Artificial Intelligence

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.

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.

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.

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.