Wuhan University

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.

Distribution System Restoration With Microgrids Using Spanning Tree Search

Abstract: Distribution system restoration (DSR) is aimed at restoring loads after a fault by altering the topological structure of the distribution network while meeting electrical and operational constraints. The emerging microgrids embedded in distribution systems enhance the self-healing capability and allow distribution systems to recover faster in the event of an outage. This paper presents a graph-theoretic DSR strategy incorporating microgrids that maximizes the restored load and minimizes the number of switching operations. Spanning tree search algorithms are applied to find the candidate restoration strategies by modeling microgrids as virtual feeders and representing the distribution system as a spanning tree. Unbalanced three-phase power flow is performed to ensure that the proposed system topology satisfies all operational constraints. Simulation results based on a modified IEEE 37-node system and a 1069-node distribution system demonstrate the effectiveness of the proposed approach.

Edge-Enhanced GAN for Remote Sensing Image Superresolution

Abstract: The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set , Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

Functional hyperbranched polymers with advanced optical, electrical and magnetic properties.

Abstract: As one kind of important functional material, those with advanced optical, electrical and magnetic characteristics have attracted increasing attention due to their essential and irreplaceable role in the daily life of humans. In particular, optical, electrical and magnetic hyperbranched polymers (HBPs) exhibit some unique properties, partially derived from their highly branched topological structures. This review summarizes the recent progress in the field of functional HBPs and their application in optics, electronics and magnetics, including light-emitting polymers, nonlinear optical materials, chemosensors, solar cells, magnetic materials, etc., and also gives some outlooks for further exploration in this field at the end of this paper.