This paper investigates the stabilization problem of a class of Cohen Grossberg BAM neural network with time-varying delays. Instead of using matrix measure methods, linear matrix inequality (LMI) methods, and integral inequality methods, a new research approach is adopted: the differential inequality method, which utilizes the properties of higher-order polynomials. By finding two local extrema points of a function and designing an appropriate controller, based on Lyapunov function theory, the asymptotic stability criterion for CG-BAMNNs is obtained. In the application of the differential inequality method, the process of finding two local extrema points and the application of the properties of higher-order polynomials are very proficient, resulting in sufficiently novel outcomes. Finally, the reasonableness and effectiveness of this approach are verified through simulation examples.
In this paper, we investigate the anomaly detection problem within stochastic networked cyber-physical systems. We introduce an anomaly detection scheme centered on estimator design employing a statistical approach. Specifically, we extend the statistical method recently developed for single systems to networked control scenarios. Additionally, we analyse the detection of several representative anomalies through this statistical approach. We then apply our anomaly detection strategy to a power network system exposed to such anomalies. Our findings demonstrate that the proposed design effectively detects these anomalies, which simpler methods relying solely on residual assessment with a threshold fail to identify.
The most crucial feature of cooperative robot is interaction safety. Ensuring safe interaction with humans is essential for improving the quality and efficiency of industrial robot production. The promotion and development of cooperative robot heavily rely on ensuring safe human-robot interaction. Therefore, this paper introduces a vision-centric cooperative robot interaction system designed for dynamic environments. Firstly, the system utilizes a highly accurate 3D camera to reconstruct the monitoring scene in three dimensions. Simultaneously, the camera mounted on the robot's head reconstructs objects within its current field of view with high precision. By merging the point clouds of these two scenes, the system achieves a comprehensive reconstruction. Secondly, the system extracts the point cloud data of human bodies in the scene and determines a safe distance between humans and the robot based on the human point cloud. This analysis allows the system to define an appropriate safety range. Finally, the system evaluates whether the human body is within the predefined safe distance from the robot and facilitates improved human-robot interactions accordingly. By implementing this approach, the system effectively ensures interaction safety in the work environment. Experimental results demonstrate the effectiveness of the proposed system in maintaining a secure interaction environment.
The integration of Generative Artificial Intelli-gence (GAI) into Digital Twins (DTs) marks a revolutionary stride in the evolution of virtual replicas for physical systems. This paper explores the cutting-edge advancements brought about by the incorporation of GAI technologies, specifically Large Language Models (LLMs), into DTs. These technologies herald a significant transformation, propelling DTs beyond their current capabilities to become more dynamic, predictive, and interactive tools that can simulate complex scenarios and anticipate future conditions with remarkable accuracy. By systematically examining the levels of GAI integration within DTs, this study delves into the methodologies and strategies for embedding AI capabilities into these virtual models. It outlines how GAI can enhance the functionality of DTs, enabling them to generate synthetic datasets, simulate unprecedented events, and provide actionable insights with LLM-based agents for decision-making. Furthermore, the paper highlights the extended applications of DTs, enriched by GAI, across various domains such as healthcare, urban planning, and beyond. The implications of this integration for operational efficiency, innovation, and decision-making processes are profound. By offering a comprehensive overview of the current state of technology and projecting future trends, this paper aims to provide stakeholders with a deep understanding of the syner-gistic potential between GAI and DTs. It sets the stage for a new era of DT technologies, where the boundaries of what can be achieved with virtual models are continually expanding.
Networked multi-agent reinforcement learning (NMARL) is widely used in multi-agent systems (MASs). However, most existing NMARL algorithms share the global state and reward, which hinders their scalability in large-scale MASs. To make NMARL applicable to large-scale MASs, we propose a proximal policy optimization (PPO) based fully decentralized NMARL. First, we design a fully decentralized multi-agent reinforcement learning (MARL) framework, formulated as a networked partially observable multi-agent markov decision process (N-POMDP). The networked MAS is represented by a graph, where each agent communicates with and shares reward only with neighbors. Second, we design a gate recurrent unit (GRU) based communication strategy to learn the temporal communication correlation. Each agent exchanges observation information and hidden state with its neighbors. Finally, we conduct experiments using the multi-agent particle environment (MPE) and compare our algorithm with common MARL algorithms. The experimental outcomes reveal the exceptional performance of our algorithm in terms of both cumulative return and convergence speed within the extensive MAS.
