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Institution

Wuhan University

EducationWuhan, China
About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Feature extraction. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors present a review of the recent developments in this field, and focus on two categories of PNIPAAm-based copolymer micelles as smart drug delivery systems.

635 citations

Proceedings ArticleDOI
Jian Ding1, Nan Xue1, Yang Long1, Gui-Song Xia1, Qikai Lu1 
01 Jun 2019
TL;DR: The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations.
Abstract: Object detection in aerial images is an active yet challenging task in computer vision because of the bird’s-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. In this paper, we propose a RoI Transformer to address these problems. The core idea of RoI Transformer is to apply spatial transformations on RoIs and learn the transformation parameters under the supervision of oriented bounding box (OBB) annotations. RoI Transformer is with lightweight and can be easily embedded into detectors for oriented object detection. Simply apply the RoI Transformer to light head RCNN has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer.

634 citations

Journal ArticleDOI
Shanfu Lu1, Jing Pan, Aibin Huang, Lin Zhuang, Juntao Lu 
TL;DR: In this article, a type of polymer electrolyte fuel cells (PEFC) employing a hydroxide ion-conductive polymer, quaternary ammonium polysulphone, as alkaline electrolyte and nonprecious metals, chromium-decorated nickel and silver, as the catalyst for the negative and positive electrodes, respectively.
Abstract: In recent decades, fuel cell technology has been undergoing revolutionary developments, with fundamental progress being the replacement of electrolyte solutions with polymer electrolytes, making the device more compact in size and higher in power density. Nowadays, acidic polymer electrolytes, typically Nafion, are widely used. Despite great success, fuel cells based on acidic polyelectrolyte still depend heavily on noble metal catalysts, predominantly platinum (Pt), thus increasing the cost and hampering the widespread application of fuel cells. Here, we report a type of polymer electrolyte fuel cells (PEFC) employing a hydroxide ion-conductive polymer, quaternary ammonium polysulphone, as alkaline electrolyte and nonprecious metals, chromium-decorated nickel and silver, as the catalyst for the negative and positive electrodes, respectively. In addition to the development of a high-performance alkaline polymer electrolyte particularly suitable for fuel cells, key progress has been achieved in catalyst tailoring: The surface electronic structure of nickel has been tuned to suppress selectively the surface oxidative passivation with retained activity toward hydrogen oxidation. This report of a H2–O2 PEFC completely free from noble metal catalysts in both the positive and negative electrodes represents an important advancement in the research and development of fuel cells.

634 citations

Journal ArticleDOI
Jiangfeng Qian1, Xianyong Wu1, Yuliang Cao1, Xinping Ai1, Hanxi Yang1 
TL;DR: An amorphous phosphorus/carbon (a-P/C) composite was synthesized using simple mechanical ball milling of red phosphorus and conductive carbon powders, showing great promise as a high capacity and high rate anode material for sodium ion batteries.
Abstract: Turning on your P/C: An amorphous phosphorus/carbon (a-P/C) composite was synthesized using simple mechanical ball milling of red phosphorus and conductive carbon powders. This material gave an extraordinarily high sodium ion storage capacity of 1764 mA h g(-1) (see graph) with a very high rate capability, showing great promise as a high capacity and high rate anode material for sodium ion batteries.

633 citations

Journal ArticleDOI
TL;DR: Li et al. as discussed by the authors proposed a COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net) to automatically identify infected regions from chest CT slices, where a parallel partial decoder is used to aggregate the high-level features and generate a global map.
Abstract: Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network ( Inf-Net ) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net , a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.

633 citations


Authors

Showing all 93441 results

NameH-indexPapersCitations
Jing Wang1844046202769
Jiaguo Yu178730113300
Lei Jiang1702244135205
Gang Chen1673372149819
Omar M. Yaghi165459163918
Xiang Zhang1541733117576
Yi Yang143245692268
Thomas P. Russell141101280055
Jun Chen136185677368
Lei Zhang135224099365
Chuan He13058466438
Han Zhang13097058863
Lei Zhang130231286950
Zhen Li127171271351
Chao Zhang127311984711
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023286
20221,139
20219,716
20209,672
20197,977
20186,629