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1. Research on the Thermal-Electric Coupling Behavior Under Compound AC-DC Voltage Within Saddle-Like Electric-Stress Dependence

Author

Duan Pan, Gao Bing, Chi Cheng, Yang Fan, and Luo Ping

Subjects
Compound ac???dc voltage, converter transformer, electric-stress dependence, finite element sub-model, electromagnetic-thermal-velocity coupling, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper proposes a nonlinear electromagnetic-thermal-velocity coupling model to study the insulation performance of converter transformer, in which the saddle-like electric-stress dependence is considered under compound alternating current-direct current (AC-DC) voltage situation. Firstly, the conductance of transformer oil and oil-immersed pressboard have been measured through constructed three-electrode experimental system. An interesting electric-stress characteristic, which is saddle-like electric-stress dependence is obtained and counted in the thermal-insulation analysis in the proposed model. Then, the high-order harmonics of compound AC-DC voltage have been considered in the built electromagnetic-thermal-velocity coupling model. Additionally, the impact of non-uniform temperature result has been mapped to the electric field domain through interpolation method, due to different meshes between the flow-thermal and electric fields in accordance with distinct computational characteristics. Finally, the performance of nonlinear thermal-electric coupling properties, including saddle-like electric-stress dependence, have been considered in the proposed coupling model. Results determine that saddle-like thermal-electric dependent properties would considerably change insulation capability, compared with results from the traditional method. Even intensity field of some sampling points could jump more than 1.6 kV/mm. Therefore, it is essential to evaluate the insulation design by practical saddle-like electric-dependent properties.

Published
2020
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6. The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

Author

Kristan, Matej, primary, Matas, Jiří, additional, Danelljan, Martin, additional, Felsberg, Michael, additional, Chang, Hyung Jin, additional, Čehovin Zajc, Luka, additional, Lukežič, Alan, additional, Drbohlav, Ondrej, additional, Zhang, Zhongqun, additional, Tran, Khanh-Tung, additional, Vu, Xuan-Son, additional, Björklund, Johanna, additional, Mayer, Christoph, additional, Zhang, Yushan, additional, Ke, Lei, additional, Zhao, Jie, additional, Fernández, Gustavo, additional, Al-Shakarji, Noor, additional, An, Dong, additional, Arens, Michael, additional, Becker, Stefan, additional, Bhat, Goutam, additional, Bullinger, Sebastian, additional, Chan, Antoni B., additional, Chang, Shijie, additional, Chen, Hanyuan, additional, Chen, Xin, additional, Chen, Yan, additional, Chen, Zhenyu, additional, Cheng, Yangming, additional, Cui, Yutao, additional, Deng, Chunyuan, additional, Dong, Jiahua, additional, Dunnhofer, Matteo, additional, Feng, Wei, additional, Fu, Jianlong, additional, Gao, Jie, additional, Han, Ruize, additional, Hao, Zeqi, additional, He, Jun-Yan, additional, He, Keji, additional, He, Zhenyu, additional, Hu, Xiantao, additional, Huang, Kaer, additional, Huang, Yuqing, additional, Jiang, Yi, additional, Kang, Ben, additional, Lan, Jin-Peng, additional, Lee, Hyungjun, additional, Li, Chenyang, additional, Li, Jiahao, additional, Li, Ning, additional, Li, Wangkai, additional, Li, Xiaodi, additional, Li, Xin, additional, Liu, Pengyu, additional, Liu, Yue, additional, Lu, Huchuan, additional, Luo, Bin, additional, Luo, Ping, additional, Ma, Yinchao, additional, Miao, Deshui, additional, Micheloni, Christian, additional, Palaniappan, Kannappan, additional, Park, Hancheol, additional, Paul, Matthieu, additional, Peng, HouWen, additional, Qian, Zekun, additional, Rahmon, Gani, additional, Scherer-Negenborn, Norbert, additional, Shao, Pengcheng, additional, Shin, Wooksu, additional, Kazemi, Elham Soltani, additional, Song, Tianhui, additional, Stiefelhagen, Rainer, additional, Sun, Rui, additional, Tang, Chuanming, additional, Tang, Zhangyong, additional, Toubal, Imad Eddine, additional, Valmadre, Jack, additional, van de Weijer, Joost, additional, Van Gool, Luc, additional, Vira, Jash, additional, Vujasinović, Stèphane, additional, Wan, Cheng, additional, Wan, Jia, additional, Wang, Dong, additional, Wang, Fei, additional, Wang, Feifan, additional, Wang, He, additional, Wang, Limin, additional, Wang, Song, additional, Wang, Yaowei, additional, Wang, Zhepeng, additional, Wu, Gangshan, additional, Wu, Jiannan, additional, Wu, Qiangqiang, additional, Wu, Xiaojun, additional, Xiao, Anqi, additional, Xie, Jinxia, additional, Xu, Chenlong, additional, Xu, Min, additional, Xu, Tianyang, additional, Xu, Yuanyou, additional, Yan, Bin, additional, Yang, Dawei, additional, Yang, Ming-Hsuan, additional, Yang, Tianyu, additional, Yang, Yi, additional, Yang, Zongxin, additional, Yin, Xuanwu, additional, Yu, Fisher, additional, Yu, Hongyuan, additional, Yu, Qianjin, additional, Yu, Weichen, additional, Yuan, YongSheng, additional, Yuan, Zehuan, additional, Zhang, Jianlin, additional, Zhang, Lu, additional, Zhang, Tianzhu, additional, Zhao, Guodongfang, additional, Zhao, Shaochuan, additional, Zheng, Yaozong, additional, Zhong, Bineng, additional, Zhu, Jiawen, additional, Zhu, Xuefeng, additional, Zhuang, Yueting, additional, Zong, ChengAo, additional, and Zuo, Kunlong, additional

Published
2023
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13. V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting

Author

Yu, Haibao, primary, Yang, Wenxian, additional, Ruan, Hongzhi, additional, Yang, Zhenwei, additional, Tang, Yingjuan, additional, Gao, Xu, additional, Hao, Xin, additional, Shi, Yifeng, additional, Pan, Yifeng, additional, Sun, Ning, additional, Song, Juan, additional, Yuan, Jirui, additional, Luo, Ping, additional, and Nie, Zaiqing, additional

Published
2023
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30. RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs

Author

Wang, Zhouxia, Zhang, Jiawei, Chen, Runjian, Wang, Wenping, and Luo, Ping

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

Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous Vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction, leading to superior restoration results. Third, RestoreFormer outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets, as well as produces images with better visual quality., Comment: Accepted by CVPR 2022

Published
2022

31. Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers

Author

Li, Zhiqi, Wang, Wenhai, Xie, Enze, Yu, Zhiding, Anandkumar, Anima, Alvarez, Jose M., Luo, Ping, and Lu, Tong

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

Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved post-processing method. We also use Deformable DETR to efficiently process multi-scale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2\% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO test-dev with 56.2\% PQ. It also shows stronger zero-shot robustness over existing methods. The code is released at \url{https://github.com/zhiqi-li/Panoptic-SegFormer}., Comment: Accepted to CVPR 2022

Published
2022

32. Bridging Video-text Retrieval with Multiple Choice Questions

Author

Ge, Yuying, Ge, Yixiao, Liu, Xihui, Li, Dian, Shan, Ying, Qie, Xiaohu, and Luo, Ping

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

Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder to interact video with texts, but results in low efficiency since each text-video pair needs to be fed into the model. In this work, we enable fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the "questions" constructed by the text features via resorting to the video features. Specifically, we exploit the rich semantics of text (i.e., nouns and verbs) to build questions, with which the video encoder can be trained to capture more regional content and temporal dynamics. In the form of questions and answers, the semantic associations between local video-text features can be properly established. BridgeFormer is able to be removed for downstream retrieval, rendering an efficient and flexible model with only two encoders. Our method outperforms state-of-the-art methods on the popular text-to-video retrieval task in five datasets with different experimental setups (i.e., zero-shot and fine-tune), including HowTo100M (one million videos). We further conduct zero-shot action recognition, which can be cast as video-to-text retrieval, and our approach also significantly surpasses its counterparts. As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e.g., action recognition with linear evaluation., Accepted by CVPR 2022

Published
2022

48. Optimal Dispatch Method of Park-level Integrated Energy Internet based on Average-Consensus ADMM Distributed Algorithm

Author

Lu Qiang, Luo Ping, Wang Yan, and Zhang Jiahao

Subjects
Scheme (programming language), Mathematical optimization, Temperature control, Computer science, business.industry, Energy consumption, Set (abstract data type), Distributed algorithm, The Internet, Convex function, business, computer, Energy (signal processing), computer.programming_language
Abstract

Park-level integrated energy internet (PEI), a multiple energy coupling and obvious zoning system, is set up to meet the growing demand of energy consumption and improve the comprehensive utilization efficiency of multiple energy resources. Traditional distributed alternating direction method of multipliers (ADMM) algorithm with a control center holding all information is often used to solve the dispatch model of PEI, which may cause security problems. In this article, we use the parallel ADMM algorithm to solve the problem in a distributed way. Meanwhile, a completely distributed scheme based on the average-consensus parallel ADMM algorithm is designed to eliminate the computing center of the subregion. The simulation results verified the validity of the proposed method.

Published
2021

49. A Fully Integrated Switched Capacitor Power Converter With Minimum Driving Loss Point Tracking Technique.

Author

Zheng, Xinyi and Luo, Ping

Abstract

A fully integrated switched capacitor power converter (SCPC) with minimum driving loss point tracking (MDLPT) technique is proposed in this brief. The driving voltage, switching frequency and power switch size are all taken into consideration, and are dynamically adjusted to the optimal value. As the result, the driving loss of SCPC is optimized in real time. Digital LDO is used to detect driving loss in the proposed technique, thereby avoiding complicated search algorithms or sampling circuits. The proposed SCPC is manufactured in $0.18\mathbf {\mathrm {\mu }}\text{m}$ CMOS process. The input voltage is 1.8V and the output voltage is 0.5V to 1.5V, the maximum load current is 15mA. Experimental results show that the efficiency of the proposed SCPC can reach 81.9%. The proposed method can reduce the driving loss by up to 27% at typical load point, resulting in a 7.8% increase in efficiency. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation.

Author

Pan, Xingang, Zhan, Xiaohang, Dai, Bo, Lin, Dahua, Loy, Chen Change, and Luo, Ping

Subjects
GENERATIVE adversarial networks, IMAGE reconstruction, IMAGE color analysis, PROBABILISTIC generative models, IMAGE processing
Abstract

Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there are still gaps toward an image prior that captures rich image semantics including color, spatial coherence, textures, and high-level concepts. This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. As shown in Fig. 1, the deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images. It also enables diverse image manipulation including random jittering, image morphing, and category transfer. Such highly flexible restoration and manipulation are made possible through relaxing the assumption of existing GAN inversion methods, which tend to fix the generator. Notably, we allow the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN. We show that these easy-to-implement and practical changes help preserve the reconstruction to remain in the manifold of nature images, and thus lead to more precise and faithful reconstruction for real images. Code is available at https://github.com/XingangPan/deep-generative-prior. [ABSTRACT FROM AUTHOR]

Published
2022
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