The Baseline set described in the IEEE article (https://ieeexplore.ieee.org/document/10077565) as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
One to three columns of data are X, Y, Z coordinates respectively
One to three columns of data are X, Y and Z coordinates respectively.
Mindlin Plate data used for training DNN surrogate model for Uncertainty Quantification.
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Vision is important for transitions between different locomotor controllers (e.g., level-ground walking to stair ascent) by sensing the environment prior to physical interactions. Here we developed StairNet to support the development and comparison of deep learning models for visual recognition of stairs. The dataset builds on ExoNet – the largest open-source dataset of egocentric images of real-world walking environments.
In blast furnace iron-making process (BFIP), there is a significant push to maintain a stable iron-making process and ensure process at maximum efficiency. While some control systems can compensate for multiple types of disturbances when faults occur, some significant process faults often require precise human intervention to avoid safety hazards. Therefore, it is crucial to develop an efficient and stable diagnostic system to efficiently identify these faults so that operators can deal with them quickly.
Retail Gaze, a dataset for remote gaze estimation in real-world retail environments. Retail Gaze is composed of 3,922 images of individuals looking at products in a retail environment, with 12 camera capture angles.
Each image captures the third-person view of the customer and shelves. Location of the gaze point, the Bounding box of the person's head, segmentation masks of the gazed at product areas are provided as annotations.
TDC560 dataset contains 560 difficult images, one of which are selected from the testing set of CTW1500 and TD500, others are generated by ourselves with text-line annotations. In the selecting process, we sort images with the extreme spatial distances between characters and words.
The unfolding of the COVID-19 outbreak was an unprecedented and unanticipated opportunity to understand how a sudden global shock modulates people’s online searches when seeking information about their emotional well-being. Furthermore, it also illustrated how public health surveillance systems were essential for tracking diseases’ spatial and temporal dynamics and shaping rapid public policy changes.
