K-SVD dictionary learning process.

<div><p>With increasing traffic loads and extended bridge service life, fatigue damage in steel bridge decks has become a significant concern. Traditional detection methods often lack the accuracy and responsiveness needed for practical engineering applications. To address the non-statio...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Li Jiaqing (21212614) (author)
مؤلفون آخرون:	Song Fei (16087964) (author), Xiao Zidong (21212617) (author), Zhu Longji (21212620) (author), Chen Lan (6467111) (author), Wei Zheliang (21212623) (author)
منشور في:	2025
الموضوعات:	Biophysics Biochemistry Molecular Biology Sociology Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified though computationally intensive supporting future applications singular value decomposition remove background noise increasing traffic loads convolutional neural networks various damage states steel bridge decks accurate crack detection svd algorithm functions svd &# 8217 practical engineering applications processed ae data dictionary learning algorithm fatigue crack detection fatigue damage crack initiation crack identification bridge inspections practical settings practical guidance fatigue micro xlink "> theoretical foundation test sets study combines stationary nature significant concern scalable solution results provide responsiveness needed optimization strategies damaged areas combination viable adaptive filter acoustic emission
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K-SVD dictionary learning process.

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