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1,151 results on '"Wang, Deming"'

156. NDNet: Spacewise Multiscale Representation Learning via Neighbor Decoupling for Real-Time Driving Scene Parsing

Author

Li, Shu, Yan, Qingqing, Zhou, Xun, Wang, Deming, Liu, Chengju, and Chen, Qijun

Abstract

As a safety-critical application, autonomous driving requires high-quality semantic segmentation and real-time performance for deployment. Existing method commonly suffers from information loss and massive computational burden due to high-resolution input-output and multiscale learning scheme, which runs counter to the real-time requirements. In contrast to channelwise information modeling commonly adopted by modern networks, in this article, we propose a novel real-time driving scene parsing framework named NDNet from a novel perspective of spacewise neighbor decoupling (ND) and neighbor coupling (NC). We first define and implement the reversible operations called ND and NC, which realize lossless resolution conversion for complementary thumbnails sampling and collation to facilitate spatial modeling. Based on ND and NC, we further propose three modules, namely, local capturer and global dependence builder (LCGB), spacewise multiscale feature extractor (SMFE), and high-resolution semantic generator (HSG), which form the whole pipeline of NDNet. The LCGB serves as a stem block to preprocess the large-scale input for fast but lossless resolution reduction and extract initial features with global context. Then the SMFE is used for dense feature extraction and can obtain rich multiscale features in spatial dimension with less computational overhead. As for high-resolution semantic output, the HSG is designed for fast resolution reconstruction and adaptive semantic confusion amending. Experiments show the superiority of the proposed method. NDNet achieves the state-of-the-art performance on the Cityscapes dataset which reports 76.47% mIoU at 240+ frames/s and 78.8% mIoU at 150+ frames/s on the benchmark. Codes are available at https://github.com/LiShuTJ/NDNet.

Published
2024
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157. A Communication-Assisted Distance Protection for AC Microgrids considering the Fault-Ride-Through Requirements of Distributed Generators.

Author

Wang, Deming, Li, Fei, and Li, Yingliang

Subjects
*MICROGRIDS, *DISTRIBUTED power generation, *OVERCURRENT protection, *MARKETING channels, *DESIGN protection, *PROBLEM solving
Abstract

The conventional overcurrent protection is ineffective in isolating faults in microgrids due to the low fault current levels contributed by inverter-interfaced distributed generations (IIDGs). To extend the microgrid protection methods, the distance protection is considered as a common alternative to detect faults. However, the fault-ride-through (FRT) requirements and the high impedance fault (HIF) detection challenge the application of the conventional distance protection. To solve the two problems, a new inverse-time distance (ITD) protection for medium-voltage AC microgrids was proposed in this paper. The primary ITD protection was designed to meet the FRT requirements, and the backup protection was coordinated to isolate faults. The fault resistance and distributed generation infeed effect on the measured impedance were mitigated by a communication-assisted method proposed in this paper. Owing to the shorter communication channel time in distribution feeders and lower communication investments without a synchronous clock, the proposed method can be used to improve the ITD protection performance. An 11 kV AC microgrid was simulated with different fault types in the islanded mode and grid-connected mode. Time domain simulation verified the effectiveness of the proposed ITD protection. [ABSTRACT FROM AUTHOR]

Published
2023
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162. Plasma Cell-Free DNA After Embolization

Author

Sun, Yi, primary, Cai, Ren, additional, Wang, Zhenfeng, additional, Wang, Deming, additional, Zhao, Xiong, additional, Yue, Xiaojie, additional, Gu, Hao, additional, Shi, Hao, additional, Liu, Yun, additional, Fan, Xindong, additional, and Su, Lixin, additional

Published
2023
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186. GeoPose: Dense Reconstruction Guided 6D Object Pose Estimation With Geometric Consistency

Author

Chen Qijun, Huiyi Chen, Zhou Guangliang, Wang Deming, and Yan Yi

Subjects
Pixel, business.industry, Computer science, Pipeline (computing), Normalization (image processing), RANSAC, Object (computer science), Computer Science Applications, Image (mathematics), Signal Processing, Media Technology, RGB color model, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, Pose
Abstract

6D object pose estimation for texture-less objects from RGB images remains challenging, especially in occlusion scenarios. Instead of localizing sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion, we introduce GeoPose, a novel reconstruction guided pose estimation pipeline that predicts dense correspondences and leverages geometric consistency effectively. We first design a dense reconstruction network (ReconNet) to reconstruct pixel-wise object coordinates in normalization space. Dense 2D-3D correspondences are generated intuitively by our explicit pa-rameterization for 3D object models, which dismisses keypoint selection efforts. These 2D-3D correspondences are then utilized to estimate 6D poses by the PnP algorithm with RANSAC iterations. Furthermore, a novel Cycle Loss is proposed to provide 3D prior supervision, which significantly correlates with the pose estimation task by guiding geometric consistency between reconstruction (pixel to 3D) and reprojection (3D to pixel). In addition, a training data augmentation method is proposed to handle the insufficiency of 6D datasets, the acquisition of which is error-prone and time-consuming. Extensive experiments demonstrate that, compared with existing RGB-based methods, our GeoPose can achieve state-of-the-art (SOTA) 6D pose estimation performance on the LINEMOD, Occlusion LINEMOD and T-LESS datasets.

Published
2022
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197. Dermatoscopic features differentiating among port wine stain, arteriovenous malformation, and capillary malformation-arteriovenous malformation syndrome: to detect potential fast-flow vascular malformations at an early stage

Author

Sun, Yi, primary, Su, Lixin, additional, Zhang, Yunjie, additional, Wang, Zhenfeng, additional, Chen, Shijie, additional, Gu, Hao, additional, Yue, Xiaojie, additional, Zhao, Xiong, additional, Yang, Xitao, additional, Wang, Deming, additional, Fan, Xindong, additional, and Cai, Ren, additional

Published
2022
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