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RISS 인기검색어
- 검색키워드
- 저자 : Zengyu Cai (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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A Chebyshev-Map Based One-Way Authentication and Key Agreement Scheme for Multi-Server Environment
Zengyu Cai,Yuan Feng,Junsong Zhang,Yong Gan,Qikun Zhang 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.6
One-way authentication and key agreement scheme can achieve strong user anonymity and transmitted data confidentiality over insecure public communication channel, which is very useful for the user who cares about his/her identity information. In conventional networks, Public Key Infrastructure (PKI) is a very useful component to design one-way authentication key agreement scheme. However, it will consume large amounts of computing resources. Therefore, it is inappropriate to PKI in a resource-constrained environment. In this paper, we proposed a new one-way authentication and key agreement scheme based on Chebyshev chaotic map. Compared with the related research activities, our proposed scheme has not only the high efficiency and unique functions, but also robust to various attacks and achieves perfect forward secrecy. Security and performance analyses demonstrate that the proposed scheme can solve various types of security problems and can meet the requirements of computational complexity for low-power mobile devices.
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Design and Realization of Visible Birds Recognition Expert System
Fangmei Liu,Zengyu Cai,Yuan Feng,Yong Gan 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.12
Expert System is one of the most important and active parts in the application of artificial intelligence. This text presents the design and Realization Expert System in Visible Birds Recognition Area to show the steps and the methods of establishing visible expert system. Firstly, this text introduces key technology of expert system, especially the basic functions and knowledge presentation of it; Secondly, the test provides the design of expert system, including function design, interface design, knowledge presentation and inference machine design; finally, it shows the realization and testing of rule-based expert system. The text proves the expert system in visible birds recognition mentioned and designed in this paper has the ability of knowledge management, words-based birds’ recognition and graphs-based recognition, and this system owns the basic characters of expert system.
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Migration and Energy Aware Network Traffic Prediction Method Based on LSTM in NFV Environment
Ying Hu,Liang Zhu,Jianwei Zhang,Zengyu Cai,Jihui Han 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.3
The network function virtualization (NFV) uses virtualization technology to separate software from hardware. One of the most important challenges of NFV is the resource management of virtual network functions (VNFs). According to the dynamic nature of NFV, the resource allocation of VNFs must be changed to adapt to the variations of incoming network traffic. However, the significant delay may be happened because of the reallocation of resources. In order to balance the performance between delay and quality of service, this paper firstly made a compromise between VNF migration and energy consumption. Then, the long short-term memory (LSTM) was utilized to forecast network traffic. Also, the asymmetric loss function for LSTM (LO-LSTM) was proposed to increase the predicted value to a certain extent. Finally, an experiment was conducted to evaluate the performance of LO-LSTM. The results demonstrated that the proposed LO-LSTM can not only reduce migration times, but also make the energy consumption increment within an acceptable range.
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Multi-level Cross-attention Siamese Network For Visual Object Tracking
Jianwei Zhang,Jingchao Wang,Huanlong Zhang,Mengen Miao,Zengyu Cai,Fuguo Chen 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12
Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.
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