Artificial Intelligence
The data used in this work is collected using the AirBox Sense system developed to detect six air pollutants, ambient temperature, and ambient relative humidity. The pollutants are Nitrogen Dioxide (NO2), surface Ozone (O3), Carbon Monoxide (CO), Sulphur Dioxide (SO2), Particulate Matter (PM2.5, and PM10). The sensors monitor these pollutants in real-time and store them in a cloud-based platform using a cellular module. Data are collected every 20 seconds, producing 4320 readings each day.
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Scene understanding is essential for a wide range of robotic tasks, such as grasping. Simplifying the scene into predefined forms makes the robot perform the robotic task more properly, especially in an unknown environment. This paper proposes a combination of simulation-based and realworld datasets for domain adaptation purposes and grasping in practical settings. In order to compensate for the weakness of depth images in previous studies reported in the literature for clearly representing boundaries, the RGB image has also been fed as input in RGB and RGB-D input modalities.
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This work presents a specialized dataset designed to advance autonomous navigation in hiking trail and off-road natural environments. The dataset comprises over 1,250 images (640x360 pixels) captured using a camera mounted on a tele-operated robot on hiking trails. Images are manually labeled into eight terrain classes: grass, rock, trail, root, structure, tree trunk, vegetation, and rough trail. The dataset is provided in its original form without augmentations or resizing, allowing end-users flexibility in preprocessing.
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This dataset, titled "Synthetic Sand Boil Dataset for Levee Monitoring: Generated Using DreamBooth Diffusion Models," provides a comprehensive collection of synthetic images designed to facilitate the study and development of semantic segmentation models for sand boil detection in levee systems. Sand boils, a critical factor in levee integrity, pose significant risks during floods, necessitating accurate and efficient monitoring solutions.
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The UNISTUDIUM dataset contains the logs collected by Unistudium, the University of Perugia elearning platform based on moodle, a open source software for learning management systems (https://moodle.org).
The collected logs record interactions with the platform of students attending 4 courses during the time period of one semester, from 1st September to 31st December.
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We organized and collected two years' worth of complete fault work orders from a wind farm, and structured these work orders into a fault diagnosis event knowledge graph using the proposed algorithm. This graph includes fault modes, fault impacts, fault symptoms, inspection schemes, root cause identification, and maintenance strategies, covering all potential fault information and handling methods for wind turbines. This dataset records the head entity-relation-tail entity information in the form of triples using JSON format.
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750 Views
Surface electromyography (EMG) can be used to interact with and control robotic systems via intent recognition. However, most machine learning algorithms used to decode EMG signals have been trained on relatively small datasets with limited subjects, which can affect their widespread generalization across different users and activities. Motivated by these limitations, we developed EMGNet - a large-scale dataset to support research and development in EMG neural decoding, with an emphasis on human locomotion.
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To solve the problem of accurate recognition and picking of tea by tea picking robot, this study proposes a S-YOLOv10-SIC algorithm that integrates slice-assisted hyper-inference algorithm. This algorithm enhances the YOLOv10 network by introducing Space-to-Depth Convolution, asymptotic feature pyramid network, and Inner-IoU. These improvements reduce the loss of detailed information in long-distance and low-resolution images, improve key layer saliency, optimize non-adjacent layer fusion, enhance model convergence speed, and increase model universality.
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We have selected the ImageNet validation set and the Flower dataset as benchmark standards for the image classification domain. These datasets provide a robust and diverse set of images, ensuring a comprehensive evaluation of model performance. For benchmark testing in the object detection domain, we utilize the COCO2012 validation set and the Road Voc dataset. These datasets are well-suited for assessing the accuracy and efficiency of object detection models in various real-world scenarios.
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This dataset is the dataset used in article 'A Multi-tropical Cyclone Trajectory Prediction Method Based on a Density Map with Memory and Data Fusion' by Dongfang Ma, Zhaoyang Ma, Chengying Wu and Jianmin Lin. The authors are with the Institute of Marine Sensing and Networking, Ocean College, Zhejiang University, Zhoushan 316021, China. This dataset contains satellite images, density maps of TC locations and geopotential height maps.
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