Few-shot and zero-shot point cloud semantic segmentation aim to accurately segment novel categories using limited or no labeled samples, respectively. However, existing methods face significant challenges including domain shifts between support and query sets and the inability to handle both few-shot and zero-shot scenarios within a unified framework. To address these issues, we propose a biologically-inspired Evolutionary Domain Symbiosis Network EDS-Net for unified few-shot and zero-shot point cloud semantic segmentation. Specifically, inspired by natural symbiotic evolution, we propose a Symbiotic Evolution Module (SEM) that models co-adaptation between support and query features through self-correlation and cross-correlation mechanisms. Second, motivated by genetic crossover mechanisms, we introduce a Vision-Semantic Bridging Module (VSBM) that treats visual prototypes and semantic prototypes as two “parent” individuals, creating fused offspring prototypes through adaptive crossover operations and mutation strategies for zero-shot scenarios. Third, we develop a multi-generational evolutionary optimization framework employing an adaptive gating network to learn optimal fusion weights across different evolutionary stages. Extensive experiments demonstrate that EDS-Net with biological interpretability achieves state-of-the-art performance on both few-shot and zero-shot settings.

Drugs play a central role in limiting bacterial population spread, yet laboratory studies typically assume well-mixed environments when assessing microbial drug responses. In contrast, bacteria in the human body often occupy spatially structured habitats where drug concentrations vary. Understanding how this heterogeneity shapes growth and decline is therefore essential for controlling infections and mitigating resistance evolution. Here, we developed a minimal robot-automated system to study how spatial drug heterogeneity affects short-term population dynamics in E. faecalis, a Gram-positive opportunistic pathogen. This system was combined with a theoretical framework to interpret and explain the observed outcomes. We first recapitulated the classic critical-patch-size model result: in a spatially homogeneous environment, a population persists in a finite domain only when growth outpaces diffusive losses at the boundaries. In heterogeneous environments, we found certain conditions that population persistence can depend critically on the spatial arrangement of the drug, even when its total amount is fixed. Using theoretical and experimental approaches, we identified the arrangements that produce the strongest growth and the fastest decline, revealing the range of possible outcomes under drug heterogeneity. We further tested this framework in more complex environments, including ring-shaped communities, and observed consistent arrangement-dependent behavior. Overall, our results extend the classical growth-condition framework to general heterogeneous environments and demonstrate that spatial drug arrangement - not only total dose - can strongly influence bacterial population dynamics. These findings highlight the importance of spatially structured dosing strategies and motivate further theoretical and experimental investigation.

This paper proposes an advanced composite predictive framework leveraging motion pattern decoupling, designed to enhance the prediction accuracy and computational efficiency of three-dimensional relative distance in autonomous underwater docking operations for Unmanned Underwater Vehicles (UUVs). Initially, the framework employs a velocity discontinuity detection algorithm to partition the original three-dimensional trajectory data into kinematic-consistent subtrajectories characterized by uniform motion patterns. Subsequently, integrating Dynamic Time Warping (DTW) with the elbow method, the K-means algorithm is refined to facilitate trajectory pattern clustering and establish distinct pattern category labels. For each motion pattern identified, a customized Gated Recurrent Unit (GRU) predictive model is meticulously trained. During the online prediction phase, real-time kinematic states are extracted through a sliding window mechanism, and the K-Nearest Neighbors (KNN) classifier is adeptly utilized to select the most suitable GRU model for dynamic forecasting. Empirical analyses, grounded in 200 sets of authentic underwater docking experiment data, reveal that this framework markedly surpasses standalone LSTM and GRU models, as well as acoustic-optic sensors, in predictive precision. Furthermore, the single-step prediction latency consistently remains under 17 milliseconds, thus fulfilling the real-time requisites of high-dynamic conditions. This research furnishes theoretical and technical underpinnings for precise UUV docking in intricate underwater milieus.

Point Cloud Few-Shot Semantic Segmentation (PC-FSS) aims to segment unknown categories in query samples using only a small number of annotated support samples. However, scene complexity and insufficient representation of local geometric structures pose significant challenges to PC-FSS. To address these issues, we propose a novel pre-training-free Visual Introspective Prototype Segmentation network (VIP-Seg). Specifically, we design a Visual Introspective Prototype (VIP) module that employs a multi-step reasoning approach to tackle intra-class diversity and domain gaps between support and query sets. The VIP module consists of a Prototype Enhancement Module (PEM) and a Prototype Difference Module (PDM), which work alternately to progressively refine prototypes. The PEM enhances prototype discriminability and reduces intra-class diversity, while the PDM learns common representations from the differences between query and support features, effectively eliminating semantic inconsistencies caused by domain gaps. To further reduce intra-class diversity and enhance point discriminative ability, we propose a Dynamic Power Convolution (DyPowerConv) that leverages learnable power functions to effectively capture local geometric structures and detailed features of point clouds. Extensive experiments on S3DIS and ScanNet demonstrate that our proposed VIP-Seg significantly outperforms current state-of-the-art methods, proving its effectiveness in PC-FSS tasks. Our code will be available at https://github.com/changshuowang/VIP-Seg .

The wind energy, as a kind of renewable energy resources, has the potential to replace traditional fossil fuels. However, its intermittent power output can incur frequency instability due to the instantaneous unbalance between power generation and load demand. To smooth the penetration of wind energy, this paper presents a robust cooperative load frequency control (LFC) strategy for multi-area power systems, which is a hierarchical control approach. For the low-level wind turbine control, this paper adopts model predictive control (MPC) method to achieve the rated wind power tracking. In the meantime, an improved event-triggered scheme (ETS) considering multiple historic released signals is employed to relieve the computational burden of MPC. For the high-level cooperative LFC, this paper incorporates the robust performance index in the control synthesis to suppress the impact of intermittent wind power on frequency stability. In addition, to address the underlying shift of the steady-state operating point caused by the intermittent wind power supply, this paper improves the commonly used small-signal LFC model by adding an uncertain matrix, which reasonably explains the possible change of system parameters and extends the applicability of the traditional LFC model. Simulations are done on a four-area power system, and the results verify the efficacy of the presented event-triggered scheme and the robust cooperative LFC approach.Note to Practitioners—To promote the penetration of wind energy into power systems, this work explores a robust cooperative LFC approach under multi-agent structure to ensure the stability of the system, aiming at extending the applicability of existing approaches. The proposed approach is hierarchical. At the rated wind power tracking level, the MPC is employed to handle constraints associated with actuating devices, such as heterogeneous convertors. Simultaneously, an improved ETS considering multiple historic triggered signals is integrated in the MPC to reduce the computational burden. At the power system level, the robust performance index is incorporated in the control design to smooth the impacts of intermittent wind power on frequency stability. Additionally, the study accounts for the potential shift of the steady-state operating point and improves the traditional small-signal LFC model by adding an uncertain matrix, which can better explain the variation of system parameters and is more applicable in practical power system engineering. Simulation results demonstrate that the proposed robust cooperative LFC approach can effectively maintain the system frequency within the admissible range under the high penetration of wind energy, whereas the traditional PI controller falls short in this regard.

Although rich collection of research results on sensor attack (SA) exist, no attack detection mechanism has ever been designed based on output error information to detect whether an attack has occurred. The primary objective of this article is to build a backstepping adaptive tracking control protocol for heterogeneous nonlinear uncertain multi-agent systems (HNUMASs) with partially SA. A dynamic SA detection mechanism by using output error information of agent only is developed to identify the SA. After locating the attack, to circumvent the effects of unknown time-varying output gain caused by SAs, we introduce the Nussbaum function in backstepping design to compensate the unknown time-varying output gain. The result shows that the developed SA detection mechanism is able to detect the occurrence of attack in a timely manner, while the derived adaptive backstepping tracking controller can effectively handle the adverse effects of SA and ensure that all signals are bounded in the closed-loop system. At last, the effectiveness and benefits of the presented approach are verified by simulation example. Note to Practitioners—This paper aims to achieve the adaptive tracking control for HNUMASs under SA, which can be widely used in practice, such as power systems, vehicular platoon systems, etc. The control protocol consists of a attack detection mechanism and Nussbaum function that compensates for the unfavorable effects caused by SAs. Moreover, the system may also be influenced by uncertainties from its neighboring agents in practical applications. Therefore, an additional estimator is designed in each subsystem to handle the uncertainties involved in its neighbor dynamics. This design avoids the exchange of information related to local neighborhood consensus errors among connected subsystems. A feasible strategy is provided for industrial applications.

This paper solves the problem of spacecraft rendezvous with input delay by designing the periodic event-triggered control (PETC) and periodic self-triggered control (PSTC), respectively. Firstly, a PETC based on the discrete-time parametric Lyapunov equation (DPLE) is designed to stabilize the delayed spacecraft rendezvous systems. Moreover, in order to avoid monitoring the measurement errors, a PSTC algorithm that the updates of the next control law depend on the previous triggered states is also designed. Specially, by using the properties of the DPLE, this new approach is not only simple, but also provides an easy and explicit condition on the only parameter of DPLE to guarantee the non-triviality of the designed PETC and PSTC. Finally, the effectiveness of theoretical results is verified by simulations. Note to Practitioners—Although this paper was inspired by the problem of spacecraft rendezvous, the designed algorithms can also be applied to other time-delay systems. Existing general event-triggered control methods have been used to solve the problem of spacecraft rendezvous for reducing the communication load, although this often complicates the design of the controller due to preventing the occurrence of Zeno phenomenon. To overcome this drawback, This paper proposes a periodic event-triggered control to achieve the spacecraft rendezvous with input delay, which naturally avoids the occurrence of Zeno phenomenon. In addition, a periodic self-triggered control is also designed, which has never been designed to solve the problem of delayed spacecraft rendezvous.

Although distributed renewable energy sources (DRESs) provide a sustainable solution to future microgrids (MGs), their fluctuant power outputs can incur frequency instability. The work studies the load frequency control (LFC) for MGs with the integration of wind energy under a hierarchical architecture. At the DRES level, a model predictive control method is employed together with an intensified event-triggered scheme considering multiple historic released signals to improve the computation efficiency. At the MG level, robustness specification is addressed in mean-square asymptotic stability to relieve the fluctuations caused by wind power penetration. Furthermore, the phasor measurement unit (PMU) failure and intermittent transmissions are considered in the control design, leading to the resilient control policy. Besides, this article extends the applicability of conventional small-signal LFC model by adding an uncertain matrix to tolerant the parameter variation due to the shift of the steady-state operating point caused by wind energy integration. The closed-loop performance based on the deployed resilient LFC strategy is verified through hardware-in-the-loop experiments, by which the frequency regulations against PMU failures and intermittent communication at different levels are effectively exhibited.

The increasing integration of renewable energies, while beneficial for environmental and economic sustainability through decarbonization, poses challenges to frequency stability due to the intermittent nature of renewable power supply. To facilitate smoother integration into the main grid, this study proposes a resilient distributed load frequency control (RDLFC) strategy with a hierarchical structure. At the lower level, wind energy integration is managed using a model predictive control framework enhanced by an improved event-triggered scheme, which can effectively trigger key feedback signals at critical points and tolerates imperfect event modeling and generator dysfunctions. Plug-in electric vehicles are also utilized for fast frequency regulation. At the higher level, the linearized model is improved with an uncertain parameter matrix to account for variations in steady-state operating points due to renewable integration. A robust performance index is incorporated to derive stability conditions, even in the presence of temporary faults in phasor measurement units (PMUs). Validation results confirm the effectiveness of the proposed RDLFC strategy in handling temporary PMU faults.

The microgrid, capable of providing flexible and controllable means to integrate distributed renewable energy sources (DRESs), has long been seen as a most promising solution to convert DRESs into the electrical power. However, the intermittent power output of DRESs, together with the unexpected load perturbations, challenges the frequency stability of microgrids. In this context, the work proposes a robust distributed load frequency control (DLFC) method for multi-area wind energy-dominated microgrids, in which way all interconnected areas can work cooperatively to confront the unbalanced power occurring in partial areas. Considering the flexibility for large-scale deployment, the wind energy is taken as the DRES, while the thermal power plant is chosen as the base power generation. To mitigate the fluctuation of wind power output caused by uncertain wind speed, a sample-based stochastic model predictive control approach is presented. For the high-level DLFC of multi-area microgrids, robust performance index is incorporated in stability analysis and control synthesis. Moreover, temporary phasor measurement unit (PMU) failures are considered in DLFC design and are modeled by random Bernoulli variables to quantitatively analyze their impacts on frequency dynamics. Validations on a four-area microgrid verify the efficacy of the proposed robust DLFC method under different PMU failure probabilities.

The rapid developments of machine learning techniques open new viewpoints of control designs for cyber-physical systems, especially for their intrinsical advantages in adaptability to uncertain environment, less conservatism as well as extensible safe region compared to classical control schemes. In this paper, we synthesize the deep neural network (DNN)-based control architecture for safety-critical systems with uncertainty resilience. This can be achieved by deploying a DNN controller to stabilize a class of constrained nonlinear systems with unknown uncertainties and a DNN compensator to estimate the uncertainty impacts on the safety certificates to modify the control actions leading to safety preference. Based on the quadratic constraint of DNN and Lipschitz smoothness of nonlinear plant, we establish the formal guarantees on ensuring the closed-loop stability and obtain the optimal inner approximation of region of attraction. We then develop the DNN-based estimators of uncertainty impacts embedded in discrete-time control barrier functions with different relative of degrees via imitation learning, respectively, such that the control inputs can be minimally modified by the learning-based safety filter to realize the collision avoidance. The applicability of our theoretical results is demonstrated by the case study of vehicle lateral dynamics.

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

To further the penetration level of renewable energy sources (RESs) in power systems, the paper integrates wind turbines into conventional load frequency control (LFC). A resilient model predictive control (MPC) framework is constructed in the context of potential false data injection (FDI) attacks on vulnerable communication networks of multi-area power systems. To reduce the power generation cost, an economic cost function for MPC is firstly formulated. Then, a decentralized-model-based$\chi^{2}$detection unit is presented to distinguish the attacked measurements sent from neighbors. Moreover, to reduce the computation burden of executing the distributed MPC strategy, an intensified event-triggered scheme that can handle incomplete and inaccurate modeling issues is proposed. Validation results illustrate the efficacy of the detection unit and the intensified event-triggered scheme, and conclude the relationships between alarming thresholds and key performance indicators.Note to Practitioners—This paper explores the applicability of LFC with the integration of wind turbines under economic MPC framework. Motivated by the underlying FDI attacks on vulnerable communication networks among different control areas, an intrusion detection unit is proposed to install at each controller side to realize resiliency enhancement. Different from the existing works, this paper meticulously investigates the relationships between alarming thresholds and key performance indicators (KPIs), aiming at providing some valuable references for power operators and managers. Besides, this paper proposes an intensified event-triggered scheme to relieve the computation burden of MPC algorithm. This intensified event-triggered scheme has two advantages. One is that it considers the historic released signals in event-triggered conditions, which makes sure the critical signals at crests or troughs of frequency dynamic curves can be triggered. The other advantage is that the supplementary event-trigged condition can well tolerant the incomplete and inaccurate modeling problems existing in conventional event-triggered conditions. Simulations verify the efficacy and feasibility of the resilient event-triggered MPC strategy for frequency regulation under FDI attacks.

While the Internet of Things (IoT) structure is capable to facilitate the distributed load frequency control (DLFC), the open-air sensors and the intrinsically open communication networks are inevitably vulnerable to uncertain environments. This work endeavors to present a general resiliency enhancement framework for DLFC considering the IoT faults. Multiple fault sources are incorporated, including the intermittent measurements caused by sensor aging, the communication network failures caused by cyberattacks, etc. The framework is equipped with two resilient layers. The first resilient layer focuses on the offline robust DLFC design, in which we consider the intermittent measurements from sensors in system modeling. The second resilient layer concerns the online cyberattack detection, which can further tolerant the incomplete modeling issues of the first resilient layer. Simulation results verify the efficacy of the presented resilient framework.

For a long period, automated vehicles (AVs) or vehicle platoons will coexist with human-driven vehicles (HDVs) in heterogeneous traffic flow, where the cut-in maneuver of human drivers can be frequently expected. In this paper, to understand and simulate the driver decisions on whether to continue the cut-in and when to execute the lane-change during the cut-in process, we propose a two-layer prediction-based decision model by integrating a dynamic prediction module, a continuity decision module, and an execution decision module. To our best knowledge, this is the first study to model the driver decision behavior of the cut-in process. Cut-in experiments are conducted to collect the decision and control data of drivers under one-and two-target-vehicle scenarios, which both include sixty sub-scenarios with different initial velocities, accelerations, or positions of the vehicles. We prove the effectiveness of the proposed model in simulating the driver decision behavior of the cut-in process by comparing the experimental and simulation results under various scenarios over different subjects. Besides, we analyze the effects of some model parameters on the model performance to show their ability to represent different driving styles.

Pedestrian trajectory prediction has been broadly applied in video surveillance and autonomous driving. Most of the current trajectory prediction approaches are committed to improving the prediction accuracy. However, these works remain drawbacks in several aspects, complex interaction modeling among pedestrians, the interactions between pedestrians and environment and the multimodality of pedestrian trajectories. To address the above issues, we propose one new trajectory distribution aware graph convolutional network to improve trajectory prediction performance. First, we propose a novel directed graph and combine multi-head self-attention and graph convolution to capture the spatial interactions. Then, to capture the interactions between pedestrian and environment, we construct a trajectory heatmap, which can reflect the walkable area of the scene and the motion trends of the pedestrian in the scene. Besides, we devise one trajectory distribution-aware module to perceive the distribution information of pedestrian trajectory, aiming at providing rich trajectory information for multi-modal trajectory prediction. Experimental results validate the proposed model can achieve superior trajectory prediction accuracy on the ETH & UCY, SSD, and NBA datasets in terms of both the final displacement error and average displacement error metrics.

Distributed cyber–physical systems (CPSs) are with complex and interconnected framework to receive, process, and transmit data. However, they may suffer from adversarial false data injection attacks due to the more open attribute of their cyber layers, and the connections with neighbor agents could aggravate the disastrous consequences on the system performance degradation. In this article, we focus on investigating distributed secure estimation paradigms against sparse actuator and sensor corruptions by virtue of combinational optimization. First, the consensus-based static batch optimization and secure observer design problems are established, based on which the concepts of sparsity repairability and restricted eigenvalues under attacks are discussed. Then, both the distributed projected heavy-ball estimator and distributed projected Luenberger-like observer are designed, in terms of the intensified combinational vote locations and distributed implementation of projection operator, with strict convergence guarantees. Finally, two numerical examples are performed to verify the effectiveness of our theoretical derivation.

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme.

In this article, a novel distributed fuzzy load frequency control (LFC) approach is investigated for multiarea power systems under cross-layer attacks. The nonlinear factors existing in turbine dynamics and governor dynamics as well as the uncertain parameters therein are modeled and analyzed under the interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy framework. The cross-layer attacks threatening the stability of power systems are considered and modeled as an independent Bernoulli process, including denial-of-service (DoS) attacks in the cyber layer and phasor measurement unit (PMU) attacks in the physical layer. By using the Lyapunov theory, an area-dependent Lyapunov function is proposed and the sufficient conditions guaranteeing the system’s asymptotically stability with the area control error (ACE) signals satisfying$\mathcal {H}_{\infty }$performance are deduced. In simulations, we adopt a four-area power system to verify the resiliency enhancement of the presented distributed fuzzy control strategy against random cross-layer DoS attacks. Results show that the designed resilient controller can effectively regulate the load frequency under different cross-layer DoS attack probabilities.

With the participation of a significant number of plug-in electric vehicles (PEVs), it is really challenging to achieve economic-effective in load frequency control (LFC) while sustaining satisfiable system performance. To tackle this challenge, a new distributed economic model predictive control (DEMPC) strategy is proposed for the LFC with the large-scale PEV participation. In the light of the vulnerability of LFC to false data injection (FDI) attacks, a model-based χ2intrusion detection unit is integrated with the proposed DEMPC. This model-based intrusion detection unit can not only monitor the FDI attacks, but also generate a model-based state prediction for the DEMPC once the data is identified as compromised. Then, an event-triggering mechanism is presented to reduce the computation and communication burdens of each area controller. Simulation studies of a four-area power system are conducted and the results validate the effectiveness of the proposed intrusion detection unit and event-triggering conditions for the DEMPC.

As communication networks are increasingly implemented to support the information exchange between microgrid control centers and/or local controllers, they expose microgrids to cyber-attack threats. This paper aims to analyze the stochastic stability of islanded microgrids in the presence of random denial of service (DoS) attack and propose a mode-dependent resilient controller to mitigate the influence of DoS attacks. Specifically, the small-signal model of the microgrid under the DoS attack is integrated as a stochastic jump system with state continuity disruptions. A new vulnerability metric is defined by using observability Gramians of the stochastic jump system, to measure the vulnerability of the system regarding DoS attack choices. The Lyapunov function analysis is conducted to find conditions sustaining the stochastic stability of the islanded microgrid in the form of linear matrix inequalities. A mode-dependent control approach is proposed for microgrids to mitigate the influence of random DoS attacks. In case studies, the vulnerability analysis and time-domain simulation results show the performance of the investigated microgrid can be degraded when the random DoS attacks exist. When the proposed mode-based secondary frequency controllers are installed, the islanded microgrid can sustain its stability during the attacking period and system dynamics rapidly converge when the DoS attack is over.