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This paper presents a novel adaptive memory-event-triggered scheme (A-METS) for network-based T-S fuzzy systems with asynchronous premise constraints. Different from the event-triggered scheme (ETS), the proposed A-METS has two characters. First, some recent released packets are applied in deciding whether the current packet is supposed to be released. Second, the triggering parameter can adjust itself according to the variation of the state. It should be pointed out that the proposed adaptive memory-event-triggered scheme can save network recourses and improve the system performance simultaneously when compared with ETS or adaptive ETS. Furthermore, considering the network environment, the asynchronous premise between the fuzzy plant and controller are considered. By resorting to the Lyapunov functional approach, sufficient conditions are derived for the stability and H ∞ performance of the network-based T-S fuzzy systems. For the sake of illustrating the usefulness of the proposed adaptive memory-event-triggered scheme, a tunnel diode circuit example is demonstrated at the end of this paper. INTRODUCTIONIn networked control systems (NCSs), sensors, actuators, controllers and other major features are connected through wire/wireless network , wherein relevant control/measurement signals are transferred through a communication network [1,2]. The main advantages of NCSs include resource sharing, remote operating and control, increasing flexibility and reliability and so on. Based on these merits, many fruitful results have been proposed (see [3][4][5][6][7] and the references therein). Nowadays, NCSs are used in many fields such as industrial automation, multiagents systems, unmanned marine vehicles [8][9][10][11].Recently, event-triggered scheme (ETS) has become a hot topic because it can improve the network utilisation effectively by holding back the unnecessary executions. In general, the ETS works as follows, when a new packet is sampled, which is computed through a triggering conditions, if this condition is satisfied, this packet is regarding as "necessary" one and released to the controller, else, it will be dropped. Obviously, the triggering condition plays an utmost key role in the ETS. The parameters This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Deep Convolutional Neural Networks (CNNs). On the one hand, ViTs' focus on global interaction between individual patches reduces the local noise sensitivity of images. On the other hand, the existing decision-based attacks for CNNs ignore the difference in noise sensitivity between different regions of the image, which affects the efficiency of noise compression. Therefore, validating the black-box adversarial robustness of ViTs when the target model can only be queried still remains a challenging problem. In this paper, we propose a new decisionbased black-box attack against ViTs termed Patch-wise Adversarial Removal (PAR). PAR divides images into patches through a coarse-to-fine search process and compresses the noise on each patch separately. PAR records the noise magnitude and noise sensitivity of each patch and selects the patch with the highest query value for noise compression. In addition, PAR can be used as a noise initialization method for other decision-based attacks to improve the noise compression efficiency on both ViTs and CNNs without introducing additional calculations. Extensive experiments on ImageNet-21k, ILSVRC-2012, and Tiny-Imagenet datasets demonstrate that PAR achieves a much lower magnitude of perturbation on average with the same number of queries. Our code is available at https://github.com/shiyuchengTJU/PAR.