Ocean University of China

About: Ocean University of China is a education organization based out in Qingdao, China. It is known for research contribution in the topics: Population & Sea surface temperature. The organization has 27604 authors who have published 27886 publications receiving 440181 citations. The organization is also known as: Zhōngguó Hǎiyáng Dàxué & OUC.

Counter electrodes from double-layered polyaniline nanostructures for dye-sensitized solar cell applications

Abstract: Counter electrodes from polypyrrole (PPy) and polyaniline (PANi) nanostructures were fabricated by chemical and electrochemical approaches for dye-sensitized solar cell (DSSC) applications. It was found that double-layered PANi consisting of a nanoparticle layer and a nanofiber layer was formed by an electrodeposition route, given significantly increased active sites and solar cell performances. The solar-to-electricity conversion efficiency of the double-layered PANi-based DSSC was 6.58% under 100 mW cm−2 (AM 1.5), which was much higher than those of PPy and chemically deposited PANi or Pt-based DSSCs. The enhancement in the conversion efficiency was due to the design of double-layered nanostructures, which resulted in an increased charge transfer kinetics and higher electrocatalytic activity for the I−/I3− redox reaction.

VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results

Abstract: Object detection is a hot topic with various applications in computer vision, e.g., image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of object detection benchmark datasets, including PASCAL VOC, ImageNet, and MS COCO. However, object detection on the drone platform is still a challenging task, due to various factors such as view point change, occlusion, and scales. To narrow the gap between current object detection performance and the real-world requirements, we organized the Vision Meets Drone (VisDrone2018) Object Detection in Image challenge in conjunction with the 15th European Conference on Computer Vision (ECCV 2018). Specifically, we release a large-scale drone-based dataset, including 8, 599 images (6, 471 for training, 548 for validation, and 1, 580 for testing) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. Featuring a diverse real-world scenarios, the dataset was collected using various drone models, in different scenarios (across 14 different cities spanned over thousands of kilometres), and under various weather and lighting conditions. We mainly focus on ten object categories in object detection, i.e., pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles (e.g., machineshop truck, forklift truck, and tanker) are ignored in evaluation. The dataset is extremely challenging due to various factors, including large scale and pose variations, occlusion, and clutter background. We present the evaluation protocol of the VisDrone-DET2018 challenge and the comparison results of 38 detectors on the released dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/. We expect the challenge to largely boost the research and development in object detection in images on drone platforms.