VLDB 2026 Research / reviewers in the wild / expert
Fei Tan 0001
dblp:80/8352-1
· DBLP profile ↗
14ranked-venue papers
5as first author
12since 2021 · last 2026
0000-0001-7057-581XORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 11 · 3 first-author · 9 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 2 first-author · 3 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A chaotic-system-based parallel image encryption algorithm with orthogonal arrays supporting thumbnail decryption
Qilin Chen, Fei Tan 0001, Changxin Wu |
Expert Syst. Appl. | 3 |
| 2025 | K-core percolation and node protection strategy for edge-coupling partially interdependent networks
Haibin Liao, Fei Tan 0001, Zeliang Chen |
Expert Syst. Appl. | 3 |
| 2025 | Multi-image encryption based on new two-dimensional hyperchaotic model via cyclic shift coding of deoxyribonucleic acid
Zeqin Lin, Fei Tan 0001 |
Expert Syst. Appl. | 3 |
| 2025 | Fixed and Predefined-Time Control to Synchronize DMDNNs With Unknown Nonlinearity Parameters Under Adaptive IdentificationabstractAdaptive fixed/predefined-time control to synchronize discontinuous multiple delay neural networks (DMDNNs) under unknown parameters identification is studied in this paper. To overcome the influence under uncertain factors in the DMDNNs, adaptive control technology is introduced into the design of controllers. In order to identify unknown parameters for state dependent switching in the DMDNNs within a fixed/predefined settling time, a fixed/predefined time identification method is derived. Under the premise that fixed time synchronization (FTS) is implemented in DMDNNs, adaptive fixed time identifier and controller are redesigned to achieve synchronization and identify unknown parameters in the DMDNNs within a predefined settling time. By the Lyapunov stability method, the corresponding synchronization criteria under FTS and predefined-time synchronization (PTS) are derived. FTS/PTS and identification of parameters under DMDNNs have potential applications in secure communication, pattern recognition, and other fields. Fei Tan 0001, Quanxin Zhu |
IEEE Trans Autom. Sci. Eng. | 1 |
| 2025 | Fixed Time Control for Interlayer Synchronism Under DDMNNs and Application in Secure CommunicationabstractThis paper mainly focuses on fixed time control for interlayer synchronism problem under voltage-flux–time (VFT) delay deplux memristive neural networks (DDMNNs). Distinct from most previously reported achievements, continuous memristive VFT neural networks (MMNs) field with$2^{2{n^{2}} + n}$variables are considered, where the memristor is considered as a continuous time-varying uncertain parameter, and n is the number of states in DDMNNs. This memristor is a type of continuous system built on the HP memristor. To synchronize VFT DDMNNs, a fixed time control policy is proposed for interlayer synchronism of flux and voltage values under the VFT DDMNNs. According to the second method of Lyapunov, selecting appropriate Lyapunov functionals and using inequality techniques, the structure and parameters of a kind of fixed time controller are designed, and interlayer synchronism criteria for DDMNNs are obtained. Finally, a secure communication scheme based on interlayer synchronism is designed. It is found that the fixed settling time is relative to the constructive of DDMNNs and the parameters of the fixed time controller. Note to Practitioners—This work solves the fixed time interlayer synchronism control problem of VFT DDMNNs, which can be applied to some practical scenarios, such as information security communication and image encryption. When transmitting signals in computer networks, for security, encryption mechanisms are usually added to network communication protocols to achieve secure communication. In some encryption applications, there are special requirements for speed of encryption and decryption, which requires reducing encryption and decryption time and improving efficiency of encryption. This work provides a fixed time control strategy which can achieve synchrony for VFT DDMNNs under controllable time, thereby achieving encryption under controllable time. Meanwhile, improve encryption efficiency in secure communication. Fei Tan 0001, Guangdeng Zong, Zhen Wang 0008, Guangming Zhuang, Xing-Chen Shang-Guan |
IEEE Trans Autom. Sci. Eng. | 1 |
| 2025 | Fixed-Time Discontinuous Control for Cluster Secure Synchronization of Interlayer Switching Networks Under AttacksabstractThis paper mainly concentrates on the issue of fixed-time cluster secure synchronization upon interlayer switching networks with DoS attacks, in which the DoS attacks may cause random damage to the nodes/edges in networks and then generate impulsive disturbances. To resist the attacks, one robust fixed-time discontinuous control strategy is proposed, such that all the nodes in interlayer switching networks can achieve the cluster secure synchronization even when the communication topology are disconnected or changed over time after being influenced by attacks. By using the Lyapunov method, several effective conditions for fixed-time cluster secure synchronization and the maximum convergence time near to the actual value are obtained. In view of the comparison principle, a suitable estimation method upon stability time for the switching networks is established, and the stability time is computable and controllable. Moreover, the correlation among attack intensity, connectivity network, impulsive interval, impulsive disturbance intensity and synchronization time is revealed. Simulation case is finally supplied to manifest the availability of the obtained outcomes. Mingzhe Huang, Fei Tan 0001, Guangdeng Zong, Zhen Wang 0008, Guangming Zhuang |
IEEE Trans Autom. Sci. Eng. | 3 |
| 2024 | Unified quantified adaptive control for multiple-time stochastic synchronization of coupled memristive neural networks
Huo Lin, Fei Tan 0001 |
Neurocomputing | 3 |
| 2023 | Stochastic fixed-time quantitative synchronization for multilayer derivative dynamic Cohen-Grossberg networks and secure communication
Fei Tan 0001, Yongmin Li 0003 |
Soft Comput. | 1 |
| 2022 | Adaptive quantitative control for robust H∞ synchronization between multiplex neural networks under stochastic cyber attacks
Fei Tan 0001, Shengyuan Xu 0001, Yongmin Li 0003, Yuming Chu, Zhengqiang Zhang |
Neurocomputing | 1 |
| 2022 | An image encryption scheme based on finite-time cluster synchronization of two-layer complex dynamic networks
Fei Tan 0001 |
Soft Comput. | 3 |
| 2022 | A two-layer networks-based audio encryption/decryption scheme via fixed-time cluster synchronization
Fei Tan 0001, Weina Ma |
Soft Comput. | 3 |
| 2021 | A fixed-time synchronization-based secure communication scheme for two-layer hybrid coupled networks
Fei Tan 0001 |
Neurocomputing | 2 |
| 2020 | Fixed-time stochastic outer synchronization in double-layered multi-weighted coupling networks with adaptive chattering-free control
Fei Tan 0001, Yuming Chu, Yongmin Li 0003 |
Neurocomputing | 1 |
| 2019 | Cluster synchronization of two-layer nonlinearly coupled multiplex networks with multi-links and time-delays
Fei Tan 0001, Fei Yu 0009 |
Neurocomputing | 2 |