Artificial Intelligence
Simulation data for the following paper
DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver (K. Chae and Y. Kim)
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268 Views
To verify the proposed protection scheme, the simulation model of a six-terminal ring flexible DC distribution system is built in PSCAD/EMTDC , where the rated voltage of the DC line is ±10 kV . The fault inception is set at 0.6 s, the sampling frequency is 10 kHz and the protection data window length is 1 ms. The data set reflects the current and voltage values of each line after standardization
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163 Views
This data contains training and testing data for single-shot deflectometry generated by the deformable mirror. The training data has total of 4000 data with single input composite pattern Ic and four outputs (Dx, Dy, Mx, and My).
The test data contains a pre-trained model, a script for testing, and test images
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570 Views
To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.
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142 Views
This dataset is for robust sEMG-based intention recognition with respect to upper-limb positions.
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24 Views
Water leakage problems increased over the last few years, and innovative tools and techniques appeared to solve this widespread problem. The still unresolved problem concerns the identification of water leaks at the nearest point; at the household level, the most common and inexpensive devices are still mechanical meters, which cannot detect leaks.
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541 Views
In total, about 4245 labeled images in train set, and 755 labeled images in test set. All labels in YOLOv7 format.
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1028 Views
Deep video representation learning has recently attained state-of-the-art performance in video action recognition. However, when used with video clips from varied perspectives, the performance of these models degrades significantly. Existing VAR models frequently simultaneously contain both view information and action attributes, making it difficult to learn a view-invariant representation.
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61 Views