Artificial Intelligence
This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model
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A Multi-Agent Approach for Personalized Hypertension Risk Prediction
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Trajectory generation for robotic pick-and-place task using nested double-memory deep deterministic policy gradient.
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YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.
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The data-set used in the paper titled "Short-Term Load Forecasting Using an LSTM Neural Network."
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Data for outlier test
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This file is the related program and data of a deep interpolation convnet for bearing fault classification under complex conditions
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Time series univariate
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