Learning deep representation of imbalanced SCADA data for fault detection of wind turbines
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
An Open-Set Modulation Recognition Scheme With Deep Representation Learning
Sign Up to like & getrecommendations! 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
Switchable Normalization for Learning-to-Normalize Deep Representation
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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