Suchergebnisse - convolutional LSTM autoencoder*

Convolutional LSTM Neural Network Autoencoder Based Fault Detection in Manufacturing Predictive Maintenance

Autoren: Young Jun Park

Quelle: Journal of Machine and Computing. :914-923

Video Anomaly Detection using Variational Convolutional LSTM Autoencoder

Autoren: Sandhya Rani Sahoo Jaideep Kokkiligadda Ratnakar Dash

Quelle: 2023 International Conference on Communication, Circuits, and Systems (IC3S). :1-5

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder

Autoren: Yangde Gao Zahoor Ahmad Jong-Myon Kim

Quelle: Sensors (Basel)
Sensors, Vol 24, Iss 8, p 2382 (2024)

Schlagwörter: health index analysis, convolutional LSTM autoencoder, Chemical technology, Communication, remaining useful life, 0202 electrical engineering, electronic engineering, information engineering, TP1-1185, 02 engineering and technology, 0210 nano-technology, 3. Good health

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38675999
https://doaj.org/article/e9c4faea42fb40c3a039f68109ed00b5

Enhancing Video Anomaly Detection Using Spatio-Temporal Autoencoders and Convolutional LSTM Networks

Autoren: Ghayth Almahadin Maheswari Subburaj Mohammad Hiari et al.

Quelle: SN Computer Science. 5

A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders

Autoren: Cinzia Giannetti Aniekan Essien

Quelle: 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). :1-6

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

Zugangs-URL: https://cronfa.swan.ac.uk/Record/cronfa51622/Download/0051622-29082019102938.pdf
https://cronfa.swan.ac.uk/Record/cronfa51622/Download/0051622-29082019102938.pdf
https://ieeexplore.ieee.org/document/8778417/
https://cronfa.swansea.ac.uk/Record/cronfa51622/Details
https://cronfa.swansea.ac.uk/Record/cronfa51622/Download/0051622-29082019102938.pdf
https://dblp.uni-trier.de/db/conf/inista/inista2019.html#EssienG19
https://doi.org/10.1109/INISTA.2019.8778417

A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders

Autoren: Aniekan Essien Cinzia Giannetti

Quelle: IEEE Transactions on Industrial Informatics. 16:6069-6078

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

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/9424/9106618/08967003.pdf
https://cronfa.swan.ac.uk/Record/cronfa53318
https://dblp.uni-trier.de/db/journals/tii/tii16.html#EssienG20
https://cronfa.swan.ac.uk/Record/cronfa53318/Download/53318__17414__2da379d11c2f437aa113e87ad1ed306b.pdf
https://ieeexplore.ieee.org/iel7/9424/9106618/08967003.pdf
https://ieeexplore.ieee.org/abstract/document/8967003/
https://cronfa.swan.ac.uk/Record/cronfa53318/Download/53318__17414__2da379d11c2f437aa113e87ad1ed306b.pdf

Bidirectional Convolutional LSTM Autoencoder for Risk Detection

Quelle: International Journal of Advanced Trends in Computer Science and Engineering. 9:8585-8589

StepEncog: A Convolutional LSTM Autoencoder for Near-Perfect fMRI Encoding

Autoren: Raju S. Bapi Manish Gupta Vijay Rowtula et al.

Quelle: 2019 International Joint Conference on Neural Networks (IJCNN). :1-8

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

Exploring a Convolutional Autoencoder with LSTM on E-commerce Time-Series Forecasting

Autoren: Sivertsen, Sindre J. I. Sander, K. Kilen Kofod-Peterson, Anders

Weitere Verfasser: Sivertsen, Sindre J. I. Sander, K. Kilen Kofod-Peterson, Anders

Verfügbarkeit: https://hdl.handle.net/11250/3049064

Exploring a Convolutional Autoencoder with LSTM on E-commerce Time-Series Forecasting

Autoren: Sivertsen, Sindre I. J. Kilen, Sander K Kofod-Petersen, Anders

Weitere Verfasser: Sivertsen, Sindre I. J. Kilen, Sander K Kofod-Petersen, Anders

Verfügbarkeit: https://hdl.handle.net/11250/3026814

Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM

Autoren: Ki-Su Kim June-Beom Lee Myung-Il Roh et al.

Quelle: Journal of Marine Science and Engineering
Volume 8
Issue 10

Schlagwörter: ocean weather, 13. Climate action, 9. Industry and infrastructure, deep learning, 14. Life underwater, denoising AutoEncoder, convolutional LSTM, 01 natural sciences, 0105 earth and related environmental sciences

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.mdpi.com/2077-1312/8/10/805/pdf
https://www.mdpi.com/2077-1312/8/10/805/htm
https://www.mdpi.com/2077-1312/8/10/805/pdf
https://www.mendeley.com/catalogue/ac93d23c-7ba9-3665-b5a3-a0aa626f7aaf/

Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

Autoren: Gallego, Antonio Javier orcid:0000-0003-3148- Gil, Pablo et al.

Schlagwörter: Supervised learning, neural networks, coast detection, ship detection, oil spills, side-looking airborne radar

Relation: https://zenodo.org/records/12783380; oai:zenodo.org:12783380; https://doi.org/10.3390/rs11121402

Verfügbarkeit: https://doi.org/10.3390/rs11121402
https://zenodo.org/records/12783380
https://www.mdpi.com/2072-4292/11/12/1402

Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

Autoren: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal Gallego, Antonio-Javier et al.

Index Begriffe: Side-looking airborne radar, Oil spills, Ship detection, Coast detection, Neural networks, Supervised learning, info:eu-repo/semantics/article

URL: http://hdl.handle.net/10045/92897
https://doi.org/10.3390/rs11121402
https://doi.org/10.3390/rs11121402

Thick cloud removal of Landsat time-series using convolutional LSTM with embedded residual modules

Autoren: Wang, Lanxing Wang, Qunming Atkinson, Peter M.

Index Begriffe: Journal Article, PeerReviewed

URL: https://eprints.lancs.ac.uk/id/eprint/230718/
https://eprints.lancs.ac.uk/id/eprint/230718

Air Traffic Prediction as a Video Prediction Problem Using Convolutional LSTM and Autoencoder

Autoren: Hyewook Kim Keumjin Lee

Quelle: Aerospace, Vol 8, Iss 10, p 301 (2021)

Schlagwörter: air traffic prediction, trajectory prediction, deep learning, video prediction, Motor vehicles. Aeronautics. Astronautics, TL1-4050

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2226-4310/8/10/301; https://doaj.org/toc/2226-4310

Zugangs-URL: https://doaj.org/article/b42a82d562e44f1c90ef1765f0c5f0ef

The Prediction of the Remaining Useful Life of Rotating Machinery Based on an Adaptive Maximum Second-Order Cyclostationarity Blind Deconvolution and a Convolutional LSTM Autoencoder.

Autoren: Gao, Yangde Ahmad, Zahoor Kim, Jong-Myon

Quelle: Sensors (14248220); Apr2024, Vol. 24 Issue 8, p2382, 13p

Schlagwörter: REMAINING useful life, MONITORING of machinery, ROTATING machinery, SIGNAL denoising, FEATURE extraction

Thick Cloud Removal of Landsat Time-Series Using Convolutional LSTM With Embedded Residual Modules

Autoren: Lanxing Wang Qunming Wang Peter M. Atkinson

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 63:1-16

Dateibeschreibung: text

Zugangs-URL: https://eprints.lancs.ac.uk/id/eprint/230718/

Fall Detection from Thermal Camera Using Convolutional LSTM Autoencoder

Autoren: Jacob Nogas Shehroz S. Khan Alex Mihailidis

Quelle: EasyChair Preprints.

Zugangs-URL: https://easychair.org/publications/preprint_open/S48v
https://easychair.org/publications/preprint/S48v
https://easychair.org/publications/preprint_open/S48v

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

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

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

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

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11511/116183

Anomaly Detection Using Prediction Error with Spatio-Temporal Convolutional LSTM

Autoren: Tran, H. T. (Hanh) Hogg, D. (David)

Quelle: The University of Danang - Journal of Science and Technology

Schlagwörter: Indonesia, Convolutional LSTM, convolutional autoencoder, prediction error, reconstruction error, anomaly detection

Dateibeschreibung: application/pdf

Verfügbarkeit: https://www.neliti.com/publications/453677/anomaly-detection-using-prediction-error-with-spatio-temporal-convolutional-lstm