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    Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

    Published 2025-05-01
    “…A 5-fold cross-validation with 999 repetitions was performed on all trained machine learning models. The random seeds are set during resampling, parameter tuning and model training to ensure model reproducibility. …”
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    Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach by T. Savioli, M. Pampanini, G. Visani, L. Esposito, C. A. Rinaldini

    Published 2024-10-01
    “…The present work aims to investigate a novel approach for engine control system calibration, by adopting machine learning techniques to model physical parameters of the engine starting from experimental data measured at the test bench. …”
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    INFLUENCE OF MACHINING TECHNOLOGIES ON VALUES OF RESIDUAL STRESSES OF OXIDE CUTTING CERAMICS by Jakub Němeček, Kamil Kolařík, Jiří Čapek, Nikolaj Ganev

    Published 2017-07-01
    “…Measurements made in the X-ray diffraction laboratory at the Department of solid state engineering were performed for both the phases. The influence of the parameters of machining to residual stresses was studied and the resulting values were compared with each other.…”
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    Towards Compensation for Servo-Control Defects in Coordinate Measuring Machines (CMMs) by Jean-François Manlay, Abdérafi Charki, Anthony Delamarre

    Published 2025-06-01
    “…Coordinate measuring machines (CMMs) are increasingly used in manufacturing, mechanical engineering, and wherever special geometries need to be measured with the utmost precision. …”
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    Science Through Machine Learning: Quantification of Post‐Storm Thermospheric Cooling by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, Douglas P. Drob, W. Kent Tobiska, Jean Yoshii

    Published 2022-09-01
    “…Abstract Machine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. …”
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