Artificial Intelligence
To train the machine learning model, a dataset was generated containing data for «Budennovskoye» field, part of which is shown in title figure. (AR and SP are given for 90 centimeter intervals, for which, in turn, the actual values K_fpo. obtained by pumping out (pump out) was determined. As a result, the input variable set consisted of 19 values, including the rock code (AR, SP). The target column isK_f_pump_out .
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Data used for evaluation of the Chameleon system. We use it to evaluate capabilities of sensor fusion system that is able to adapot to multiple envrionemnts and monitor activity states within a room. The data set is divided by the two deployments and includes inforamtion for both of the sensors used to test the system. We include two weeks worth of data along with training and testing accuracy results.
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The dataset is pre-processed from two datasets: UCI credit card dataset (https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients) and ULB credit card dataset (https://www.kaggle.com/mlg-ulb/creditcardfraud).
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Surveillance video captured by Multi-intensity infrared illuminator.
GT(ground-truths) :bounding boxes of 'person' in channel 2,4 and 6 by following the Pascal VOC format.
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This dataset presents a collection of coordinates that belongs to paths generated with a 3D disjstkra algorithm,in diferents enviroments,with a grid size equal to one. The output is a six dimension vector that represents the action taken by the agent (z+,z-,y+,y-,x+,x-) based on his pose, sensors readings and the target.
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The iSAID-Reduce100 is a reduced version of the DOTA dataset for instance segmentation task, including 1400 training samples and 1362 validation samples. The images are captured from multiple sensors and cropped to (512, 512).
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The dataset consists of training and test data and label matrices for single-pixel compressive DoA estimation for mmWave metasurface. The dataset will be uploaded soon. Currently, a small part of the dataset can be accessed through this repository. For detailed information, please visit Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation through https://arxiv.org/abs/2109.05466
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In our study, datasets of two simulators, namely phasor-based simulator and hybrid-type simulator are used. In the hybrid environment, first, the outputs of the phasor-based simulator are converted to instantaneous waveforms, then based on instruction, distortions and noises are added (superimposed) to these waveforms, and finally, the distorted waveforms are fed to the detailed model of PMUs simulated in EMT domain. Outputs of both simulators can be found in the submitted file.
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