Výsledky vyhledávání - "convolutional variational autoencoder"

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

Autoři: Rui He Yupeng Chang Sumin Zhang a další

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

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

Autoři: Yiming Jia Mehrdad Sasani

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

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

Autoři: Fataneh Dabaghi-Zarandi Hassan Ezzaidi Michel Gauvin a další

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

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

Přístupová URL adresa: https://doaj.org/article/ac289464ef3d4fcab92c01b9c0328203

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

Autoři: Ahmed Mesai Belgacem Mounir Hadef Kheira Kahili a další

Zdroj: IEEE Access. 13:150537-150554

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

Autoři: Duo Zhang Hongyi Bai Laijun Sun a další

Zdroj: Analytical Methods. 17:2446-2455

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/40035486

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Autoři: Maksudul Alam Srikanth B. Yoginath Isabelle Snyder a další

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

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

Autoři: Meng Hu Shaohua Wang Yude Xu a další

Zdroj: Lecture Notes in Mechanical Engineering ISBN: 9789819611904

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

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

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

Patch-based Finger Vein Verification using Convolutional Variational Autoencoder

Autoři: Raul Ismayilov Tugce Arican Luuk Spreeuwers a další

Zdroj: 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

Autoři: Youngbin Ahn Jonggeun Choe Baehyun Min

Zdroj: Engineering Applications of Artificial Intelligence. 159:111620

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

Autoři: Vicky Zilvan Ade Ramdan Ana Heryana a další

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

Témata: 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

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

Opinion Unaware Image Quality Assessment via Adversarial Convolutional Variational Autoencoder

Autoři: Ankit Shukla Avinash Upadhyay Swati Bhugra a další

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

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

Autoři: Sijie Liu Nan Zhou Zekun Bai a další

Zdroj: 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

Autoři: Ryad Zemouri Melanie Levesque Normand Amyot a další

Přispěvatelé: Ryad Zemouri Melanie Levesque Normand Amyot a další

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

Témata: 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

Popis souboru: application/pdf

Přístupová URL adresa: 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

Autoři: Ramezankhani, Milad Harandi, Mehrtash Seethaler, Rudolf a další

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

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

Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment

Autoři: Chunxiao Ning Yazhou Xie

Zdroj: Computer-Aided Civil and Infrastructure Engineering. 39:165-185

Témata: 0211 other engineering and technologies, 02 engineering and technology, 0201 civil engineering

Compact Image Transformer Based on Convolutional Variational Autoencoder with Augmented Attention Backbone for Target Recognition in Infrared Images

Autoři: Billel Nebili Atmane Khellal Abdelkrim Nemra a další

Zdroj: Arabian Journal for Science and Engineering. 49:3383-3396

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

Autoři: C. U. Om Kumar Suguna Marappan Bhavadharini Murugeshan a další

Zdroj: International Journal of Network Management. 34

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

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

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

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

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

Autoři: Saleem, Arslan Direkoğlu, Cem Electrical and Electronics Engineering a další

Přispěvatelé: Saleem, Arslan Direkoğlu, Cem Electrical and Electronics Engineering a další

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

Popis souboru: application/pdf

Přístupová URL adresa: https://hdl.handle.net/11511/116183