Strength Using Multiple Soft Computing and Metaheuristic Optimization Algorithms by Tianyu Li, Xiamin Hu, Tao Li, Jie Liao, Lidan Mei, Huiwen Tian, Jinlong Gu

    Published 2024-10-01
    “…The experimental results demonstrate that the hybrid ensemble learning and heuristic optimization algorithm achieve a regression coefficient of 0.9329, a mean absolute error (MAE) of 2.7695, and a mean square error (MSE) of 4.0891. …”
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