In the "standard" way of simulating register machines by spiking neural P systems (in short, SN P systems), one neuron is associated with each instruction of the register machine that we want to simulate. In...
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Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based br...
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Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG)-based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate perturbations. This paper proposes an unsupervised fast gradient sign method (UFGSM) to attack three popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also verify the transferability of adversarial examples in BCIs, which means we can perform attacks even without knowing the architecture and parameters of the target models, or the datasets they were trained on. To the best of our knowledge, this is the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and hopefully will trigger more attention on the security of BCI systems.
This paper presents an active disturbance rejection guidance method using quadratic transition for the atmospheric ascent guidance problem. The quadratic transition is designed from the current flight states with a re...
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In this article, an event-triggered method is proposed to solve the consensus of the second-order hybrid multiagent systems (MASs), which contain discrete-time and continuous-time individuals. First, we give a selecti...
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In this article, an event-triggered method is proposed to solve the consensus of the second-order hybrid multiagent systems (MASs), which contain discrete-time and continuous-time individuals. First, we give a selection criteria of the coupling gains, the eigenvalues of communication topology, and the event-triggered sampling interval to guarantee the hybrid consensus, which have an impact on system stability, due to the interaction and co-existence of discrete-time and continuous-time individuals. Second, the hybrid second-order consensus under the event-triggered strategy is proven, where the agents communicate with their neighbors and update their controllers only at the triggered instants. Finally, we give some simulation examples to prove the validity of the main results.
Pandemic influenza A (H1N1) has spread rapidly across the globe. In the event of pandemic influenza A (H1N1), decision-makers are required to act in the face of substantial uncertainties. Simulation models can be used...
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This is a survey of two approaches to intelligentcontrol. The approaches are based on the author’s previous and ongoing projects in the Maximum Clique Problem (MCP) and the Crowd Dynamics. The ideas came from comput...
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Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic started to be considered also in the framework of (synchronized) spiking neural P system...
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作者：
Ye, BPeng, JXHuazhong Univ Sci & Technol
Inst Pattern Recognit & Artificial Intelligence State Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China
Zernike moments have many desirable properties, such as rotation invariance, robustness to noise, expression efficiency, fast computation and multi-level representation for describing the shapes of patterns, but there...
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Zernike moments have many desirable properties, such as rotation invariance, robustness to noise, expression efficiency, fast computation and multi-level representation for describing the shapes of patterns, but there is a major drawback with Zernike moments: they need to normalize an image to achieve scale invariance. This introduces some errors since it involves the re-sampling and re-quantifying of digital images, and leads to inaccuracy of classifier. In this paper, we present improved Zernike moments, with theory and experiments that show that the improved Zernike moments not only have better rotation invariance, but also have scale invariance. Invariance of the improved Zernike moments shows great improvement over previous methods.
The robustness of multiple Euler-Lagrange systems (MELSs) measures the capability of maintaining coordination control performance when experiencing disturbance or faults. This article investigates a leader-follower ro...
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The robustness of multiple Euler-Lagrange systems (MELSs) measures the capability of maintaining coordination control performance when experiencing disturbance or faults. This article investigates a leader-follower robust consensus problem of MELSs, which experience time-varying input disturbance and uncertain communication link faults (CLFs). First of all, based on an adaptive control theory, we design fully distributed observers for estimating the dynamic and state of leader, which have robustness to CLFs. Then, an observer-based proportional-integral (PI) control protocol is designed to achieve consensus of MELSs with robustness to time-varying input disturbance. Different from the existing related results, this robust observer-based PI controller is fully distributed and model independent, which is irrelevant to any prior information (i.e., the structures and features) of agent dynamic or global network information. At last, the validity of the proposed theoretical results is confirmed by a simulation example.
Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts ha...
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Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV's flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectory optimization problems in a single-UAV-assisted MEC system.
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