Výsledky vyhledávání - "Zdunek, Rafał"

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Reduced storage direct tensor ring decomposition for convolutional neural networks compression

Convolutional neural networks (CNNs) are among the most widely used machine learning models for computer vision tasks, such as image classification. To improve the efficiency of CNNs, many CNNs compressing approaches have been developed. Low-rank met

Externí odkaz: http://arxiv.org/abs/2405.10802

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Bearing damage detection with orthogonal and non-negative low-rank feature extraction

Autor: Gabor, Mateusz, Zdunek, Rafal, Wylomanska, Agnieszka, Zimroz, Radoslaw

Publikováno v: IEEE Transactions on Industrial Informatics ( Volume: 20, Issue: 2, February 2024)

Local damage of bearings can be detected as a weak cyclic and impulsive component in a highly noisy measured signal. A key problem is how to extract the signal of interest (SOI) from the raw signal, i.e., how to identify and design an optimal filter.

Externí odkaz: http://arxiv.org/abs/2403.12564

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Non-negative tensor factorization for vibration-based local damage detection

Autor: Gabor, Mateusz, Zdunek, Rafal, Zimroz, Radoslaw, Wodecki, Jacek, Wylomanska, Agnieszka

Publikováno v: Mechanical Systems and Signal Processing Volume 198, 1 September 2023, 110430

In this study, a novel non-negative tensor factorization (NTF)-based method for vibration-based local damage detection in rolling element bearings is proposed. As the diagnostic signal registered from a faulty machine is non-stationary, the time-freq

Externí odkaz: http://arxiv.org/abs/2403.12554

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Non-negative matrix underapproximation as optimal frequency band selector

Autor: Gabor, Mateusz, Zdunek, Rafal, Zimroz, Radoslaw, Wylomanska, Agnieszka

Time-frequency representation (TFR) is often used for non-stationary signal analysis. The most intuitive and interpretable TFR is the spectrogram. Recently, a concept of non-negative matrix factorization (NMF) has been successfully applied to local d

Externí odkaz: http://arxiv.org/abs/2403.12547

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Bi-level algorithm for optimizing hyperparameters in penalized nonnegative matrix factorization

Autor: Del Buono, Nicoletta, Esposito, Flavia, Selicato, Laura, Zdunek, Rafal

Publikováno v: Applied Mathematics and Computation 457 (2023) 128184

Learning approaches rely on hyperparameters that impact the algorithm's performance and affect the knowledge extraction process from data. Recently, Nonnegative Matrix Factorization (NMF) has attracted a growing interest as a learning algorithm. This

Externí odkaz: http://arxiv.org/abs/2203.13129

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