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- 저자 : Yuwei Ren (검색결과 2 건)
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Xiaoyun Yi,Yuwei Ren,Li Qi,Ben Niu,Yixian Fang 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.4
In this paper, we investigate the problem of weight estimation and secure control for discrete stochastic distribution control (SDC) systems under sparse sensor attacks. Firstly, a Luenberger observer is designed for the linear SDC systems to perform the weight estimation under sparse sensor attacks. Then, a generalized proportionalintegral (PI) tracking control strategy is proposed for the linear B-spline model. Furthermore, the tracking problem for output probability density functions (PDFs) is implemented, and the designed controller ensures that the closedloop system is stable. Finally, the simulation results show the effectiveness of the proposed method.
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Convolutional auto-encoder based multiple description coding network
( Lili Meng ),( Hongfei Li ),( Jia Zhang ),( Yanyan Tan ),( Yuwei Ren ),( Huaxiang Zhang ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.4
When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.
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