Artificial Intelligence

The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD) dataset for polycrystalline solar cell, which contains 36,543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly-free images and anomalous images with 10 different categories.

This data set contains nifty 50 companies and all their details of Open, Volume, Close, Adj Close, and name of the company.

“DCA-IoMT Dataset” belongs to the research article entitled “DCA-IoMT: Knowledge Graph Embedding-enhanced Deep Collaborative Alerts-recommendation against COVID19 (DOI: 10.1109/TII.2022.3159710)” accepted for publication in the Journal of IEEE Transactions on Industrial Informatics.

This dataset consists of 1878 labeled images of flowers from blackberry trees from the specie Rubus L. subgenus Rubus Watson. These are white flowers with five petals that blossom in the spring through summer. The images were collected using an Intel RealSense D435i camera inside a greenhouse.

This images were inicially collected to support a robotic autonomous pollination project.

The experiment is based on the open source RSRP data provided by Huawei Technologies Co., LTD. It measures RSRP of 415,244 signal receiving points in 180 dense urban communication cells. 

The dataset consists of images 

 We provided a new set of extreme low-light datasets of short exposure raw images in RGB format and long-exposure reference images of the same scenes.

This is the supplementary material for the paper  "A multimodal AI system for out-of-distribution generalization of seizure identification" in IEEE Journal of Biomedical and Health Informatics. 

QQP and Mechanical Field Q&A

A commonly used definition of spatial disorientation (SD) in aviation is "an erroneous sense of one’s position and motion relative to the plane of the earth’s surface". There exists a wide range of SD use-cases dictated by situational factors, therefore SD has been predominantly studied using reduced motion detection experimental contexts in isolation. The study of SD by use-case makes it difficult to understand general SD occurrence and thus provide viable solutions. To investigate SD in a generalized manner, a two-part Human Activity Recognition (HAR) study was performed.