Showing 861 - 880 results of 2,900 for search '(feature OR features) parameters computational', query time: 0.25s Refine Results
  1. 861

    Full-process aerosol jet printing modelling: achieving high-fidelity simulation via coupling jetting and deposition by Yufeng Jin, Hao Yi, Huajun Cao, Xianshan Dong

    Published 2025-12-01
    “…Various factors – including printing parameters and ink properties – affect the printing features. …”
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  2. 862
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  4. 864

    Dispersive surface-response formalism to address nonlocality in extreme plasmonic field confinement by Babaze Antton, Neuman Tomáš, Esteban Ruben, Aizpurua Javier, Borisov Andrei G.

    Published 2023-06-01
    “…An explicit comparison with time-dependent density functional theory (TDDFT) results shows that the dispersive SRF correctly describes the plasmonic response of planar and nonplanar systems featuring extreme field confinement. This work thus significantly extends the applicability range of the SRF, contributing to the development of computationally efficient semiclassical descriptions of light–matter interaction that capture quantum effects.…”
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  5. 865

    Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

    Published 2025-07-01
    “…The most important and related predictors selected by the Boruta feature selection method were used to develop ML prediction models. …”
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  6. 866
  7. 867

    Lightweight deep learning method for end-to-end point cloud registration by Linjun Jiang, Yue Liu, Zhiyuan Dong, Yinghao Li, Yusong Lin

    Published 2025-02-01
    “…In this study, we propose a lightweight deep learning-based registration method that captures features from multiple perspectives to predict overlapping points and mitigate the interference of non-overlapping points. …”
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    A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive Fitting by Honghui Wan, Feiyu Zhao

    Published 2024-08-01
    “…This network effectively extracts features, segments point clouds, and accurately identifies and computes parameters of regular geometric primitives with notable resilience to noise. …”
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    Article
  10. 870

    M-RSF: a multilevel feedback queue task scheduling mechanism for Unikernel by DONG Bonan, YANG Qiusong, LI Mingshu

    Published 2024-05-01
    “…Unikernel, as a cutting-edge technology in the field of cloud computing, is characterized by its fast start-up speed and minimal resource usage. …”
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  11. 871

    Comparative morpho-topometric characteristics of human T<sub>I</sub> and T<sub>VI</sub> vertebrae in the first period of adulthood and old age by A. A. Balandin, O. A. Chudinov, I. A. Balandina

    Published 2025-05-01
    “…To carry out various diagnostic and therapeutic manipulations in the thoracic spine, a specialist doctor needs a solid body of knowledge about the morphotopometric features of the vertebrae associated with the age and gender of the subject. …”
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  16. 876

    An Image and State Information-Based PINN with Attention Mechanisms for the Rapid Prediction of Aircraft Aerodynamic Characteristics by Yiduo Kan, Xiangdong Liu, Haikuo Liu

    Published 2025-05-01
    “…Prediction of aircraft aerodynamic parameters is crucial for aircraft design, yet traditional computational fluid dynamics methods remain time-consuming and labor-intensive. …”
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    Article
  17. 877

    A lightweight power quality disturbance recognition model based on CNN and Transformer by ZHANG Bide, QIU Jie, LOU Guangxin, ZHOU Can, LUO Qingqing, LI Tianqian

    Published 2025-01-01
    “…Depthwise separable convolutions are first employed to extract local features from the disturbance signals. An efficient softthreshold block is then introduced to reduce noise and redundant features without significantly increasing the model′s parameters or complexity. …”
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    Tiny-MobileNet-SE: A Hybrid Lightweight CNN Architecture for Resource-Constrained IoT Devices by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu

    Published 2025-01-01
    “…This architecture integrates Squeeze-and-Excitation (SE) blocks for adaptive feature recalibration, Batch Normalization (BN) for accelerated convergence, and applies knowledge distillation techniques from MobileNetV2 for enhanced feature generalization. …”
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  20. 880