Ruonan Liu

Visual context is essential for point cloud semantic segmentation. The contextual information captures the semantic relationship between 3-D points, providing helpful hints for reasoning the category labels of points. Most current methods harness the internal context from the parts of the same object (or from the things within the same scene). In contrast, we propose the external point-set context (EPSC), allowing a richer context of external points learned across various objects/scenes to assist the segmentation task. We employ an external memory with multiple sets to store the EPSC representations learned from the training data. Each representation is a cluster feature, which captures the relationship between adjacent 3-D points for recognizing the semantic category of the center point. During the inference phase, the external memory releases the EPSC representations, providing rich and relevant context for segmenting the target point cloud. We extensively evaluate our method on Stanford Large-Scale 3-D Indoor Spaces (S3DIS), ScanNetv2, and ShapeNetPart datasets, where we achieve the result of effective improvement.

Networked systems (NSs) are particularly vulnerable to faults and cyber threats. To address these challenges, this article develops an event-triggered fault detection (FD) methodology in NSs subject to denial-of-service (DoS) attacks. Firstly, a novel probabilistic event-triggered protocol (NPETP) is proposed that utilizes the time-varying characteristics of transmission delays, significantly reducing unnecessary data transmissions. Secondly, we establish a residual system model, where sufficient conditions ensuring exponential stability and ${\mathcal {H}}_{\infty }$ performance are rigorously derived using piecewise Lyapunov functional methods. Finally, a genetic algorithm (GA)-assisted optimization framework is employed to solve the complex nonlinear matrix inequalities, effectively eliminating conventional conservatism. Simulation results demonstrate the superior detection performance of the proposed GA-assisted event-triggered FD method. Note to Practitioners—Networked systems (NSs) serve as critical infrastructures in industrial automation, intelligent transportation, and so on. In these systems, communication resources are typically shared, limited, and vulnerable to both physical faults and cyber threats such as denial-of-service (DoS) attacks. The dual challenge of efficiently allocating scarce bandwidth while maintaining robust fault detection—particularly under DoS attacks—has thus become an urgent priority. To address this, this paper proposes a novel event-triggered fault detection method utilizing a GA-assisted optimization algorithm. Compared with traditional literature, the proposed method can effectively save communication resources while guaranteeing system performance. Crucially, the GA is employed as an assisted optimization tool to resolve the complex nonlinear couplings within the design constraints, which significantly yields more reasonable and less conservative results than conventional analytical decoupling techniques. This GA-assisted framework enables reliable fault detection even during network attacks, providing operators with timely warnings to prevent catastrophic system failures. The method’s effectiveness in maintaining detection capabilities under adverse conditions represents a significant improvement over conventional fault detection systems.

This article investigates the problem of disturbance attenuation for autonomous surface vehicles (ASVs) subject to asymmetric time-varying saturation. To address this challenge, we propose a novel event-triggered (ET) zero-sum (ZS) differential game framework that integrates a barrier function and a nonquadratic value function, enabling simultaneous achievement of safety control and disturbance attenuation. Initially, by designing a barrier function, the control problem of the asymmetric time-varying saturated ASV system is transformed into an unsaturated form. Afterward, a nonquadratic value function is designed, and ZS games are employed to obtain the saturated-optimal control policy and the worst-case disturbance policy. Then, the ET method is introduced into policy execution and critic neural network learning. The scheme in this article ensures the safety and stability of the ASV while reducing computational burden and meeting the needs of disturbance attenuation. Finally, theoretical analysis and simulation experiments verify the feasibility of the ET ZS differential game approach.

This article addresses the formation control of under-actuated multiple autonomous surface vehicles (MASVs) with input quantization under communication delay conditions, which is influenced by external marine disturbances and internal model uncertainties. A two-level distributed guidance and quantization control architecture based on the Nussbaum function is proposed. At the communication level, a time-delay distributed event-triggered extended state observer (ESO) is introduced to estimate the state of the single virtual leader, thereby further conserving communication resources. At the control level, the distributed formation guidance laws based on ESO are proposed in the kinematic subsystem, enabling effective tracking of the ideal trajectory while estimating the states of neighboring agents and unknown ocean disturbances. In the dynamics subsystem, a fuzzy logic system is used to estimate the uncertain terms within the model, and a linear model is introduced to handle the input quantization process. Additionally, the fuzzy adaptive quantization tracking control laws based on the Nussbaum function are proposed to achieve accurate tracking of the guidance signals and reduce actuator execution frequency, which makes the proposed scheme more applicable to practical marine engineering scenarios. The stability of the designed control structure is proven based on stability theory, and all signals within the closed-loop control system are uniformly ultimately bounded. Simulation experiments validate the rationality and effectiveness of the proposed method.