Výsledky vyhľadávania - semi-supervised convolutional variational autoencoder

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data.

Autori: Choi, Seung-Hwan An, Dawn Lee, Inho a ďalší

Zdroj: Mathematics (2227-7390). Dec2024, Vol. 12 Issue 23, p3750. 16p.

Predmety: *INDUSTRIAL robots, *ANOMALY detection (Computer security), *AUTOENCODERS, *SYSTEM failures, *FEATURE extraction, *DEEP learning

An Improved Semi-supervised Variational Autoencoder with Gate Mechanism for Text Classification.

Autori: Ye, Haiming Zhang, Weiwen Nie, Mengna

Zdroj: International Journal of Pattern Recognition & Artificial Intelligence. Aug2022, Vol. 36 Issue 10, p1-21. 21p.

Predmety: *SUPERVISED learning, *CONVOLUTIONAL neural networks, *DEEP learning

A novel semi-supervised power transformer defect monitoring technique using unreliable pseudo-labels with highly imbalanced partial discharge signals.

Autori: Rajamayil, Manimala Basharan, Vigneshwaran

Zdroj: Electrical Engineering. Apr2025, Vol. 107 Issue 4, p4939-4957. 19p.

Predmety: *LONG short-term memory, *CONVOLUTIONAL neural networks, *AUTOENCODERS, *MARGINAL distributions, *ELECTRIC transformers

Deep Residual Learning-Based Convolutional Variational Autoencoder For Driver Fatigue Classification

Autori: Sameera Adhikari Senaka Amarakeerthi

Zdroj: Advances in Technology. :277-290

Predmety: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Deep Convolutional Variational Autoencoder for Anomalous Sound Detection

Autori: Dai Bui Huy-Dung Han Minh-Hieu Nguyen a ďalší

Zdroj: 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). :313-318

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: http://xplorestaging.ieee.org/ielx7/9352035/9352036/09352085.pdf?arnumber=9352085
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9352085

A Semi-Supervised Approach For Identifying Abnormal Heart Sounds Using Variational Autoencoder

Autori: Avik Ghose Rohan Banerjee

Zdroj: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1249-1253

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Prístupová URL adresa: https://ieeexplore.ieee.org/abstract/document/9054632
https://dblp.uni-trier.de/db/conf/icassp/icassp2020.html#BanerjeeG20

Semi-Supervised Representation Learning for Infants Biliary Atresia Screening Using Deep CNN-Based Variational Autoencoder

Autori: Dahao Zhang Bin Wang Ling Li a ďalší

Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030002138

Prístupová URL adresa: https://link.springer.com/chapter/10.1007/978-3-030-00214-5_148

Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model.

Autori: Om Kumar, C. U. Marappan, Suguna Murugeshan, Bhavadharini a ďalší

Zdroj: International Journal of Network Management; Nov/Dec2024, Vol. 34 Issue 6, p1-17, 17p

Predmety: SMART cities, SMART devices, DEEP learning, INTERNET of things, DATA quality, INTRUSION detection systems (Computer security)

Exploring semi-supervised variational autoencoders for biomedical relation extraction.

Autori: Zhang, Yijia Lu, Zhiyong

Zdroj: Methods. Aug2019, Vol. 166, p112-119. 8p.

Predmety: *BIG data, *EXTRACTION (Chemistry), *EXTRACTION techniques, *SUPERVISED learning, *SHORT-term memory, *PROTEIN-protein interactions

Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data.

Autori: Matsuo, Hidetoshi Kitajima, Kazuhiro Kono, Atsushi K. a ďalší

Zdroj: Medical Physics. Dec2023, Vol. 50 Issue 12, p7548-7557. 10p.

Predmety: *POSITRON emission tomography computed tomography, *POSITRON emission tomography, *THREE-dimensional imaging, *DIAGNOSTIC imaging, *RECEIVER operating characteristic curves, *SURVIVAL analysis (Biometry), *STOCHASTIC learning models

Unsupervised deep learning approach for structural anomaly detection using probabilistic features.

Autori: Wan, Hua-Ping Zhu, Yi-Kai Luo, Yaozhi a ďalší

Zdroj: Structural Health Monitoring; Jan2025, Vol. 24 Issue 1, p3-33, 31p

Predmety: MACHINE learning, STRUCTURAL health monitoring, DISTRIBUTION (Probability theory), AUTOENCODERS, VECTOR data, DEEP learning

Supervised and semi-supervised deep probabilistic models for indoor positioning problems.

Autori: Qian, Weizhu Lauri, Fabrice Gechter, Franck

Zdroj: Neurocomputing. May2021, Vol. 435, p228-238. 11p.

Predmety: *SUPERVISED learning, *RECURRENT neural networks, *CONVOLUTIONAL neural networks

Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy.

Autori: Chen, Wenjing Kang, Tongzhou Heyat, Md Belal Bin a ďalší

Zdroj: Frontiers in Neurology; 2025, p1-11, 11p

Predmety: K-means clustering, WAVELET transforms, AUTOENCODERS, MACHINE learning, PEOPLE with epilepsy

Unsupervised convolutional variational autoencoder deep embedding clustering for Raman spectra.

Autori: Yixin Guo Weiqi Jin Weilin Wang a ďalší

Zdroj: Analytical Methods; 10/21/2022, Vol. 14 Issue 39, p3898-3910, 13p

A semi-supervised temporal modeling strategy integrating VAE and Wasserstein GAN under sparse sampling constraints.

Autori: Hu, Yujie Xie, Changrui Chen, Xi

Zdroj: Journal of Process Control. Aug2025, Vol. 152, pN.PAG-N.PAG. 1p.

Predmety: *GENERATIVE adversarial networks, *LONG short-term memory, *TIME series analysis, *AUTOENCODERS, *LATENT variables, *SUPERVISED learning

Semi-supervised method for tunnel blasting quality prediction using measurement while drilling data

Autori: Hengxiang Jin Qian Fang Jun Wang a ďalší

Zdroj: Journal of Rock Mechanics and Geotechnical Engineering, Vol 17, Iss 5, Pp 2633-2649 (2025)

Predmety: Semi-supervised learning, Measurement while drilling (MWD), TA703-712, Engineering geology. Rock mechanics. Soil mechanics. Underground construction, Over/under excavation, Tunnel blasting quality

Prístupová URL adresa: https://doaj.org/article/36306ea7c4f34e8283f27b96db764e1a

A semi-supervised autoencoder framework for joint generation and classification of breathing.

Autori: Pastor-Serrano, Oscar Lathouwers, Danny Perkó, Zoltán

Zdroj: Computer Methods & Programs in Biomedicine. Sep2021, Vol. 209, pN.PAG-N.PAG. 1p.

Predmety: *COMPUTER-aided diagnosis, *CONVOLUTIONAL neural networks, *RESPIRATION, *ALGORITHMS, *TIME series analysis

A semi-supervised approach to dark matter searches in direct detection data with machine learning

Autori: Herrero-Garcia, Juan Patrick, Riley Scaffidi, Andre

Témy: High Energy Physics - Phenomenology, text

URL: http://arxiv.org/abs/2110.12248

Detection of Anomalous Grapevine Berries Using Variational Autoencoders.

Autori: Miranda, Miro Zabawa, Laura Kicherer, Anna a ďalší

Zdroj: Frontiers in Plant Science; 6/1/2022, Vol. 13, p1-11, 11p

Predmety: GRAPES, CROP quality, PLANT performance, MACHINE learning, DECISION making, VITIS vinifera, BERRIES

Fault diagnosis for high-dimensional complex processes using semi-supervised deep convolutional generative models ; 준지도학습 심층 생성 모델을 활용한 공정 고장 분류

Autori: Ko, Taeyoung Kim, Heeyoung 김희영 a ďalší

Prispievatelia: Ko, Taeyoung Kim, Heeyoung 김희영 a ďalší

Predmety: convolutional auxiliary deep generative model▼amultivariate time series, semi-supervised convolutional variational autoencoder▼aTennessee Eastman process▼aunlabeled data, 합성곱 보조변수 심층 생성 모델▼a다변수 시계열▼a준지도학습 합성곱 변분 오토인코더▼a테네시 이스트만 공정▼a레이블이 없는 데이터

Relation: 843192; http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843192&flag=dissertation; http://hdl.handle.net/10203/266236; 325007; 1935

Dostupnosť: http://hdl.handle.net/10203/266236
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843192&flag=dissertation