Artificial Intelligence
The Partial Discharge - Localisation Dataset, abbreviated: PD-Loc Dataset is an extensive collection of acoustic data specifically curated for the advancement of Partial Discharge (PD) localisation techniques within electrical machinery. Developed using a precision-engineered 32-sensor acoustic array, this dataset encompasses a wide array of signals, including chirps, white Gaussian noise, and PD signals.
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Problems of neurodegenerative disorder patients can be solved by developing Brain-Computer Interface (BCI) based solutions. This requires datasets relevant to the languages spoken by patients. For example, Marathi, a prominent language spoken by over 83 million people in India, lacks BCI datasets based on the language for research purposes. To tackle this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of selected common Marathi words.
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This data set consists of 16 half-hour EEG recordings, obtained from 10 volunteers, as described below.
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The datasets comprise configurations of loading and unloading plans for container ships, generated under six distinct scenarios based on varying numbers of stacks and maximum stack heights of containers in each row. Considering typical container ship characteristics, scenarios encompass stack numbers ranging from 5 to 30 and maximum stack heights from 4 to 10. The dataset includes loading and unloading plans for dockyard containers, with sample plans provided for small ships. Each of the 5 datasets comprises 20 instances representing different container loading and unloading scenarios.
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173 Views
In today's world of online communication and digital media, hate speech has become an alarming problem worldwide. With the advancement of the internet, while people enjoy numerous benefits, there's also a dark side where individuals are subjected to horrendous bullying through hate speech. Tragically, some instances even lead to extreme actions like suicide or self-destructive behavior.
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428 Views
The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including Autism Spectrum Disorder (ASD) - a neurodevelopmental condition that impacts language, communication, and social interactions. However, privacy issues and a lack of data complicate Artificial Intelligence applications in this domain. Synthetic ERG signals generated from real ERG recordings should carry similar information and could be used as an extension for natural data.
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254 Views
With the goal of improving machine learning approaches in inverse scattering, we provide an experimental data set collected with a 2D near-field microwave imaging system. Machine learning approaches often train solely on synthetic data, and one of the reasons for this is that no experimentally-derived public data set exists. The imaging system consists of 24 antennas surrounding the imaging region, connected via a switch to a vector network analyzer. The data set contains over 1000 full Scattering parameter scans of five targets at numerous positions from 3-5 GHz.
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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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