This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing...This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles(UAVs).Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks,leading to low capture efficiency and a significant decline in performance when the scale expands.To tackle these issues,based on the proximal policy optimization(PPO)algorithm,MA2PPO adopts the centralized training with decentralized execution(CTDE)framework and introduces a dynamic decoupling mechanism,that is,sharing the multi-head attention(MHA)mechanism for critics during centralized training to solve the credit assignment problem.This method enables the pursuers to identify highly correlated interactions with their teammates,effectively eliminate irrelevant and weakly relevant interactions,and decompose large-scale cooperation problems into decoupled sub-problems,thereby enhancing the collaborative efficiency and policy stability among multiple agents.Furthermore,a reward function has been devised to facilitate the pursuers to encircle the escapee by combining a formation reward with a distance reward,which incentivizes UAVs to develop sophisticated cooperative pursuit strategies.Experimental results demonstrate the effectiveness of the proposed algorithm in achieving multi-UAV cooperative pursuit and inducing diverse cooperative pursuit behaviors among UAVs.Moreover,experiments on scalability have demonstrated that the algorithm is suitable for large-scale multi-UAV systems.展开更多
In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies an...In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments.To improve the UAV-environment interaction efficiency,this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning(DRL).This algorithm integrates the Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),and Visual Navigation System(VNS)for comprehensive information fusion.Specifically,an improved multi-UAV integrated navigation algorithm called Information Fusion with MultiAgent Deep Deterministic Policy Gradient(IF-MADDPG)was developed.This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time.Through simulations and experiments,test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm.The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments.Additionally,it has advantages in terms of mission completion time.This study provides a novel approach for efficient collaboration in multi-UAV systems,which significantly improves the robustness and adaptability of navigation systems.展开更多
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w...Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.展开更多
This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of...This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer.First,a topologyrepairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources considerably.The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated.Second,a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed.Moreover,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given explicitly.The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.展开更多
This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre...This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.展开更多
This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on th...This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.展开更多
Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contempo...Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.展开更多
Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities ofte...Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities often result in the deprioritization of standardized management practices,as they do not yield immediate benefits.The implementation of such systems typically encompasses the integrated phases of "development,construction,utiliz ation,and operation and maintenance".To enhance the overall delivery quality of these systems,it is imperative to dismantle the management barriers among these phases and adopt a holistic approach to standardized management.This paper takes a specific system project as a research object to identify common challenges,and proposes improvement strategies in the implementation of standar dized management.Empirical results indicate a substantial reduction in the system s full-lifecycle costs.展开更多
Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for...Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for mitigating the energy crisis.A comprehensive review connecting the advancements in engineered radiative cooling systems(ERCSs),encompassing material and structural design as well as thermal and energy-related applications,is currently absent.Herein,this review begins with a concise summary of the essential concepts of ERCSs,followed by an introduction to engineered materials and structures,containing nature-inspired designs,chromatic materials,meta-structural configurations,and multilayered constructions.It subsequently encapsulates the primary applications,including thermal-regulating textiles and energy-saving devices.Next,it highlights the challenges of ERCSs,including maximized thermoregulatory effects,environmental adaptability,scalability and sustainability,and interdisciplinary integration.It seeks to offer direction for forthcoming fundamental research and industrial advancement of radiative cooling systems in real-world applications.展开更多
This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consen...This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consensus under average dwell time switching.Then sufficient conditions are derived to guarantee the positive consensus.The gain matrices of the control protocol are described using a matrix decomposition approach and the corresponding computational complexity is reduced by resorting to linear programming and co-positive Lyapunov functions.Finally,two numerical examples are provided to illustrate the results obtained.展开更多
The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention a...The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies.展开更多
Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transporta...Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems(ITS).This paper presents a systematic review of ILC's developmental progress,current methodologies,and practical implementations across these two critical domains.The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows.Through case studies,it evaluates demonstrated improvements in positioning accuracy,surface finish quality,and production throughput.Furthermore,the study examines ILC’s applications in ITS,with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking,platoon coordination,and traffic signal timing optimization,where its data-driven characteristics enhance adaptability to dynamic environments.Finally,the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields.展开更多
Malignant pleural effusion(MPE) is a serious disease caused by malignant tumors with high morbidity and mortality.Chemotherapy,immunotherapy,and antiangiogenic therapy are common treatments for MPE at present.However,...Malignant pleural effusion(MPE) is a serious disease caused by malignant tumors with high morbidity and mortality.Chemotherapy,immunotherapy,and antiangiogenic therapy are common treatments for MPE at present.However,traditional chemotherapeutic drugs have many side effects and can easily lead to drug resistance in patients.The complex tumor microenvironment(TME) of MPE directly reduces the antitumor efficacy of immunotherapy.Fortunately,drug delivery systems(DDSs) based on biomaterials have the ability to overcome some of the drawbacks of conventional treatments by improving drug stability,increasing the accuracy of tumor cell targeting,reducing toxic side effects,and remodeling TME,ultimately improving drug efficacy.Therefore,the purpose of this review is to provide an overview and discussion of the latest progress in biomaterial-based DDSs for the treatment of MPE.We discuss the application of biomaterials in the treatment of MPE from multiple perspectives,including chemotherapy,immunotherapy,combination therapy,and pleurodesis,where microspheres,cell membrane-derived microparticles(MPs),micelles,nanoparticles,and liposomes,are involved.The application of these biomaterials has been proven to have great potential in the treatment of MPE,providing a new idea for follow-up research.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation charact...This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation characteristics and potential fading powering faults.By performing deliberately designed coordinate transformations on the tracking error,the complex and demanding problem of“reaching specified precision within a given time”is transformed into a bounded control problem,facilitating the development of the control scheme.To enhance practicality,the design incorporates smooth function fitting and dynamic surface control techniques.Additionally,the proposed control algorithm is robust to faults,effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention.Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.展开更多
This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external di...This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.展开更多
Energy storage-equipped photovoltaic(PV-storage)systems can meet frequency regulation requirements under various operating conditions,and their coordinated support for grid frequency has become a future trend.To addre...Energy storage-equipped photovoltaic(PV-storage)systems can meet frequency regulation requirements under various operating conditions,and their coordinated support for grid frequency has become a future trend.To address frequency stability issues caused by low inertia and weak damping,this paper proposes a multi-timescale frequency regulation coordinated control strategy for PV-storage integrated systems.First,a self-synchronizing control strategy for grid-connected inverters is designed based on DC voltage dynamics,enabling active inertia support while transmitting frequency variation information.Next,an energy storage inertia support control strategy is developed to enhance the frequency nadir,and an active frequency support control strategy for PV system considering a frequency regulation deadband is proposed,where the deadband value is determined based on the power regulation margin of synchronous generators,allowing the PV-storage system to adaptively switch between inertia support and primary frequency regulation under different disturbance conditions.This approach ensures system frequency stability while fully leveraging the regulation capabilities of heterogeneous resources.Finally,the real-time digital simulation results of the PV-storage integrated system demonstrate that,compared to existing control methods,the proposed strategy effectively reduces the rate of change of frequency and improves the frequency nadir under various disturbance scenarios,verifying its effectiveness.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of th...In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems.展开更多
This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the ...This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the design process,we transform the studied system into the form of a fully actuated system through a state transformation.Then,to address the unknown nonlinear functions and actuator fault parameters,we employ neural networks and adaptive estimation techniques,respectively.Moreover,to reduce the control cost and improve the control efficiency,we introduce event-triggered inputs into the control strategy.It is proved by the Lyapunov stability analysis that all signals of the closed-loop system are bounded and the output of system eventually converge to a bounded region.The efficacy of the control approach is ultimately demonstrated via the simulation of an actual machine feeding system.展开更多
基金supported by the National Research and Development Program of China under Grant JCKY2018607C019in part by the Key Laboratory Fund of UAV of Northwestern Polytechnical University under Grant 2021JCJQLB0710L.
文摘This paper proposes a Multi-Agent Attention Proximal Policy Optimization(MA2PPO)algorithm aiming at the problems such as credit assignment,low collaboration efficiency and weak strategy generalization ability existing in the cooperative pursuit tasks of multiple unmanned aerial vehicles(UAVs).Traditional algorithms often fail to effectively identify critical cooperative relationships in such tasks,leading to low capture efficiency and a significant decline in performance when the scale expands.To tackle these issues,based on the proximal policy optimization(PPO)algorithm,MA2PPO adopts the centralized training with decentralized execution(CTDE)framework and introduces a dynamic decoupling mechanism,that is,sharing the multi-head attention(MHA)mechanism for critics during centralized training to solve the credit assignment problem.This method enables the pursuers to identify highly correlated interactions with their teammates,effectively eliminate irrelevant and weakly relevant interactions,and decompose large-scale cooperation problems into decoupled sub-problems,thereby enhancing the collaborative efficiency and policy stability among multiple agents.Furthermore,a reward function has been devised to facilitate the pursuers to encircle the escapee by combining a formation reward with a distance reward,which incentivizes UAVs to develop sophisticated cooperative pursuit strategies.Experimental results demonstrate the effectiveness of the proposed algorithm in achieving multi-UAV cooperative pursuit and inducing diverse cooperative pursuit behaviors among UAVs.Moreover,experiments on scalability have demonstrated that the algorithm is suitable for large-scale multi-UAV systems.
基金co-supported by the National Natural Science Foundation of China(Nos.92371201 and 52192633)the Natural Science Foundation of Shaanxi Province of China(No.2022JC-03)the Aeronautical Science Foundation of China(No.ASFC-20220019070002)。
文摘In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments.To improve the UAV-environment interaction efficiency,this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning(DRL).This algorithm integrates the Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),and Visual Navigation System(VNS)for comprehensive information fusion.Specifically,an improved multi-UAV integrated navigation algorithm called Information Fusion with MultiAgent Deep Deterministic Policy Gradient(IF-MADDPG)was developed.This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time.Through simulations and experiments,test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm.The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments.Additionally,it has advantages in terms of mission completion time.This study provides a novel approach for efficient collaboration in multi-UAV systems,which significantly improves the robustness and adaptability of navigation systems.
基金supported By Guangdong Major Project of Basic and Applied Basic Research(2023B0303000009)Guangdong Basic and Applied Basic Research Foundation(2024A1515030153,2025A1515011587)+1 种基金Project of Department of Education of Guangdong Province(2023ZDZX4046)Shen-zhen Natural Science Fund(Stable Support Plan Program 20231122121608001),Ningbo Municipal Science and Technology Bureau(ZX2024000604).
文摘Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors.
基金This work was supported in part by the National Natural Science Foundation of China(61903258)Guangdong Basic and Applied Basic Research Foundation(2022A1515010234)+1 种基金the Project of Department of Education of Guangdong Province(2022KTSCX105)Qatar National Research Fund(NPRP12C-0814-190012).
文摘This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer.First,a topologyrepairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources considerably.The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated.Second,a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed.Moreover,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given explicitly.The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.
基金supported by China NSF Grants(61631020)the Fundamental Research Funds for the Central Universities(NP2018103,NE2017103,NC2017003)
文摘This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.
文摘This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.
文摘Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.
文摘Large-scale complex systems are integral to the functioning of various organizations within the national economy.Despite their significance,the lengthy construction cycles and the involvement of multiple entities often result in the deprioritization of standardized management practices,as they do not yield immediate benefits.The implementation of such systems typically encompasses the integrated phases of "development,construction,utiliz ation,and operation and maintenance".To enhance the overall delivery quality of these systems,it is imperative to dismantle the management barriers among these phases and adopt a holistic approach to standardized management.This paper takes a specific system project as a research object to identify common challenges,and proposes improvement strategies in the implementation of standar dized management.Empirical results indicate a substantial reduction in the system s full-lifecycle costs.
基金support from the Contract Research(“Development of Breathable Fabrics with Nano-Electrospun Membrane”,CityU ref.:9231419“Research and application of antibacterial and healing-promoting smart nanofiber dressing for children’s burn wounds”,CityU ref:PJ9240111)+1 种基金the National Natural Science Foundation of China(“Study of Multi-Responsive Shape Memory Polyurethane Nanocomposites Inspired by Natural Fibers”,Grant No.51673162)Startup Grant of CityU(“Laboratory of Wearable Materials for Healthcare”,Grant No.9380116).
文摘Radiative cooling systems(RCSs)possess the distinctive capability to dissipate heat energy via solar and thermal radiation,making them suitable for thermal regulation and energy conservation applications,essential for mitigating the energy crisis.A comprehensive review connecting the advancements in engineered radiative cooling systems(ERCSs),encompassing material and structural design as well as thermal and energy-related applications,is currently absent.Herein,this review begins with a concise summary of the essential concepts of ERCSs,followed by an introduction to engineered materials and structures,containing nature-inspired designs,chromatic materials,meta-structural configurations,and multilayered constructions.It subsequently encapsulates the primary applications,including thermal-regulating textiles and energy-saving devices.Next,it highlights the challenges of ERCSs,including maximized thermoregulatory effects,environmental adaptability,scalability and sustainability,and interdisciplinary integration.It seeks to offer direction for forthcoming fundamental research and industrial advancement of radiative cooling systems in real-world applications.
基金supported by the National Natural Science Foundation of China(62463007,62463005)the Natural Science Foundation of Hainan Province(625RC710,625MS047)+1 种基金the System Control and Information Processing Education Ministry Key Laboratory Open Funding,China(Scip20240119)the Science Research Funding of Hainan University,China(KYQD(ZR)22180,KYQD(ZR)23180).
文摘This paper focuses on the leader-following positive consensus problems of heterogeneous switched multi-agent systems.First,a state-feedback controller with dynamic compensation is introduced to achieve positive consensus under average dwell time switching.Then sufficient conditions are derived to guarantee the positive consensus.The gain matrices of the control protocol are described using a matrix decomposition approach and the corresponding computational complexity is reduced by resorting to linear programming and co-positive Lyapunov functions.Finally,two numerical examples are provided to illustrate the results obtained.
基金National Natural Science Foundation of China,Grant/Award Number:82000102 and 82270112。
文摘The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies.
基金funded by the Wuxi Young Scientific and Technological Talent Support Initiative,project number:TJXD-2024-203the Natural Science Foundation of the Jiangsu Higher Education Institutions of China,grant number:24KJB470027.
文摘Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems(ITS).This paper presents a systematic review of ILC's developmental progress,current methodologies,and practical implementations across these two critical domains.The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows.Through case studies,it evaluates demonstrated improvements in positioning accuracy,surface finish quality,and production throughput.Furthermore,the study examines ILC’s applications in ITS,with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking,platoon coordination,and traffic signal timing optimization,where its data-driven characteristics enhance adaptability to dynamic environments.Finally,the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields.
基金financial support from the Noncommunicable Chronic Diseases-National Science and Technology Major Project (Nos.2024ZD0522800,2024ZD0522803)the National Natural Science Foundation of China (Nos.U21A20417,31930067,31800797)+2 种基金the Natural Science Foundation of Sichuan Province (No.2024NSFSC0046)the Sichuan Science and Technology Program (No.2022YFS0333)the 1·3·5 Project for Disciplines of Excellence,West China Hospital,Sichuan University (No.ZYGD24003)。
文摘Malignant pleural effusion(MPE) is a serious disease caused by malignant tumors with high morbidity and mortality.Chemotherapy,immunotherapy,and antiangiogenic therapy are common treatments for MPE at present.However,traditional chemotherapeutic drugs have many side effects and can easily lead to drug resistance in patients.The complex tumor microenvironment(TME) of MPE directly reduces the antitumor efficacy of immunotherapy.Fortunately,drug delivery systems(DDSs) based on biomaterials have the ability to overcome some of the drawbacks of conventional treatments by improving drug stability,increasing the accuracy of tumor cell targeting,reducing toxic side effects,and remodeling TME,ultimately improving drug efficacy.Therefore,the purpose of this review is to provide an overview and discussion of the latest progress in biomaterial-based DDSs for the treatment of MPE.We discuss the application of biomaterials in the treatment of MPE from multiple perspectives,including chemotherapy,immunotherapy,combination therapy,and pleurodesis,where microspheres,cell membrane-derived microparticles(MPs),micelles,nanoparticles,and liposomes,are involved.The application of these biomaterials has been proven to have great potential in the treatment of MPE,providing a new idea for follow-up research.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金supported in part by the National Natural Science Foundation of China(W2411061,624B2029)the Graduate Research and Innovation Foundation of Chongqing,China(CYS20069)+1 种基金the Fundamental Research Funds for the Central Universities(2024CDJYXTD-007)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0026).
文摘This paper proposes a fault-tolerant control scheme for Euler-Lagrange systems that ensures the tracking error decays to a pre-specified accuracy level within a prescribed time period,despite unknown actuation characteristics and potential fading powering faults.By performing deliberately designed coordinate transformations on the tracking error,the complex and demanding problem of“reaching specified precision within a given time”is transformed into a bounded control problem,facilitating the development of the control scheme.To enhance practicality,the design incorporates smooth function fitting and dynamic surface control techniques.Additionally,the proposed control algorithm is robust to faults,effectively handling a combination of fading powering faults and additive actuator faults without requiring additional human intervention.Numerical simulations on a two-link robotic manipulator verify the effectiveness of the proposed control algorithm.
基金supported in part by the Beijing Natural Science Foundation under Grant 4252050in part by the National Science Fund for Distinguished Young Scholars under Grant 62425304in part by the Basic Science Center Programs of NSFC under Grant 62088101.
文摘This paper investigates the consensus tracking control problem for high order nonlinear multi-agent systems subject to non-affine faults,partial measurable states,uncertain control coefficients,and unknown external disturbances.Under the directed topology conditions,an observer-based finite-time control strategy based on adaptive backstepping and is proposed,in which a neural network-based state observer is employed to approximate the unmeasurable system state variables.To address the complexity explosion problem associated with the backstepping method,a finite-time command filter is incorporated,with error compensation signals designed to mitigate the filter-induced errors.Additionally,the Butterworth low-pass filter is introduced to avoid the algebraic ring problem in the design of the controller.The finite-time stability of the closed-loop system is rigorously analyzed with the finite-time Lyapunov stability criterion,validating that all closed-loop signals of the system remain bounded within a finite time.Finally,the effectiveness of the proposed control strategy is verified through a simulation example.
基金supported by the State Grid Corporation of China under Grant for Science and Technology Projects(No.SGNXJYOOZWJS2500029).
文摘Energy storage-equipped photovoltaic(PV-storage)systems can meet frequency regulation requirements under various operating conditions,and their coordinated support for grid frequency has become a future trend.To address frequency stability issues caused by low inertia and weak damping,this paper proposes a multi-timescale frequency regulation coordinated control strategy for PV-storage integrated systems.First,a self-synchronizing control strategy for grid-connected inverters is designed based on DC voltage dynamics,enabling active inertia support while transmitting frequency variation information.Next,an energy storage inertia support control strategy is developed to enhance the frequency nadir,and an active frequency support control strategy for PV system considering a frequency regulation deadband is proposed,where the deadband value is determined based on the power regulation margin of synchronous generators,allowing the PV-storage system to adaptively switch between inertia support and primary frequency regulation under different disturbance conditions.This approach ensures system frequency stability while fully leveraging the regulation capabilities of heterogeneous resources.Finally,the real-time digital simulation results of the PV-storage integrated system demonstrate that,compared to existing control methods,the proposed strategy effectively reduces the rate of change of frequency and improves the frequency nadir under various disturbance scenarios,verifying its effectiveness.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
基金supported by the NSFC(12461012)and the NSF of Chongqing(CSTB2024NSCQ-MSX1246).
文摘In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems.
基金supported by the Science Center Program of National Natural Science Foundation of China under Grant 62188101the National Natural Science Foundation of China under Grant 62573265.
文摘This study develops an event-triggered control strategy utilizing the fully actuated system approach for nonlinear interconnected large-scale systems containing actuator failures.First,to reduce the complexity of the design process,we transform the studied system into the form of a fully actuated system through a state transformation.Then,to address the unknown nonlinear functions and actuator fault parameters,we employ neural networks and adaptive estimation techniques,respectively.Moreover,to reduce the control cost and improve the control efficiency,we introduce event-triggered inputs into the control strategy.It is proved by the Lyapunov stability analysis that all signals of the closed-loop system are bounded and the output of system eventually converge to a bounded region.The efficacy of the control approach is ultimately demonstrated via the simulation of an actual machine feeding system.
