Suchergebnisse - "convolutional autoencoder network"

Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Autoren: Li, Sheng Jin, Liang Jiang, Jinpeng et al.

Quelle: Sensors. 22(15)

Schlagwörter: Data Management and Data Science, Civil Engineering, Information and Computing Sciences, Engineering, Clinical Research, Railroads, Technology, feature identification, track fastener looseness, distributed vibration, convolutional autoencoder network, pseudo-Hilbert scan, ultra-weak fiber optic Bragg grating, Analytical Chemistry, Environmental Science and Management, Ecology, Distributed Computing, Electrical and Electronic Engineering, Electrical engineering, Electronics, sensors and digital hardware, Environmental management, Distributed computing and systems software

Dateibeschreibung: application/pdf

Zugangs-URL: https://escholarship.org/uc/item/4jq321md
https://escholarship.org/content/qt4jq321md/qt4jq321md.pdf

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images

Autoren: Huayue Chen Ye Chen Qiuyue Wang et al.

Quelle: Sensors, Vol 22, Iss 8881, p 8881 (2022)

Schlagwörter: stacked convolutional autoencoder network, transfer learning, feature extraction, small samples, HRSI, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/22/22/8881; https://doaj.org/toc/1424-8220; https://doaj.org/article/6e5c53c528b04a9a934f22282bdde353

Verfügbarkeit: https://doi.org/10.3390/s22228881
https://doaj.org/article/6e5c53c528b04a9a934f22282bdde353

Attention mechanism-based deep denoiser for desert seismic random noise suppression

Autoren: Lin, Hongbo Liu, Chang Wang, Shigang et al.

Schlagwörter: seismic explorations, random noise suppression, deep convolutional autoencoder network, attention mechanism, low signal-to-noise ratio

Zugangs-URL: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-6efc455c-5479-4f38-ad63-96fee9d0a7b3

Unsupervised seismic data deblending based on the convolutional autoencoder regularization

Autoren: Xue, Yaru Chen, Yuyao Jiang, Minhui et al.

Schlagwörter: simultaneous source data, unsupervised learning, convolutional autoencoder network, deblending, jednoczesne dane źródłowe, uczenie bez nadzoru

Zugangs-URL: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-2f53f3a0-77ce-43b2-8241-452cc1cae5ad