Suchergebnisse - Encoder-Decoder ConvLSTM*

EDDA-ConvLSTM: Encoder-Decoder Dual Attention ConvLSTM for Moroccan Coastal Sea Surface Temperature Prediction

Autoren: Fatima Zahrae El Azhary Khalid Minaoui

Quelle: IEEE Geoscience and Remote Sensing Letters. 22:1-5

Coupled Encoder-Decoder ConvLSTM Structure for the Parameter Identification Problems of Time-Dependent Partial Differential Equations

Quelle: Advances in Applied Mathematics. 14:341-355

An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting

Autoren: Cheng Wang Kaiyu Xue Chuang Shi

Quelle: IEEE Geoscience and Remote Sensing Letters. 22:1-5

Encoder–Decoder ConvLSTM Structure with Skip Connections for Solving Time-Dependent Partial Differential Equation

Autoren: Qi Guo Jiangtao Xin Hongen Jia et al.

Quelle: International Journal of Computational Methods.

Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM Encoder-Decoder With STA Mechanism

Autoren: S. Lakshmi A. Krishnamoorthy

Quelle: IEEE Access, Vol 12, Pp 179628-179647 (2024)

Schlagwörter: BiConvLSTM, Air quality prediction, encoder-decoder, deep learning, spatial-temporal attention mechanism, prediction of PM2.5 and PM10, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/986c1e6c5ede4c3faaf7799ce205fe80

Predicting 360° Video Saliency: A ConvLSTM Encoder-Decoder Network With Spatio-Temporal Consistency

Autoren: Zhaolin Wan Han Qin Ruiqin Xiong et al.

Quelle: IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 14:311-322

Physics-Informed Encoder-Decoder Network Based on ConvLSTM for Solving Time-Dependent Partial Differential Equations

Quelle: Advances in Applied Mathematics. 13:714-722

Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation

Autoren: Jeongdae Kim Hyunseung Choo Jongpil Jeong

Quelle: Applied Sciences, Vol 14, Iss 23, p 11315 (2024)

Schlagwörter: Technology, video prediction, QH301-705.5, Physics, QC1-999, SA-ConvLSTM, 02 engineering and technology, Engineering (General). Civil engineering (General), 01 natural sciences, encoder–decoder, self-attention memory module, Chemistry, 0202 electrical engineering, electronic engineering, information engineering, TA1-2040, Biology (General), QD1-999, spatiotemporal, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/c12289f199ed4826a442d168b4203b74

ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images

Autoren: Andaç Imak Adalet Çelebi Onur Polat et al.

Quelle: Oral Radiology. 39:614-628

Schlagwörter: Artificial Intelligence, X-Rays, Tooth, Impacted, Humans, Neural Networks, Computer, Delayed Emergence from Anesthesia

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36920598
https://avesis.gazi.edu.tr/publication/details/19f102a9-1a50-4729-b99a-5a076a3b4807/oai

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction

Autoren: Deyu Zhang Changying Wang Baoxiang Huang et al.

Quelle: Applied Sciences, Vol 14, Iss 8, p 3278 (2024)

Schlagwörter: 0301 basic medicine, Technology, QH301-705.5, Physics, QC1-999, deep learning, Engineering (General). Civil engineering (General), 01 natural sciences, Chemistry, 03 medical and health sciences, pyramidal convolution, 13. Climate action, dilated encoder–decoder ConvLSTM, sea ice concentration prediction, TA1-2040, Biology (General), QD1-999, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/299eaa279c644087970737e9a35260a4

Multi-task prediction model based on ConvLSTM and encoder-decoder

Autoren: Xueliang Wei Rui Zhang Xudong Cao et al.

Quelle: Intelligent Data Analysis. 25:359-382

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Zugangs-URL: https://content.iospress.com/articles/intelligent-data-analysis/ida194969
https://doi.org/10.3233/IDA-194969
https://dblp.uni-trier.de/db/journals/ida/ida25.html#LuoCLDZW21

Forecasting Stock Market Using Machine Learning Approach Encoder-Decoder ConvLSTM

Autoren: Khurum Iqbal Ali Hassan Syed Shah Mir Ul Hassan et al.

Quelle: 2021 International Conference on Frontiers of Information Technology (FIT). :43-48

Schlagwörter: 05 social sciences, 0507 social and economic geography

An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls

Autoren: Zhao Feng Zeeshan Tariq Xianda Shen et al.

Weitere Verfasser: Zhao Feng Zeeshan Tariq Xianda Shen et al.

Quelle: Gas Science and Engineering. 125:205314

Dateibeschreibung: application/pdf

Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation.

Autoren: Kim, Jeongdae Choo, Hyunseung Jeong, Jongpil

Quelle: Applied Sciences (2076-3417); Dec2024, Vol. 14 Issue 23, p11315, 15p

Schlagwörter: LONG short-term memory, OBJECT recognition (Computer vision), PROBLEM solving, DECISION making, FORECASTING

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction.

Autoren: Zhang, Deyu Wang, Changying Huang, Baoxiang et al.

Quelle: Applied Sciences (2076-3417); Apr2024, Vol. 14 Issue 8, p3278, 21p

Schlagwörter: SEA ice, PYRAMIDS, FORECASTING, PREDICTION models, DEEP learning

ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

Autoren: Guozhen Xia Fubin Zhang Cheng Wang et al.

Quelle: Space Weather, Vol 20, Iss 8, Pp n/a-n/a (2022)

Schlagwörter: encoder‐decoder, medium‐term global prediction, Astrophysics, 7. Clean energy, 01 natural sciences, ionospheric TEC, QB460-466, 13. Climate action, Meteorology. Climatology, ConvLSTM, 0103 physical sciences, QC851-999, CNN, 0105 earth and related environmental sciences

Zugangs-URL: https://doaj.org/article/e4fe49e43e234c15b490d86bc026991f

Structural Surrogate Model and Dynamic Response Prediction with Consideration of Temporal and Spatial Evolution: An Encoder–Decoder ConvLSTM Network

Autoren: Hong Peng Yaozhi Luo Jingwen Yan et al.

Quelle: International Journal of Structural Stability and Dynamics. 21:2150140

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

Zugangs-URL: https://www.worldscientific.com/doi/abs/10.1142/S0219455421501406

Ionospheric TEC Forecasting with ED-ConvLSTM-Res Integrating Multi-Channel Features.

Autoren: Yang, Jiayue Huang, Wengeng Zhang, Lei et al.

Quelle: Remote Sensing; Nov2025, Vol. 17 Issue 21, p3564, 14p

Schlagwörter: SOLAR activity, FORECASTING, IONOSPHERIC electron density, SPATIOTEMPORAL processes, GEOMAGNETIC variations, MACHINE learning

ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction.

Autoren: Li, Yalan Deng, Haiming Xiao, Jian et al.

Quelle: Mathematics (2227-7390); Jun2025, Vol. 13 Issue 12, p1986, 17p

Schlagwörter: LONG short-term memory, PREDICTION models, PROBLEM solving, LATITUDE, ELECTRONS

Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

Autoren: Liangchao Li Haijun Liu Huijun Le et al.

Quelle: Remote Sensing, Vol 15, Iss 12, p 3064 (2023)

Schlagwörter: ionospheric TEC, ConvLSTM, encoder–decoder, spatiotemporal, geomagnetic storm, Science

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2072-4292/15/12/3064; https://doaj.org/toc/2072-4292

Zugangs-URL: https://doaj.org/article/8a8888bd35894565a1e97f29fc28d4ee