Výsledky vyhledávání - "deep autoencoder network"

Enhanced Short-Term GHI Prediction using Cloud-Aware Deep Autoencoder Network

Autoři: Kalpalathika N Jumin Salih Lakshmi P a další

Zdroj: 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA). :890-896

Proximal Mapping-Incorporated Deep Autoencoder Network with Momentum Acceleration Method for Image Denoising

Autoři: Yang Zhao Ming Li Meng Huang a další

Zdroj: 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). :1322-1327

A Novel PAPR Reduction Scheme Based on Deep Autoencoder Network for FBMC Systems

Autoři: Xing Cheng Deli Chen Shuaishuai Li a další

Zdroj: IEEE Access, Vol 13, Pp 68948-68958 (2025)

Témata: peak to average power ratio, denoising autoencoder network, deep learning, Filter bank multicarrier, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Přístupová URL adresa: https://doaj.org/article/678c4448230241fc87fe49f3e61edf69

A health indicator construction method of rolling bearing based on vibration image features and deep autoencoder network

Autoři: Yong Duan Xiangang Cao Jiangbin Zhao a další

Zdroj: 2023 5th International Conference on System Reliability and Safety Engineering (SRSE). :268-273

Hybrid deep autoencoder network based adaptive cross guided bilateral filter for motion artifacts correction and denoising from MRI

Autoři: Shiju Samuel Rohini S. Ochawar M.S.S. Rukmini

Zdroj: The Imaging Science Journal. 72:76-91

A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Autoři: Xinping Gu Yunpeng Han Junfu Yu

Zdroj: IEEE Access, Vol 8, Pp 9846-9863 (2020)

Témata: Autonomous vehicle, 0502 economics and business, 05 social sciences, Electrical engineering. Electronics. Nuclear engineering, deep autoencoder network, lane-changing identification, lane-changing decision-making, XGBoost, TK1-9971

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/8948470/08950329.pdf
https://doaj.org/article/67071ac15d7c48968c49a9255c43bf9e
https://dblp.uni-trier.de/db/journals/access/access8.html#GuHY20
https://ieeexplore.ieee.org/document/8950329/

Social movie recommender system based on deep autoencoder network using Twitter data

Autoři: Hossein Tahmasebi Reza Ravanmehr Rezvan Mohamadrezaei

Zdroj: Neural Computing and Applications. 33:1607-1623

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

Přístupová URL adresa: https://link.springer.com/article/10.1007/s00521-020-05085-1
https://dblp.uni-trier.de/db/journals/nca/nca33.html#TahmasebiRM21
https://doi.org/10.1007/s00521-020-05085-1

Direction of Arrival Estimation by Using Deep Autoencoder Network

Autoři: Ekin Nurbas Emrah Onat

Zdroj: 2020 28th Signal Processing and Communications Applications Conference (SIU). :1-4

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

Přístupová URL adresa: http://xplorestaging.ieee.org/ielx7/9302025/9302026/09302422.pdf?arnumber=9302422

A Design of Intrusion Detection using Modified Bat Algorithm and Deep Autoencoder Network

Autoři: Soundari D.V M.E. Kuralarasi R Kalieshwari S a další

Zdroj: International Journal of Innovative Technology and Exploring Engineering. 9:571-576

Investigating the Effects of Spanwise Transversal Traveling Waves on a Turbulent Compressible Flat Plate Flow With the Aid of a Deep Autoencoder Network

Autoři: Shao, Xiao Ayan, Hilmi Oguzhan Hübenthal, Fabian a další

Zdroj: Jülich : Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag, Schriften des Forschungszentrums Jülich IAS Series 69, 89-93 (2025). doi:10.34734/FZJ-2025-02462 ; Proceedings of the 35th Parallel CFD International Conference 2024 ; Proceedings of the 35th Parallel CFD International Conference 2024 35th Parallel CFD International Conference 2024, ParCFD 2024, Bonn, Germany, 2024-09-02 - 2024-09-04

Geografické téma: DE

Relation: info:eu-repo/grantAgreement/EC//951733

Dostupnost: https://juser.fz-juelich.de/record/1041830
https://juser.fz-juelich.de/search?p=id:%22FZJ-2025-02462%22

A deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection

Autoři: Zeinab Soltani Hossein Hassani Saeid Esmaeiloghli

Zdroj: Computers & Geosciences. 190:105657

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

Autoři: Yue Si Zhousuo Zhang Chuiqing Kong a další

Zdroj: Structural Health Monitoring. 19:873-884

Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

Přístupová URL adresa: https://journals.sagepub.com/doi/full/10.1177/1475921719867446

Multi-modem Implementation Method Based on Deep Autoencoder Network

Autoři: Peng Wei Ruimin Lu Shilian Wang a další

Zdroj: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030690717

Témata: 9. Industry and infrastructure

Přístupová URL adresa: https://link.springer.com/chapter/10.1007/978-3-030-69072-4_40

Circuit reliability prediction based on deep autoencoder network

Autoři: Jie Xiao Weifeng Ma Jungang Lou a další

Zdroj: Neurocomputing. 370:140-154

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

Quality assessment of building footprint data using a deep autoencoder network

Autoři: Liang Wu Zhong Xie Yongyang Xu a další

Zdroj: International Journal of Geographical Information Science. 31:1929-1951

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

Přístupová URL adresa: https://www.tandfonline.com/doi/full/10.1080/13658816.2017.1341632
http://dblp.uni-trier.de/db/journals/gis/gis31.html#XuCXW17
https://doi.org/10.1080/13658816.2017.1341632
https://dblp.uni-trier.de/db/journals/gis/gis31.html#XuCXW17

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

Autoři: Yuanyuan Zhao Huijuan Hao Yu Chen a další

Zdroj: Electronics; Volume 12; Issue 13; Pages: 2826

Témata: 0209 industrial biotechnology, novelty detection, fault diagnosis, deep learning, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Popis souboru: application/pdf

Video classification using deep autoencoder network

Autoři: Hajati, F. (Farshid) Tavakolian, M. (Mohammad)

Témata: info:eu-repo/semantics/article, info:eu-repo/semantics/acceptedVersion

URL: http://urn.fi/urn:nbn:fi-fe202102266056

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Autoři: Abbasi, Saad Famouri, Mahmoud Shafiee, Mohammad Javad a další

Témata: Computer Science - Sound, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, text

URL: http://arxiv.org/abs/2104.00528

Video Classification Using Deep Autoencoder Network

Autoři: Farshid Hajati Mohammad Tavakolian

Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030223533

Témata: 03 medical and health sciences, 0302 clinical medicine, 4. Education, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Popis souboru: application/pdf

Přístupová URL adresa: http://jultika.oulu.fi/files/nbnfi-fe202102266056.pdf
https://link.springer.com/chapter/10.1007%2F978-3-030-22354-0_45
https://doi.org/10.1007/978-3-030-22354-0_45
https://rd.springer.com/chapter/10.1007/978-3-030-22354-0_45
https://research-repository.griffith.edu.au/handle/10072/403005
https://link.springer.com/10.1007/978-3-030-22354-0_45
https://dblp.uni-trier.de/db/conf/cisis/cisis2019.html#HajatiT19
http://urn.fi/urn:nbn:fi-fe202102266056

Reduction of Trajectory Encoding Data Using a Deep Autoencoder Network: Robotic Throwing

Autoři: Rok Pahič Zvezdan Lončarević Ales Ude a další

Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030196479

Přístupová URL adresa: https://rd.springer.com/chapter/10.1007/978-3-030-19648-6_11
https://link.springer.com/chapter/10.1007/978-3-030-19648-6_11
https://dblp.uni-trier.de/db/conf/raad/raad2019.html#LoncarevicPSUG19