Zobrazeno 1 - 10
of 3 282
pro vyhledávání: '"ZHANG Yanfeng"'
Publikováno v:
Open Physics, Vol 21, Iss 1, Pp 500-9 (2023)
The heat transfer process of the sweeping jet and film composite cooling (SJF) structure of the turbine blade leading edge is complex. The current work divides the overall cooling effectiveness of the SJF into the impingement heat transfer effectiven
Externí odkaz:
https://doaj.org/article/cba7130caf944b24ab4a333f2470f6ea
Autor:
GUO Weizhou, SHEN Bin, WANG Yang, LIU Yongli, ZHANG Yanfeng, WANG Haitao, LI Yuan, WANG Zhensuo
Publikováno v:
Meikuang Anquan, Vol 53, Iss 9, Pp 233-238 (2022)
Aiming at the problems of poor real-time ventilation monitoring, lack of remote regulation of ventilation facilities, complex and inefficient air volume regulation process in Hulusu Coal Mine, in order to realize the controllability, visualization an
Externí odkaz:
https://doaj.org/article/bb819a0e4bfc4624aa854ad90049d2fb
On-chip nonlinear photonic conversion functions with wide and precise tunability as well as high conversion efficiency are highly desirable for a wide range of applications. Photonic crystal micro-ring resonators (PhCR) facilitate efficient nonlinear
Externí odkaz:
http://arxiv.org/abs/2411.08285
Autor:
Yu, Song, Gong, Shufeng, Tao, Qian, Shen, Sijie, Zhang, Yanfeng, Yu, Wenyuan, Liu, Pengxi, Zhang, Zhixin, Li, Hongfu, Luo, Xiaojian, Yu, Ge, Zhou, Jingren
The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, existing dynamic graph storage systems suffer from read or write amplif
Externí odkaz:
http://arxiv.org/abs/2411.06392
Autor:
Zhou, Yijie, Gong, Shufeng, Yao, Feng, Chen, Hanzhang, Yu, Song, Liu, Pengxi, Zhang, Yanfeng, Yu, Ge, Yu, Jeffrey Xu
Enhancing the efficiency of iterative computation on graphs has garnered considerable attention in both industry and academia. Nonetheless, the majority of efforts focus on expediting iterative computation by minimizing the running time per iteration
Externí odkaz:
http://arxiv.org/abs/2407.14544
Autor:
Chen, Chaoyi, Gao, Dechao, Zhang, Yanfeng, Wang, Qiange, Fu, Zhenbo, Zhang, Xuecang, Zhu, Junhua, Gu, Yu, Yu, Ge
Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world
Externí odkaz:
http://arxiv.org/abs/2312.02473
Autor:
Li Bowen, Zu Shuai, Zhang Zhepeng, Zheng Liheng, Jiang Qiao, Du Bowen, Luo Yang, Gong Yongji, Zhang Yanfeng, Lin Feng, Shen Bo, Zhu Xing, Ajayan Pulickel M., Fang Zheyu
Publikováno v:
Opto-Electronic Advances, Vol 2, Iss 5, Pp 190008-1-190008-9 (2019)
Manipulation of light-matter interaction is critical in modern physics, especially in the strong coupling regime, where the generated half-light, half-matter bosonic quasiparticles as polaritons are important for fundamental quantum science and appli
Externí odkaz:
https://doaj.org/article/de1078c8730049aaac3f1a99f31591b6
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training. Since GNNs embody complex data dependencies between training samples, the training of GNNs should address distinct challenges different from DNN
Externí odkaz:
http://arxiv.org/abs/2311.13279
Graph Neural Networks (GNNs) have demonstrated outstanding performance in various applications. Existing frameworks utilize CPU-GPU heterogeneous environments to train GNN models and integrate mini-batch and sampling techniques to overcome the GPU me
Externí odkaz:
http://arxiv.org/abs/2311.13225
This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a transaction as a service (TaaS) layer, the transaction processing can be
Externí odkaz:
http://arxiv.org/abs/2311.07874