Suchergebnisse - "Spatial-Temporal Autoencoder"

Stacked Spatial-Temporal Autoencoder for Quality Prediction in Industrial Processes von Yan, Feng, Yang, Chunjie, Zhang, Xinmin

“… Therefore, in this paper, a stacked spatial-temporal autoencoder (S 2 TAE) is proposed to enhance the representation learning capability for soft sensor modeling by taking the spatial-temporal correlations into consideration …”
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Robust Spatial-Temporal Autoencoder for Unsupervised Anomaly Detection of Unmanned Aerial Vehicle With Flight Data von Jiang, Guoqian, Nan, Pengcheng, Zhang, Jingchao, Li, Yingwei, Li, Xiaoli

“… Specifically, we designed a new robust spatial-temporal autoencoder (RSTAE) model based on the temporal convolution network (TCN …”
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Efficient identification of Alzheimer’s brain dynamics with Spatial-Temporal Autoencoder: A deep learning approach for diagnosing brain disorders von Wu, Lingyun, Zhao, Quanfa, Liu, Jing, Yu, Haitao

“… •A Spatial-Temporal Autoencoder (STAE) framework was designed to estimate latent factors of EEG via unsupervised learning …”
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STAR: A Spatial-Temporal Autoencoder for EEG Restoration in Emotion Recognition von Yin, Hao-Long, Zheng, Wei-Long, Lu, Bao-Liang

“… To overcome these challenges, we introduce the Spatial-Temporal Autoencoder for EEG Restoration (STAR …”
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Anomaly detection of industrial multi-sensor signals based on enhanced spatiotemporal features von Jiang, Lin, Xu, Hang, Liu, Jinhai, Shen, Xiangkai, Lu, Senxiang, Shi, Zhan

“… Then, a stack spatial–temporal autoencoder, which relies on the improved …”
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Feature selection algorithm assisted residual channel attention spatio-temporal auto encoder for video anomaly detection von Prasudha, M. Lakshmi, Sukhavasi, Vidyullatha, Neha, Kandula, Lunawat, Poonam Shaylesh

“… The common use of video cameras in public and private spaces for traffic management, security, and anomaly detection has brought significant benefits. However, …”
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Enhanced Intelligent Video Monitoring using Hybrid Integration of Spatiotemporal Autoencoders and Convolutional LSTMs von Umale-Nagmote, Ankita, Goel, Charu, Lal, Nidhi

“… This paper proposes a hybrid deep learning framework that combines spatial-temporal autoencoders with convolutional LSTMs for automated anomaly detection in surveillance videos …”
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