Artificial Intelligence
The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD) dataset for polycrystalline solar cell, which contains 36,543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly-free images and anomalous images with 10 different categories.
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This data set contains nifty 50 companies and all their details of Open, Volume, Close, Adj Close, and name of the company.
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“DCA-IoMT Dataset” belongs to the research article entitled “DCA-IoMT: Knowledge Graph Embedding-enhanced Deep Collaborative Alerts-recommendation against COVID19 (DOI: 10.1109/TII.2022.3159710)” accepted for publication in the Journal of IEEE Transactions on Industrial Informatics.
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This dataset consists of 1878 labeled images of flowers from blackberry trees from the specie Rubus L. subgenus Rubus Watson. These are white flowers with five petals that blossom in the spring through summer. The images were collected using an Intel RealSense D435i camera inside a greenhouse.
This images were inicially collected to support a robotic autonomous pollination project.
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The dataset consists of images
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This is the supplementary material for the paper "A multimodal AI system for out-of-distribution generalization of seizure identification" in IEEE Journal of Biomedical and Health Informatics.
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QQP and Mechanical Field Q&A
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