Sep 15, 2023 · The CNN-CBAM model proposed in this study consists of CNN and CBAM attention mechanisms, and the BiGRU model is used to predict time series data ...
Therefore, a new time-series data feature extraction model (CNN-CBAM) that integrates convolutional neural networks (CNN) and convolutional attention mechanisms ...
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Wan, T.H., Tsang, C.W., Hui, K., et al.: Anomaly detection of train wheels utilizing short-time Fourier transform and unsupervised learning algorithms. · Zheng, ...
In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and ...
Jan 1, 2024 · We propose a novel M-CNN network for shale gas production prediction, where high-dimensional shale gas time-series data are encoded into 2D ...
Aug 22, 2022 · We believe that it is necessary to extract the temporal and spatial features of time series satellite images for typhoon path prediction.
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and ...
Usually, 1D CNN would be utilized to extract feature from the raw time series data. The filter of 1D CNN is performed as the pattern detector, which can.
Jan 30, 2024 · The temporal feature block is used to extract high-dimensional features of different bands in EEG signals, and the CBAM attention mechanism is ...
CNN Model for Time-Series Analysis | by Akash Singh - Medium
medium.com › cnn-model-for-time-serie...
Feb 12, 2024 · In summary, a CNN model for time series data leverages convolutional operations to extract temporal features from sequential data, enabling ...
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