Suchergebnisse - "temporal autoencoder"

Spatio-Temporal AutoEncoder for Traffic Flow Prediction von Liu, Mingzhe, Zhu, Tongyu, Ye, Junchen, Meng, Qingxin, Sun, Leilei, Du, Bowen

“… Along this line, we propose a novel autoencoder-based traffic flow prediction method, named Spatio-Temporal AutoEncoder (ST-AE …”
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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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Map-Informed Trajectory Recovery With Adaptive Spatio-Temporal Autoencoder von Ye, Yongchao, Wang, Ao, Zeb, Adnan, Zhang, Shiyao, Jianqiao Yu, James

“… To address these challenges, we propose a novel Map-informed Adaptive Spatio-Temporal Autoencoder, which follows an encoder-decoder architecture for trajectory recovery …”
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Fault detection for lithium-ion batteries of electric vehicles with spatio-temporal autoencoder von Li, Heng, Liu, Zhijun, Bin Kaleem, Muaaz, Duan, Lijun, Ruan, Siqi, Liu, Weirong

“… In this paper, a spatio-temporal autoencoder is proposed to address this limitation by learning the complex time dependence of time series data while considering inconsistencies within the battery pack …”
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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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Self-supervised temporal autoencoder for egocentric action segmentation von Zhang, Mingming, Liu, Dong, Hu, Shizhe, Yan, Xiaoqiang, Sun, Zhongchuan, Ye, Yangdong

“… : a self-supervised temporal autoencoder (SSTAE). Instead of directly learning visual features, the SSTAE is implemented by encoding the preceding target frame …”
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Early Gas Kick Warning Based on Temporal Autoencoder von Zhu, Zhaopeng, Zhou, Detao, Yang, Donghan, Song, Xianzhi, Zhou, Mengmeng, Zhang, Chengkai, Duan, Shiming, Zhu, Lin

“… The timing of the data is not taken into account by the majority of risk warnings today. However, identifying temporal fluctuations in data, which is a vital …”
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PET-AE: Physics-informed enhanced temporal autoencoder for incipient fault detection of shafting systems von Gao, Zhan, Yu, Kaiwei, Wu, Jun, Jiang, Weixiong, Yang, Bo

“… •A physics-informed enhanced temporal autoencoder is proposed for incipient fault detection …”
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Abnormal gait recognition with millimetre‐wave radar based on perceptual loss and convolutional temporal autoencoder von Zhao, Peng, Hong, Ling, Wang, Yu

“… Early detection of an abnormal gait can aid subsequent treatment and rehabilitation. This paper proposes a novel abnormal gait recognition method based on a perceptual loss convolutional temporal autoencoder (PLCTAE) network …”
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MDSTA: masked diffusion spatio-temporal autoencoder for multimodal remote sensing image classification von Yue, Zongqin, Xu, Jindong, Li, Ziyi, Xing, Haihua, Cheng, Xiang

“… information when one modality is missing, resulting in unsatisfying performance. To address this, we propose the Masked Diffusion Spatio-Temporal Autoencoder (MDSTA …”
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Variable-Wise Stacked Temporal Autoencoder for Intelligent Fault Diagnosis of Industrial Systems von Liu, Lang, Zheng, Ying, Liang, Shaojun

“… Fault diagnosis of dynamic multivariate systems is a challenging problem. In this article, a novel fault diagnosis scheme based on variable-wise stacked temporal autoencoder (VW-STAE) is proposed …”
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Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification von Nejedly, Petr, Kremen, Vaclav, Lepkova, Kamila, Mivalt, Filip, Sladky, Vladimir, Pridalova, Tereza, Plesinger, Filip, Jurak, Pavel, Pail, Martin, Brazdil, Milan, Klimes, Petr, Worrell, Gregory

“… Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and …”
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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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Novel Temporal Autoencoder Model Based on STD for Industrial Soft Sensing von He, Yan-Lin, Jiang, Yu, Xu, Yuan, Zhu, Qun-Xiong

“… To address this limitation, we propose a novel temporal autoencoder (TAE). This model enhances process data along the temporal dimension through multiple augmentation …”
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Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder von Wang, Lin, Zhou, Fuqiang, Li, Zuoxin, Zuo, Wangxia, Tan, Haishu

“… The LSTM Encoder-Decoder framework is used to learn representation of video sequences and applied for detect abnormal event in complex environment. However, it …”
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Reverse erasure guided spatio-temporal autoencoder with compact feature representation for video anomaly detection von Zhong, Yuanhong, Chen, Xia, Jiang, Jinyang, Ren, Fan

“… Conclusion In conventional video anomaly detection based on deep learning, the deep network is optimized without focus and the similarity between different …”
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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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End to End Lip Synchronization with a Temporal AutoEncoder von Shalev, Yoav, Wolf, Lior

“… We study the problem of syncing the lip movement in a video with the audio stream. Our solution finds an optimal alignment using a dual-domain recurrent neural …”
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Structural-temporal mining for motif-level anomaly detection in dynamic graphs von Huang, Changqin, Yu, Binghang, Gao, Chengling, Tu, Yaxin, Jiang, Fan, Huang, Xiaodi

“… Dynamic graph anomaly detection (DGAD) has received significant attention due to the dynamic nature of real-world graphical data. As fundamental subgraph …”
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Short-term multi-step wind power prediction model based on Pt-Transformer neural network integrating spatio-temporal feature and sparse attention von Zhang, Zhiyan, Sun, Zhenyang, Guo, Xianghui, Guo, Ruipeng, Yang, Xiaoliang, Yang, Pengju

“… Secondly, a seasonal trend decomposition (STD) model combined with a temporal autoencoder …”
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