Articles with "deep representation" as a keyword



Learning deep representation of imbalanced SCADA data for fault detection of wind turbines

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Published in 2019 at "Measurement"

DOI: 10.1016/j.measurement.2019.03.029

Abstract: Abstract Numerous intelligent fault diagnosis models have been developed on supervisory control and data acquisition (SCADA) systems of wind turbines, so as to process massive SCADA data effectively and accurately. However, there is a problem… read more here.

Keywords: wind turbines; learning deep; deep representation; class ... See more keywords

Semi-random subspace with Bi-GRU: Fusing statistical and deep representation features for bearing fault diagnosis

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Published in 2020 at "Measurement"

DOI: 10.1016/j.measurement.2020.108603

Abstract: Abstract Statistical features and deep representation features have been widely used in bearing fault diagnosis. These two kinds of features have their superiorities, however, few studies have explored combining them and considering their heterogeneousness. Therefore,… read more here.

Keywords: random subspace; bearing fault; semi random; representation features ... See more keywords

Wave2Vec: Deep representation learning for clinical temporal data

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Published in 2019 at "Neurocomputing"

DOI: 10.1016/j.neucom.2018.03.074

Abstract: Abstract Representation learning for time series has gained increasing attention in healthcare domain. The recent advancement in semantic learning allows researcher to learn meaningful deep representations of clinical medical concepts from Electronic Health Records (EHRs).… read more here.

Keywords: representation learning; wave2vec deep; time; model ... See more keywords

Deep Representation Clustering of Multitype Damage Features Based on Unsupervised Generative Adversarial Network

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Published in 2024 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2024.3418413

Abstract: Damage identification based on deep learning has become a hot topic recently. Damage identification and classification methods based on neural networks are much concerned, and therefore, reducing manual participation in labeling data as much as… read more here.

Keywords: damage; generative adversarial; adversarial network; network ... See more keywords

An Open-Set Modulation Recognition Scheme With Deep Representation Learning

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Published in 2023 at "IEEE Communications Letters"

DOI: 10.1109/lcomm.2023.3241388

Abstract: This letter proposes a deep representation learning based automatic modulation recognition (AMR) algorithm in the open-set recognition (OSR) regime. The challenging recognition risk of unknown modulation classes is first analyzed for most state-of-the-art approaches, and… read more here.

Keywords: recognition; modulation; deep representation; scheme ... See more keywords
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Switchable Normalization for Learning-to-Normalize Deep Representation

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Published in 2021 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2019.2932062

Abstract: We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and… read more here.

Keywords: deep representation; switchable normalization; learning normalize; normalization learning ... See more keywords

Identify Bitter Peptides by Using Deep Representation Learning Features

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Published in 2022 at "International Journal of Molecular Sciences"

DOI: 10.3390/ijms23147877

Abstract: A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need… read more here.

Keywords: bitter peptides; representation learning; peptides using; deep representation ... See more keywords