225 related articles for article (PubMed ID: 34282788)
1. A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering.
Sun H; Fu X; Zhong S
Sensors (Basel); 2021 Jul; 21(13):. PubMed ID: 34282788
[TBL] [Abstract][Full Text] [Related]
2. Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM.
Ma Z; Machado JJM; Tavares JMRS
Sensors (Basel); 2021 Nov; 21(22):. PubMed ID: 34833584
[TBL] [Abstract][Full Text] [Related]
3. Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer.
Huang C; Liu C; Wen J; Wu L; Xu Y; Jiang Q; Wang Y
IEEE Trans Cybern; 2024 May; 54(5):3197-3210. PubMed ID: 37015630
[TBL] [Abstract][Full Text] [Related]
4. Weakly Supervised Discriminative Learning With Spectral Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection.
Jiang T; Xie W; Li Y; Lei J; Du Q
IEEE Trans Neural Netw Learn Syst; 2022 Nov; 33(11):6504-6517. PubMed ID: 34057896
[TBL] [Abstract][Full Text] [Related]
5. Fault anomaly detection method of aero-engine rolling bearing based on distillation learning.
Kang Y; Chen G; Wang H; Sheng J; Wei X
ISA Trans; 2024 Feb; 145():387-398. PubMed ID: 38061925
[TBL] [Abstract][Full Text] [Related]
6. An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection.
Chen L; Gao S; Liu B
Sci Rep; 2022 Jan; 12(1):1409. PubMed ID: 35082307
[TBL] [Abstract][Full Text] [Related]
7. RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control.
Zheng D; Jung W; Kim S
Sensors (Basel); 2021 Dec; 21(24):. PubMed ID: 34960441
[TBL] [Abstract][Full Text] [Related]
8. Weakly supervised object detection with 2D and 3D regression neural networks.
Dubost F; Adams H; Yilmaz P; Bortsova G; Tulder GV; Ikram MA; Niessen W; Vernooij MW; Bruijne M
Med Image Anal; 2020 Oct; 65():101767. PubMed ID: 32674042
[TBL] [Abstract][Full Text] [Related]
9. Feature Encoding With Autoencoders for Weakly Supervised Anomaly Detection.
Zhou Y; Song X; Zhang Y; Liu F; Zhu C; Liu L
IEEE Trans Neural Netw Learn Syst; 2022 Jun; 33(6):2454-2465. PubMed ID: 34170831
[TBL] [Abstract][Full Text] [Related]
10. CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention.
Chung WH; Gu YH; Yoo SJ
Sensors (Basel); 2023 Oct; 23(21):. PubMed ID: 37960445
[TBL] [Abstract][Full Text] [Related]
11. Exploring semi-supervised variational autoencoders for biomedical relation extraction.
Zhang Y; Lu Z
Methods; 2019 Aug; 166():112-119. PubMed ID: 30822516
[TBL] [Abstract][Full Text] [Related]
12. Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems.
Arellano-Espitia F; Delgado-Prieto M; Gonzalez-Abreu AD; Saucedo-Dorantes JJ; Osornio-Rios RA
Sensors (Basel); 2021 Aug; 21(17):. PubMed ID: 34502724
[TBL] [Abstract][Full Text] [Related]
13. Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos.
Zaheer MZ; Mahmood A; Astrid M; Lee SI
IEEE Trans Neural Netw Learn Syst; 2023 May; PP():. PubMed ID: 37235464
[TBL] [Abstract][Full Text] [Related]
14. Weakly Supervised Multilabel Clustering and its Applications in Computer Vision.
Xia Y; Nie L; Zhang L; Yang Y; Hong R; Li X
IEEE Trans Cybern; 2016 Dec; 46(12):3220-3232. PubMed ID: 27046858
[TBL] [Abstract][Full Text] [Related]
15. Automated screening of computed tomography using weakly supervised anomaly detection.
Hibi A; Cusimano MD; Bilbily A; Krishnan RG; Tyrrell PN
Int J Comput Assist Radiol Surg; 2023 Nov; 18(11):2001-2012. PubMed ID: 37247113
[TBL] [Abstract][Full Text] [Related]
16. Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks.
Maciąg PS; Kryszkiewicz M; Bembenik R; L Lobo J; Del Ser J
Neural Netw; 2021 Jul; 139():118-139. PubMed ID: 33689918
[TBL] [Abstract][Full Text] [Related]
17. Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild.
Sarker MI; Losada-Gutiérrez C; Marrón-Romera M; Fuentes-Jiménez D; Luengo-Sánchez S
Sensors (Basel); 2021 Jun; 21(12):. PubMed ID: 34207883
[TBL] [Abstract][Full Text] [Related]
18. A classification-based approach to semi-supervised clustering with pairwise constraints.
Śmieja M; Struski Ł; Figueiredo MAT
Neural Netw; 2020 Jul; 127():193-203. PubMed ID: 32387926
[TBL] [Abstract][Full Text] [Related]
19. An Efficient Automatic Gait Anomaly Detection Method Based on Semisupervised Clustering.
Yang Z
Comput Intell Neurosci; 2021; 2021():8840156. PubMed ID: 33643407
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s.
Li X; Wang W; Sun L; Hu B; Zhu L; Zhang J
Sci Rep; 2022 Jul; 12(1):13067. PubMed ID: 35906368
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]