Resilient control of networked control systems with stochastic denial of service attacks

This paper focuses on resilient control of networked control systems (NCSs) under the denial of service (DoS) attacks characterized by a Markov process. Firstly, based on the game between attack strategies and defense strategies, the packet dropouts induced by DoS attacks are modeled as a Markov process. Secondly, an NCS under DoS attacks is modeled as a Markovian jump linear system. Then, by use of the Lyapunov theory and the derived NCS model, four theorems are given for the system stability analysis and controller design. Finally, a numerical example is used to illustrative the effectiveness of proposed method

Anomaly Event Detection in Security Surveillance Using Two-Stream Based Model

Anomaly event detection has been extensively researched in computer vision in recent years. Most conventional anomaly event detection methods can only leverage the single-modal cues and not deal with the complementary information underlying other modalities in videos. To address this issue, in this work, we propose a novel two-stream convolutional networks model for anomaly detection in surveillance videos. Specifically, the proposed model consists of RGB and Flow two-stream networks, in which the final anomaly event detection score is the fusion of those of two networks. Furthermore, we consider two fusion situations, including the fusion of two streams with the same or different number of layers respectively. The design insight is to leverage the information underlying each stream and the complementary cues of RGB and Flow two-stream sufficiently. Two datasets (UCF-Crime and ShanghaiTech) are used to validate the effectiveness of proposed solution

Cattle Body Detection Based on YOLOv5-EMA for Precision Livestock Farming

Accurate cattle body detection is crucial for precision livestock farming. However, traditional cattle body detection methods rely on manual observation, which is both time-consuming and labor-intensive. Moreover, computer-vision-based methods suffer prolonged training times and training difficulties. To address these issues, this paper proposes a novel YOLOv5-EMA model for accurate cattle body detection. By incorporating the Efficient Multi-Scale Attention (EMA) module into the backbone of YOLO series detection models, the performance of detecting smaller targets, such as heads and legs, has been significantly improved. The Efficient Multi-Scale Attention (EMA) module utilizes the large receptive fields of parallel sub-networks to gather multi-scale spatial information and establishes mutual dependencies between different spatial positions, enabling cross-spatial learning. This enhancement empowers the model to gather and integrate more comprehensive feature information, thereby improving the effectiveness of cattle body detection. The experimental results confirm the good performance of the YOLOv5-EMA model, showcasing promising results across all quantitative evaluation metrics, qualitative detection findings, and visualized Grad-CAM heatmaps. To be specific, the YOLOv5-EMA model achieves an average precision ([email protected]) of 95.1% in cattle body detection, 94.8% in individual cattle detection, 94.8% in leg detection, and 95.5% in head detection. Moreover, this model facilitates the efficient and precise detection of individual cattle and essential body parts in complex scenarios, especially when dealing with small targets and occlusions, significantly advancing the field of precision livestock farming

Minimal leader selection in general linear multi-agent systems with switching topologies : leveraging submodularity ratio

In multi-agent systems with leader-follower dynamics, choosing a subset of agents as leaders is a critical step in achieving the desired coordination performance. In this study, by considering consensus tracking for general linear multi-agent systems under switching topologies, we address the problem of selecting a minimum-size set of leaders by leveraging the submodularity ratio. First, using the dwell time technique, a criterion is derived to ensure that the states of all agents can converge to a reference trajectory that is directly tracked by each leader. Second, exploiting the derived consensus tracking criterion, the metrics with a structure of the Euclidean distance between specific vectors and the space spanned by an iteratively updated matrix are established to identify a set of leaders, and then the corresponding bound of the submodularity ratio is proposed. Third, combining the derived criterion and the constructed metrics, a leader selection scheme is presented together with three polynomial-time algorithms, and the related provable optimality bound of each algorithm can be obtained by leveraging the proposed bound of the submodularity ratio. Finally, illustrative examples are provided to verify the effectiveness of the proposed leader selection scheme