Artificial Intelligence

This dataset containg 1900+ images divided into fresh oranges and rotten oranges. In an orange packing factory, a video was recorded, by placing the camera parallel and above the oranges conveyor. The video was captured for 10 minutes with a quality of Ultra High Definition (4K) with 60 frames per second and a High Dynamic Range feature. The video was changed from High Dynamic Range to Standard Dynamic Range by the use of Splice - Video Editor & Maker software. The video is inserted to developed algorithm operating video processing on it and creating the frames.

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In this project, an LSTM-based Model Predictive Controller (LSTM-MPC), with 200 neurons of each layer, is designed to have a highly efficient control on the temperature. The resulted dataset is attached, for further considerations.

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651 Views

TwitterVax is a collection of 9'068'389 italian tweets on vaccines tweeted from 1st January 2019 to 1st June 2022.

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Simulation data for the following paper

DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver (K. Chae and Y. Kim)

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To verify the proposed protection scheme, the simulation model of a six-terminal ring flexible DC distribution system is built in PSCAD/EMTDC , where the rated voltage of the DC line is ±10 kV . The fault inception is set at 0.6 s, the sampling frequency is 10 kHz and the protection data window length is 1 ms. The data set reflects the current and voltage values of each line after standardization

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Buildings are essential components of urban areas. While research on the extraction and 3D reconstruction of buildings is widely conducted, information on fine-grained roof types of buildings is usually ignored. This limits the potential of further analysis, e.g., in the context of urban planning applications. The fine-grained classification of building roof type from satellite images is a highly challenging task due to ambiguous visual features within the satellite imagery.

Last Updated On: 
Tue, 03/07/2023 - 11:58

This data contains training and testing data for single-shot deflectometry generated by the deformable mirror. The training data has total of 4000 data with single input composite pattern Ic and four outputs (Dx, Dy, Mx, and My).

The test data contains a pre-trained model, a script for testing, and test images

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576 Views

To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.

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This dataset is for robust sEMG-based intention recognition with respect to upper-limb positions.

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