Search Results - convolutional variational autoencoder

Personalized Car-Following Strategy Based on Convolutional Variational Autoencoder and Mildly Conservative Q-Learning

Authors: Rui He Yupeng Chang Sumin Zhang et al.

Source: IEEE Transactions on Vehicular Technology. 74:13372-13386

Convolutional variational autoencoder for Northeast US coastal wind and flood hazard data augmentation

Authors: Yiming Jia Mehrdad Sasani

Source: Neural Computing and Applications. 37:9537-9564

Convolutional Variational Autoencoder for Anomaly Detection in On-Load Tap Changers

Authors: Fataneh Dabaghi-Zarandi Hassan Ezzaidi Michel Gauvin et al.

Source: IEEE Access, Vol 13, Pp 50838-50848 (2025)

Subject Terms: Transformer, CVAE, OLTC, vibro-acoustic signal, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/ac289464ef3d4fcab92c01b9c0328203

Advanced Fault Diagnosis in Photovoltaic Arrays Using a Metropolis-Hastings Convolutional Variational Autoencoder

Authors: Ahmed Mesai Belgacem Mounir Hadef Kheira Kahili et al.

Source: IEEE Access. 13:150537-150554

Ultraviolet spectral transfer based on a convolutional variational autoencoder model for detecting chemical oxygen demand in rivers

Authors: Duo Zhang Hongyi Bai Laijun Sun et al.

Source: Analytical Methods. 17:2446-2455

Access URL: https://pubmed.ncbi.nlm.nih.gov/40035486

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Authors: Seyfal Sultanov R. A. W. Ayyubi James P. Buban et al.

Source: Small. 21

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Authors: Maksudul Alam Srikanth B. Yoginath Isabelle Snyder et al.

Source: IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. :1-6

A NOVEL DEPTHWISE CONVOLUTIONAL VARIATIONAL AUTOENCODER FOR ANOMALY DETECTION IN COMPLEX TRAFFIC SCENARIOS FROM UAV VIEWS

Authors: Saleem, Arslan Direkoğlu, Cem Electrical and Electronics Engineering et al.

Contributors: Saleem, Arslan Direkoğlu, Cem Electrical and Electronics Engineering et al.

Subject Terms: Variational Autoencoder, Anomaly Detection, Unsupervised Learning, Depthwise Convolutional, Traffic Surveillance

File Description: application/pdf

Access URL: https://hdl.handle.net/11511/116183

Identification of Composite Damages in Cement-Based Structures Using Convolutional Variational Autoencoder Combined with Surface Wave Dispersion Energy

Authors: Meng Hu Shaohua Wang Yude Xu et al.

Source: Lecture Notes in Mechanical Engineering ISBN: 9789819611904

Unsupervised Bayesian Surprise Detection in Spatial Audio with Convolutional Variational Autoencoder and LSTM Model

Authors: Arman Nik Khah Chitsein A.D. Htun Ravi Prakash

Source: Proceedings of the 2024 ACM International Conference on Interactive Media Experiences Workshops. :116-121

Investigation of convolutional variational autoencoders for facies calibration within ensemble-smoother multiple data assimilation.

Authors: Abbate, Emanuela Dovera, Laura Di Curzio, Davide et al.

Source: Computational Geosciences. Oct2025, Vol. 29 Issue 5, p1-22. 22p.

Patch-based Finger Vein Verification using Convolutional Variational Autoencoder

Authors: Raul Ismayilov Tugce Arican Luuk Spreeuwers et al.

Source: 2024 12th International Workshop on Biometrics and Forensics (IWBF). :1-6

History matching of a three dimensional channelized reservoir using a beta-convolutional variational autoencoder and ensemble smoother with multiple data assimilation

Authors: Youngbin Ahn Jonggeun Choe Baehyun Min

Source: Engineering Applications of Artificial Intelligence. 159:111620

Convolutional variational autoencoder-based feature learning for automatic tea clone recognition

Authors: Vicky Zilvan Ade Ramdan Ana Heryana et al.

Source: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 6, Pp 3332-3342 (2022)

Subject Terms: 2. Zero hunger, Electronic computers. Computer science, Feature learning, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, QA75.5-76.95, 02 engineering and technology, 15. Life on land, 01 natural sciences, Convolutional variational autoencoder, Tea clones recognition, 0105 earth and related environmental sciences

Access URL: https://doaj.org/article/a442bfac9146424bbb8cceaaa07b10f0
https://www.sciencedirect.com/science/article/abs/pii/S1319157821000227
https://www.sciencedirect.com/science/article/pii/S1319157821000227

Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

Authors: Taufique Ahmed Luca Longo Technological University Dublin, Ireland, under Grant PB04433.

Contributors: Taufique Ahmed Luca Longo Technological University Dublin, Ireland, under Grant PB04433.

Source: IEEE Access, Vol 10, Pp 107575-107586 (2022)
Articles

Subject Terms: and neural networks, frequency bands, spectral topographic maps, deep learning, Electroencephalography, 02 engineering and technology, Electrical and Computer Engineering, TK1-9971, latent space, convolutional variational autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering

File Description: application/pdf

Access URL: https://doaj.org/article/e3dc65d627ad45ee95e687f9ea24f0ee
https://researchprofiles.tudublin.ie/en/publications/7e2afb67-6b80-443c-8a7f-5bbc0c8b03d8
https://doi.org/10.1109/ACCESS.2022.3212777
https://arrow.tudublin.ie/context/scschcomart/article/1193/viewcontent/Examining_the_Size_of_the_Latent_Space_of.pdf

Opinion Unaware Image Quality Assessment via Adversarial Convolutional Variational Autoencoder

Authors: Ankit Shukla Avinash Upadhyay Swati Bhugra et al.

Source: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). :2142-2152

LSC-VAE: a lightweight separable convolutional variational autoencoder for unsupervised and generalisable gearbox fault detection.

Authors: Das, Oguzhan Bagci Das, Duygu Ozturk, Hasan

Source: Nondestructive Testing & Evaluation. Dec2025, p1-25. 25p. 14 Illustrations.

The Study on Multivariate Time Series Anomaly Detection via Convolutional Variational Autoencoder

Authors: Sijie Liu Nan Zhou Zekun Bai et al.

Source: 2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). :605-610

Deep Convolutional Variational Autoencoder as a 2D-Visualization Tool for Partial Discharge Source Classification in Hydrogenerators

Authors: Ryad Zemouri Melanie Levesque Normand Amyot et al.

Contributors: Ryad Zemouri Melanie Levesque Normand Amyot et al.

Source: IEEE Access, Vol 8, Pp 5438-5454 (2020)

Subject Terms: Hydrogenerators, diagnosis, feature extraction, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 15. Life on land, 7. Clean energy, TK1-9971, convolutional variational autoencoder, model interpretation, deep neural networks, 13. Climate action, data visualization, generative model, Electrical engineering. Electronics. Nuclear engineering, partial discharges

File Description: application/pdf

Access URL: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08944065.pdf
https://doaj.org/article/e4b6f42fa99746b78b183493842fbf33
https://hal.archives-ouvertes.fr/hal-02462252/document
https://doi.org/10.1109/ACCESS.2019.2962775
https://dblp.uni-trier.de/db/journals/access/access8.html#ZemouriLAHKT20
https://ieeexplore.ieee.org/document/8944065/
https://hal.archives-ouvertes.fr/hal-02462252
https://hal.science/hal-02462252v1/document
https://hal.science/hal-02462252v1
https://doi.org/10.1109/access.2019.2962775

Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming

Authors: Ramezankhani, Milad Harandi, Mehrtash Seethaler, Rudolf et al.

Source: International Journal of Computer Integrated Manufacturing. 37:18-36

Subject Terms: 0209 industrial biotechnology, 9. Industry and infrastructure, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology