Showing 1 - 20 results of 9,963 for search '(((( implementing finding algorithm ) OR ( element data algorithm ))) OR ( data using algorithm ))', query time: 0.49s Refine Results
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    Comparison of different optimization algorithms. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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    Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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    NSGA Ⅲ algorithm. by Yanlin Zhao (251552)

    Published 2024
    “…The findings demonstrate that NSGA III significantly outperforms traditional algorithms, yielding superior solutions for MCMC layout problems. …”
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    The run time for each algorithm in seconds. by Edward Antonian (21453161)

    Published 2025
    “…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”
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    Algorithm comparison. by Manxian Yang (20521600)

    Published 2025
    “…In the initial stage, unmanned aerial vehicles (UAVs) are employed to collect data from the field, which is then used to construct accurate farm models. …”
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    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
    “…This article extracted features related to risk assessment, such as weather factors, airport facility inspections, and security check results, and conducted qualitative and quantitative analysis on these features to generate a datable risk warning weight table. This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. …”
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