Artificial Intelligence

This dataset includes the rotor geometrical parameters (*.csv) and motor parameters (*.csv) of interior permanent magnet synchronous motors. The rotor geometry covers three structures: 2D-, V-, and Nabla-structures. The motor parameters are generated by machine learning based on the finite element analysis results. The software JMAG Designer 19.1 was used for the finite element analysis.

 

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Dataset for paper "Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling"

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This dataset contains the test instances for the paper "A Matrix-cube-based Estimation of Distribution Algorithm for No-Wait Flow-Shop Scheduling with Sequence-Dependent Setup Times and Release Times".

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In this paper, we propose the first method to allow everyone to easily reconstruct their own 3D inner-body under clothing from a self-captured video with the mean reconstruction error of 0.73cm within 15s, avoiding privacy concerns arising from nudity or minimal clothing.

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This dataset consists of chest images of patients with different kinds of lung diseases.

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RIFIS is an image dataset that illustrates numerous aspects of rice field cultivation utilizing a walk-behind tractor. This dataset includes multiple movies, photos, and annotations. Moreover, location and orientation data are provided for the tractor during video and image recording.

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Dataset generated with Unreal Engine 4 and Nvidia NDDS. Contains 1500 images of each object: Forklift, pallet, shipping container, barrel, human, paper box, crate, and fence. These 1500 images are split into 500 images from each environment: HDRI and distractors, HDRI with no distractors, and a randomized environment with distractors.

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We provide ground truth images and moiré images in raw domain and sRGB domain respectively, which are placed in four folders gt_RAW_npz, gt_RGB, moire_RAW_npz and moire_RGB. The ground truth raw image is actually pseudo ground truth. The users can regenerate them by utilizing other RGB to raw inversing algorithms. Our raw domain data is stored in npz format, including black level corrected data, black level value, white level value and white balance value.

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The data is divided into a training set of 999 images and a test set of 335 images. The size of each 2D ultrasound image is 800 by 540 pixels with a pixel size ranging from 0.052 to 0.326 mm. The pixel size for each image can be found in the csv files: ‘training_set_pixel_size_and_HC.csv’ and ‘test_set_pixel_size.csv’. The training set also includes an image with the manual annotation of the head circumference for each HC, which was made by a trained sonographer.

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