Time Series Data Augmentation Methods for Deep Learning Models in Conveyor Belt Load Classification-Reference-Cited by-同舟云学术

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Time Series Data Augmentation Methods for Deep Learning Models in Conveyor Belt Load Classification

Published:2024-04-25 Issue: Volume:abs/1906.02691 Page:1-6
ISSN:
Container-title:2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream)
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Author:

Žvirblis Tadas¹,Pikšrys Armantas²,Bzinkowski Damian³,Rucki Mirosław³,Kilikevičius Artūras⁴

Affiliation:

1. Institute of Data Science and Digital Technologies Vilnius University,Vilnius,Lithuania

2. Institute of Computer Science Vilnius University,Vilnius,Lithuania

3. Kazimierz Pulaski University of Technology and Humanities in Radom,Radom,Poland

4. Institute of Mechanical science Vilnius Gediminas Technical University,Vilnius,Lithuania

Funder

Research Council of Lithuania (LMTLT)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10542546/10542531/10542589.pdf?arnumber=10542589

Reference12 articles.

1. Investigation of deep learning models on identification of minimum signal length for precise classification of conveyor rubber belt loads

2. Data augmentation of wearable sensor data for parkinson’s disease monitoring using convolutional neural networks

3. Time series data augmentation for neural networks by time warping with a discriminative teacher;Iwana,2021

4. An introduction to variational autoencoders;Kingma;CoRR,2019

5. Using variational autoencoder to augment sparse time series datasets;Goubeaud,2021

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