Artificial Intelligence
The dataset contains 66376 image data of 124 pedestrians, each image contains two pedestrians. Each pedestrian has 6 sets of gait sequences (NM#01-NM#06). Each set of images is taken with the pedestrian's walking route parallel to the camera's perspective. The pedestrian is not carrying anything while walking. The gait sequences of each group of pedestrians are divided into training sets and test sets.The dataset consists of two parts, an image file and an XML file. The image folder contains all the image data, and the label folder contains all the label files.
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To make it possible for the model to distinguish the connection between requirements and the software architecture pattern during training using GAI, the expected response for a specific requirement was labeled with a software architecture pattern with the prefix “Software architecture pattern: ” and its explanation with the prefix “Explanation: ”.
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Zip includes commented Jupyter notebook with associated data files. Abstract for paper is:
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Zip includes commented Jupyter notebook with associated data files. Abstract for paper is:
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The dataset contains ttwo columns, the first column represent the 'Time' data , while the second column represents the 'Current' data's. These figures are from a simulation of the 750 VDC traction system. These figures also show the train's high beginning current and used to study the DC traction system.
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The dataset Provides S-parameter measurements of an AI enhanced wireless power transfer system using two ISO/ICE 14443-1 Coils with series capacitance compensation at 13.56 MHz under three configurations of vertical and horizontal misalignment, inter-coil distance, and azimuthal tilt. The structure and components of the system is shown in the Image attached to the Dataset This measured data validates the implementation of the system at the three coil configurations discussed in the publication.
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Wearable devices, such as data gloves and electronic skins, can perceive human hand's actions, behaviors and even emotions with the help of knowledge learning and inference. Curvature or magnetism sensing in such devices often lacks comprehensive gesture interactive information, meanwhile, the limited computing power of wearable applications restricts the multi-mode fusion of different sensing data and the deployment of deep learning networks.
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We have obtained data from May 2022 to October 2023 for our suggested framework modelling. This set of data incorporates seasonality-related speech, which we convert into text, Facebook, and Twitter posts. On the whole, 4646 data elements have been acquired, comprising 3716 representing affected individuals and the remainder of 930 representing unaffected individuals, which generated a proportional 4:1 ratio.
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The existing datasets lack the diversity required to train the model so that it performs equally well in real fields under varying environmental conditions. To address this limitation, we propose to collect a small number of in-field data and use the GAN to generate synthetic data for training the deep learning network. To demonstrate the proposed method, a maize dataset 'IIITDMJ_Maize' was collected using a drone camera under different weather conditions, including both sunny and cloudy days. The recorded video was processed to sample image frames that were later resized to 224 x 224.
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