no code implementations • 21 Sep 2023 • Peiying Zhang, Nanxuan Zhao, Jing Liao
In this paper, we propose a novel pipeline that generates high-quality customized vector graphics based on textual prompts while preserving the properties and layer-wise information of a given exemplar SVG.
no code implementations • 8 Feb 2022 • Peiying Zhang, Chao Wang, Zeyu Qin, Haotong Cao
Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD.
no code implementations • 7 Feb 2022 • Peiying Zhang, Fanglin Liu, Chunxiao Jiang, Abderrahim Benslimane, Juan-Luis Gorricho, Joan Serrat-Fernacute
Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed.
no code implementations • 7 Feb 2022 • Peiying Zhang, Fanglin Liu, Gagangeet Singh Aujla, Sahil Vashist
A chaotic optimization strategy is introduced to replace the random sequence-guided crossover process to strengthen the global search capability and reduce the probability of producing invalid individuals.
no code implementations • 7 Feb 2022 • Peiying Zhang, Xue Pang, Neeraj Kumar, Gagangeet Singh Aujla, Haotong Cao
With the advent of the Internet of things (IoT) era, more and more devices are connected to the IoT.
no code implementations • 5 Feb 2022 • Peiying Zhang, Xingzhe Huang, Yaqi Wang, Chunxiao Jiang, Shuqing He, Haifeng Wang
Experimental results show that the matching of sentence similarity calculation method based on multi model nonlinear fusion is 84%, and the F1 value of the model is 75%.
no code implementations • 3 Feb 2022 • Shidong Zhang, Chao Wang, Junsan Zhang, Youxiang Duan, Xinhong You, Peiying Zhang
This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution.
no code implementations • 3 Feb 2022 • Peiying Zhang, Xue Pang, Yongjing Ni, Haipeng Yao, Xin Li
Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN).
no code implementations • 3 Feb 2022 • Peiying Zhang, Chao Wang, Chunxiao Jiang, Neeraj Kumar, Qinghua Lu
Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs.
no code implementations • 3 Feb 2022 • Peiying Zhang, Chao Wang, Gagangeet Singh Aujla, Neeraj Kumar, Mohsen Guizani
This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE).
no code implementations • 3 Feb 2022 • Peiying Zhang, Chao Wang, Chunxiao Jiang, Abderrahim Benslimane
In addition, as the use of intelligent learning algorithm to solve the problem of VNE has become a trend, this method is gradually outdated.
no code implementations • 3 Feb 2022 • Peiying Zhang, Chao Wang, Neeraj Kumar, Lei Liu
Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm.
no code implementations • 3 Feb 2022 • Peiying Zhang, Chao Wang, Neeraj Kumar, Weishan Zhang, Lei Liu
Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics.
no code implementations • 3 Feb 2022 • Chao Wang, Tao Dong, Youxiang Duan, Qifeng Sun, Peiying Zhang
Resource allocation in virtual network is essentially a problem of allocating underlying resources for virtual network requests (VNRs).
no code implementations • 3 Feb 2022 • Peiying Zhang, Chao Wang, Chunxiao Jiang, Zhu Han
Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment.
no code implementations • 7 Sep 2020 • Peiying Zhang, Chenhui Li, Changbo Wang
We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network.