Zobrazeno 1 - 10
of 287
pro vyhledávání: '"ZHANG Rufeng"'
Publikováno v:
Tongxin xuebao, Vol 45, Pp 51-61 (2024)
Aiming at the time-sensitive network (TSN) gating scheduling required high synchronization accuracy of adjacent nodes, the delay fluctuation of inter-satellite wireless links caused the deviation of gate opening of adjacent nodes, resulting in the pr
Externí odkaz:
https://doaj.org/article/cb0b503648ab47f6854ba9798f960e7f
Publikováno v:
IROS 2022 and IEEE Robotics and Automation Letters (RA-L), 2022
It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this paper, we focus on the setting of furniture assembly from a complete
Externí odkaz:
http://arxiv.org/abs/2207.01779
Autor:
Li, Xue, Shao, Junyan, Jiang, Tao, Chen, Houhe, Zhou, Yue, Zhang, Rufeng, Jia, Hongjie, Wu, Jianzhong
Publikováno v:
In Applied Energy 15 November 2024 374
Publikováno v:
In Applied Energy 15 November 2024 374
Publikováno v:
In Cellular Signalling October 2024 122
Publikováno v:
In Applied Energy 1 October 2024 371
We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin
Externí odkaz:
http://arxiv.org/abs/2110.07402
Publikováno v:
In Applied Energy 15 April 2024 360
Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that has made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either
Externí odkaz:
http://arxiv.org/abs/2106.15947
Autor:
Sun, Peize, Cao, Jinkun, Jiang, Yi, Zhang, Rufeng, Xie, Enze, Yuan, Zehuan, Wang, Changhu, Luo, Ping
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the
Externí odkaz:
http://arxiv.org/abs/2012.15460