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The event-based \(H_{\infty }\) control problem is researched for networked control systems subject to saturation constraints. In consideration of event-triggered mechanism and saturation constraints concurrently, the networked control system is modeled as a nonlinear system. By using Lyapunov-functional method and the linear matrix inequality techniques, the mean square stable criteria and \(H_{\infty }\) controller has been got for the controlled system with suitable event-driven matrix. A numerical example is given to illustrate the feasibility of the control approach in the paper.

The event-based \(H_\infty \) control problem of the networked control system is studied, which is under the constraint of stochastic actuators nonlinearity. Under event-triggered scheme and stochastic actuator nonlinearity restricted condition, a closed-loop system is constructed as a event-based nonlinear control system. Based on Lyapunov-Krasovskii function method and the stochastic analysis technology, \(H_\infty \) sufficient conditions have gained in the form of LMIs. The controller and the event-based weighted matrix are designed simultaneously. A simulation example is given to show the feasibility of the control method in this paper.

This paper is concerned with the problem of event-triggered dynamic output-feedback H∞ control for networked control system with sensor and actuator saturations. The event-triggered scheme combined with sensor saturation is first introduced to judge whether the newly sampled signal should be transmitted to the dynamic output-feedback controller or not. Under this scheme, the concurrent closed-loop system is first modeled as a control system with an interval time-varying delay and nonlinear items. Through constructing the Lyapunov–Krasovskii functional and employing linear matrix inequality approach, sufficient conditions for H∞ asymptotical stability are derived for the networked control system; furthermore, under the above stability condition, a dynamic output-feedback controller and the corresponding event-triggered parameters are co-designed through linear matrix inequality approach. Lastly, a numerical example is employed to prove the practical utility of this method.

Anomaly detection in surveillance video scenes is one of the current research hotspots. Due to the small sample collection of anomalous events, the lack of negative sample labeling data training in anomaly detection research adds a lot of difficulties. Therefore, we adopt the method of unsupervised training and improve the method of anomaly detection based on the reconstruction of the potential features of the predicted frame and ground truth based on u-net. We reduce the reconstruction error between the potential features of u-net in the predicted frame and the potential features of the real frame. Then through other constraints, the reconstruction error of the entire predicted frame is minimized according to the generative adversarial training. Due to the use of normal behavior sample training, when the abnormal behavior is detected, the reconstruction error value exceeds the set threshold to judge whether abnormal behavior occurs in the surveillance video. Experiments prove that our improved method is effective and accurate.

In a social environment, there is a natural mutual force between people. This paper proposes a method for detecting group abnormal behavior based on the potential field method. The movement of a pedestrian is described as the movement towards the target under the combined action of several forces. Forces on pedestrians include the attractive force for themselves and the repulsive force between pedestrians. By calculating the resultant force between them, the behavior of pedestrians is judged according to a set threshold. It is verified through experiments on public data sets that our method has high accuracy and robustness.

This paper studies event-driven synchronization control problem for chaotic Lur'e system with cyber attack. Firstly, In order to save network bandwidth resources, the event-driven scheme is employed to determine whether the sampled state should be transmitted to the controller. Secondly, a synchronization error model is designed to describe the master-slave synchronization system with event-driven scheme and cyber attack. Thirdly, based on Lyapunov functional approach, sufficient conditions for stabilization are derived which can ensure synchronization of the master system and slave system; particularly, a co-designed approach of controller and the event-driven matrix is obtained using the above stability condition. Lastly, a numerical example is presented to illustrate the effectiveness of the proposed approach.

This paper discusses event-driven synchronization control problem for chaotic neural networks subject to actuator saturation. Firstly, in order to reduce the load of network, the event-driven scheme is introduced to determine whether the sampled state should be sent to the controller. Secondly, a synchronization error model is constructed to describe the master-slave synchronization system with event-driven scheme and actuator saturation in a unified framework. Thirdly, based on Lyapunov-Krasovskii functional, sufficient conditions for stabilization are obtained which can ensure synchronization of the master system and slave system; particularly, co-design of controller and the event-triggered matrix is achieved based on the above stability condition. Lastly, a numerical example is presented to show the effectiveness of the proposed approach.

This paper studies co-design of event generator and synchronous controller for chaotic neural networks with quantization. A quantized event-triggered scheme is presented to reduce the load of network transmission. Sufficient stabilization conditions for synchronization error system are derived on the basis of Lyapunov-Krasovskii functional. Based on the above stability condition, a co-designed parameters of controller and the corresponding event-triggered parameters are obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control method.

This paper focuses on synchronization of the master-slave neural networks. Under the constraint of sampleddata control and actuator saturation, synchronization criterion of master-slave neural network-based systems has been derived through the dynamic output feedback controller(DOFC). The error signals of master-slave systems are sampled and then transmitted to dynamic output-feedback controller, and an augmented system is modeled as an interval time-varying delay control system with nonlinear items. Through constructing discontinuous Lyapunov function and employing linear matrix inequality approach, sufficient conditions are derived, which guarantee asymptotical stability and accordingly synchronization master-slave systems; on the other hand, under the above synchronization and stability condition, a dynamic outputfeedback controller is designed. A numerical example has proved the effectiveness of this method.

This paper is concerned with the problem of sampled-data filtering for delayed neural network subject to sensor saturation. Rather than the continuous measurements, the neural network measurements are sampled and then transmitted to the filter simultaneously considering sensor saturation constraints. By using the input delay approach, the sampling period is converted into a bounded time-vary delay in the filter error system. Sensor saturation function is decomposed into a linear part and a dead-zone nonlinear remainder term. By utilizing Lyapunov-functional approach, sufficient conditions have been obtained to ensure that the filter error dynamics is exponentially stable. Finally, an example is provided to illustrate the superiority and usefulness of the proposed filter approach.

This paper is concerned with the sampled-data state estimation problem for a class of delayed neural networks with randomly occurring sensor faults. Instead of the continuous measurements, the system measurements are sampled and then transmitted to the estimator to estimate the state of the neural network with simultaneous consideration of stochastic sensor faults. By employing the input delay approach, the sampling period is converted into a bounded time-vary delay in the estimation error dynamic. By employing Lyapunov-functional approach and utilizing LMI technique, sufficient conditions have been derived to guarantee that the estimation error dynamics is exponentially stable in the mean square. Finally, a simulation example has been applied to show the effectiveness of the proposed state estimation approach.

This paper is concerned with the stability of sampled-data system with sensor and actuator saturations using discontinuous Lyapunov functional approach. The system measurements are sampled and then transmitted to dynamic output-feedback controller, and a closed-loop system is modeled as an interval time-varying delay control system with nonlinear items. Through constructing discontinuous Lyapunov function and employing linear matrix inequality approach, sufficient conditions for asymptotical stability are derived; further, under the above stability condition, a dynamic output-feedback controller is designed. Lastly, a numerical example is cited to prove the practical utility of the proposed method.

The problem of H ∞ filtering problem for sampled-data system in the presence of sensor saturation has been investigated. By utilizing discontinuous Lyapunov function as well as linear matrix inequality, sufficient conditions of asymptotical stability and prescribed H ∞ performance for the filtering error system are given. Based on the conditions, the H ∞ filter exists if certain matrix inequalities are solvable. Finally, a simulation example is employed to show the effectiveness of the proposed H ∞ filter design method.

This paper is devoted to the event-triggered H ∞ control problem for networked control systems with sensor and actuator saturations. A new event-triggered scheme in consideration of saturation constraint is introduced for the networked control system. By constructing an appropriate Lyapunov-Krasovskii functional associated with the LMI technique, criteria for the exponential stability with an H ∞ norm bound and H ∞ control design have been obtained for the networked control system with proper event-triggered parameters. A simulation example is employed to show the effectiveness of the proposed control method.

This paper proposes a powerful and robust local descriptor, called color Weber feature(CWF). The CWF descriptor consists of two components: color contrast ratio and color edge orientation. Inspired by Weber's Law, we propose color contrast ratio which implements hierarchical quantization of salience within an image to simulate the pattern perception of human beings. We embed the proposed CWF representation model in the mean shift tracking framework to perform object tracking. The experiments results demonstrate that CWF is a viable object representation for tracking even in the adverse scenarios such as heavy occlusions, illumination variations and similar objects interference.