Artificial Intelligence
The ACDC dataset contains short-axis cardiac magnetic resonance imaging (MRI) images which are obtained from 100 patients. For each image, segmentation labels are provided for the left ventricle (LV), myocardium (Myo), and right ventricle (RV). From these, 70, 10, and 20 patient cases are randomly selected as the training, validation, and test sets, respectively.
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DJI 4 Pro UAV with a wheelbase of 350 mm, camera pixels of 20 million pixels, image sensor of 1 inch CMOS, lens parameters of FOV 84°, 8.8 mm / 24 mm (35 mm format equivalent), and aperture of f/2.8-f/11. Equipped with GPS/GLONASS dual mode positioning, the captured image resolution is 5472 pixelsÍ3078 pixels, and the aspect ratio is 16:9. The flight adopts the route automatically planned by the DJI UAV, and the aerial photography is completed and landed with automatic return.
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This is the dataset of the paper: A Non-Invasive Circuit Breaker Arc Duration Measurement Method with Improved Robustness Based on Vibration–Sound Fusion and Convolutional Neural Network (https://doi.org/10.3390/en16186551). Here is the abstract of the paper. Previous studies have shown that the contact wear estimation of circuit breakers can be based on the accumulative arc duration. However, one problem that remains unresolved is how to reliably measure the arc duration.
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The PID is collected by small unmanned aerial vehicles equipped with LiDAR.
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While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share.
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Objective: As one branch of human-computerinteraction, affective Brain-Computer Interfaces (aBCI) interpretand utilize electroencephalogram (EEG) signalsto achieve real-time monitoring and recognition of individualemotional states, opening new possibilities foremotion-aware technologies and applications. However,the challenge of individual differences in EEG emotiondata severely constrains the effectiveness and generalizationcapability of existing models.
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This data set is from the pictures collected at the industrial production site of the power adapter.The dataset contains surface defects such as scratches, glue spills, dirt, dirty spots, and off-frame labels that occur during the production process of the power adapter. The original dataset contains 235 images with a height and width of 2448 and 2048, which are annotated in VOC2007 format. These defects were categorized into five classes: label, mark, scratch, smudge, and spill, with each class containing 32, 38, 58, 80, and 32 images.
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An AI-based Ancient Hebrew Language Translator aims to revive Ancient Hebrew by constructing a comprehensive dataset with contemporary and ancient Hebrew samples. Seamless integration of the Google Vision API facilitates Optical Character Recognition (OCR) for image processing. The translation process initiates in English through the model, leading to a multilingual interface. This initiative represents a crucial step in preserving ancient languages in the digital age.
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Completing the docking between two multi-rotor unmanned aerial vehicles (UAVs) in the air has great significance for expanding the application scenarios of multiple UAVs and improving their endurance capability. In this regard, this paper first designs a safe and autonomous docking scheme and constructs a sub-mother UAV system.
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