يعرض 1 - 20 نتائج من 3,429 نتيجة بحث عن 'data ((correction algorithm) OR (selection algorithm))', وقت الاستعلام: 0.37s تنقيح النتائج
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    Example of motion correction comparison. حسب Yukako Yamane (9034133)

    منشور في 2025
    الموضوعات:
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    Overview of the Cell2Spatial algorithm. حسب Huamei Li (8815955)

    منشور في 2025
    الموضوعات:
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    Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. حسب Yuanchen Zhao (12905580)

    منشور في 2024
    "…(C) We use the synthetic data as input for three families of regression-based algorithms: the EQO of Ref. …"
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    Fitting flow of DAKM algorithm. حسب Xiaobing Chen (572034)

    منشور في 2025
    "…This paper suggests a better algorithm based on feature points method. During the curve approximation process, the projection points of data points and their parameters are calculated, and the data point parameters are corrected to achieve dynamic adjustment of the knot vector. …"
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    GMM-KVS method flowchart. حسب Jingnan Yan (20727331)

    منشور في 2025
    "…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. The proposed approach leverages the principles of the elbow method along with the properties of exponential functions, correction terms, and weight adjustments to accurately determine the optimal k-value. …"
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    Collected demonstration trajectories. حسب Jingnan Yan (20727331)

    منشور في 2025
    "…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. The proposed approach leverages the principles of the elbow method along with the properties of exponential functions, correction terms, and weight adjustments to accurately determine the optimal k-value. …"
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    Schematic diagram of the elbow method. حسب Jingnan Yan (20727331)

    منشور في 2025
    "…To address this challenge, this paper proposes a novel trajectory learning method for robotic arms that combines Gaussian Mixture Model with a k-value selection algorithm. The proposed approach leverages the principles of the elbow method along with the properties of exponential functions, correction terms, and weight adjustments to accurately determine the optimal k-value. …"
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