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117 results on '"Liu, Wentao"'

19. Advanced Power Synchronization Control of Modular Multilevel Converter in Stiff Grid

Author

Liu, Wentao, Teodorescu, Remus, Kerekes, Tamas, Dragicevic, Tomislav, Liu, Wentao, Teodorescu, Remus, Kerekes, Tamas, and Dragicevic, Tomislav

Abstract

Aiming at the instability problem of traditional power synchronization control (PSC) in a stiff grid, an advanced PSC based on modular multilevel converter (MMC) is proposed in this paper, which can effectively improve converter stability in high short circuit ratio (SCR) scenarios. The key point of the proposed PSC is to eliminate the impact of the arm inductor voltage and capacitor voltage ripple on the converter output voltage, which means a generalized decoupling process in the PSC controller. Based on the decoupling correction, MMC can be treated as a controlled voltage source regardless of the internal impedance circuit and the power oscillation between MMC and the stiff grid can be suppressed effectively. The control strategy is verified and compared with traditional PSC in time simulations.

Published
2022

25. Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images

Author

Wu, Size, Jin, Sheng, Liu, Wentao, Bai, Lei, Qian, Chen, Liu, Dong, and Ouyang, Wanli

Subjects
FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper studies the task of estimating the 3D human poses of multiple persons from multiple calibrated camera views. Following the top-down paradigm, we decompose the task into two stages, i.e. person localization and pose estimation. Both stages are processed in coarse-to-fine manners. And we propose three task-specific graph neural networks for effective message passing. For 3D person localization, we first use Multi-view Matching Graph Module (MMG) to learn the cross-view association and recover coarse human proposals. The Center Refinement Graph Module (CRG) further refines the results via flexible point-based prediction. For 3D pose estimation, the Pose Regression Graph Module (PRG) learns both the multi-view geometry and structural relations between human joints. Our approach achieves state-of-the-art performance on CMU Panoptic and Shelf datasets with significantly lower computation complexity., Comment: Accepted by ICCV'2021

Published
2021
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37. Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data

Author

Gao, Rongrong, Fan, Na, Li, Changlin, Liu, Wentao, Chen, Qifeng, Gao, Rongrong, Fan, Na, Li, Changlin, Liu, Wentao, and Chen, Qifeng

Abstract

We present a novel approach to joint depth and normal estimation for time-of-flight (ToF) sensors. Our model learns to predict the high-quality depth and normal maps jointly from ToF raw sensor data. To achieve this, we meticulously constructed the first large-scale dataset (named ToF-100) with paired raw ToF data and ground-truth high-resolution depth maps provided by an industrial depth camera. In addition, we also design a simple but effective framework for joint depth and normal estimation, applying a robust Chamfer loss via jittering to improve the performance of our model. Our experiments demonstrate that our proposed method can efficiently reconstruct high-resolution depth and normal maps and significantly outperforms state-of-the-art approaches. © 2021 IEEE.

Published
2021

42. Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation.

Author

Liu, Wentao, Yang, Huihua, Tian, Tong, Cao, Zhiwei, Pan, Xipeng, Xu, Weijin, Jin, Yang, and Gao, Feng

Subjects
RETINAL blood vessels, DEEP learning, CORONARY arteries, SURGICAL diagnosis, CORONARY angiography, DIAGNOSTIC imaging
Abstract

Vessel segmentation is critical for disease diagnosis and surgical planning. Recently, the vessel segmentation method based on deep learning has achieved outstanding performance. However, vessel segmentation remains challenging due to thin vessels with low contrast that easily lose spatial information in the traditional U-shaped segmentation network. To alleviate this problem, we propose a novel and straightforward full-resolution network (FR-UNet) that expands horizontally and vertically through a multiresolution convolution interactive mechanism while retaining full image resolution. In FR-UNet, the feature aggregation module integrates multiscale feature maps from adjacent stages to supplement high-level contextual information. The modified residual blocks continuously learn multiresolution representations to obtain a pixel-level accuracy prediction map. Moreover, we propose the dual-threshold iterative algorithm (DTI) to extract weak vessel pixels for improving vessel connectivity. The proposed method was evaluated on retinal vessel datasets (DRIVE, CHASE_DB1, and STARE) and coronary angiography datasets (DCA1 and CHUAC). The results demonstrate that FR-UNet outperforms state-of-the-art methods by achieving the highest Sen, AUC, F1, and IOU on most of the above-mentioned datasets with fewer parameters, and that DTI enhances vessel connectivity while greatly improving sensitivity. The code is available at: https://github.com/lseventeen/FR-UNet. [ABSTRACT FROM AUTHOR]

Published
2022
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