Due to the extraordinary advantages,un-manned aerial vehicle(UAV)can be utilized as aerial base station(BS)to provide temporary and on-demand wireless connections for user equipments in the cover-age area.This article...Due to the extraordinary advantages,un-manned aerial vehicle(UAV)can be utilized as aerial base station(BS)to provide temporary and on-demand wireless connections for user equipments in the cover-age area.This article specifically considers the UAV-enabled orthogonal frequency division multiple access(OFDMA)wireless communication network.Consid-ering a practical scenario,a joint resource allocation and trajectory design optimization problem with the constraints on UAV mobility,limited total resource and backhaul link rate has been formulated,which aims to maximize the minimum achievable average rate of the users.To tackle the coupling and non-convexity of the proposed problem,an efficient opti-mization algorithm has been proposed based on alter-nating optimization,successive convex approximation and introducing slack variable techniques.Simulation results illustrate that the proposed optimization algo-rithm can effectively improve the system performance.Also,the numerical results unveil that joint optimiza-tion is superior to baseline schemes.展开更多
In this work,we consider an Unmanned Aerial Vehicle(UAV)aided covert edge computing architecture,where multiple sensors are scattered with a certain distance on the ground.The sensor can implement several computation ...In this work,we consider an Unmanned Aerial Vehicle(UAV)aided covert edge computing architecture,where multiple sensors are scattered with a certain distance on the ground.The sensor can implement several computation tasks.In an emergency scenario,the computational capabilities of sensors are often limited,as seen in vehicular networks or Internet of Things(IoT)networks.The UAV can be utilized to undertake parts of the computation tasks,i.e.,edge computing.While various studies have advanced the performance of UAV-based edge computing systems,the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature,yet it has not received adequate attention.In this paper,we improve the covert performance in a UAV aided edge computing system.Parts of the computation tasks of multiple ground sensors are offloaded to the UAV,where the sensors offload the computing tasks to the UAV,and Willie around detects the transmissions.The transmit power of sensors,the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance,we propose a deep reinforcement learning framework to jointly optimize them.The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint.Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.展开更多
As a novel signaling technology,the power splitting receiver(PSR)simultaneously employs both the coherent and non-coherent signal processing.In order to improve its communication performance,an intelligent reflecting ...As a novel signaling technology,the power splitting receiver(PSR)simultaneously employs both the coherent and non-coherent signal processing.In order to improve its communication performance,an intelligent reflecting surface(IRS)is introduced into its signal propagation path.Consequently,an IRSaided PSR is concerned for a point-to-point(P2P)data link,where both the single-antenna and multiantenna deployments on the receiver are discussed.We aim at maximizing the capacity of the concerned P2P data-link by jointly optimizing the passive beamforming of IRS and the splitting ratio of PSR,either in single-antenna or multi-antenna case.However,owing to the coupling of multiple variables,the optimization problems are non-convex and challenging,especially in the later multi-antenna case.The proposed alternating-approximating algorithm(A-A),aided by semi-definite relaxation(SDR)and successive convex approximation(SCA)methods,etc.,successfully overcomes these challenges.We compare the IRS-aided PSR system that optimized by our proposed algorithm to the systems without IRS or PSR,and the systems without joint optimization.The simulation results show that our proposal has a better performance.展开更多
Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppr...Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
[Objective]Under the combined impact of climate change and urbanization,urban rainstorm flood disasters occur frequently,seriously restricting urban safety and sustainable development.Relying on traditional grey infra...[Objective]Under the combined impact of climate change and urbanization,urban rainstorm flood disasters occur frequently,seriously restricting urban safety and sustainable development.Relying on traditional grey infrastructure such as pipe networks for urban stormwater management is not enough to deal with urban rainstorm flood disasters under extreme rainfall events.The integration of green,grey and blue systems(GGB-integrated system)is gradually gaining recognition in the field of global flood prevention.It is necessary to further clarify the connotation,technical and engineering implementation strategies of the GGB-integrated system,to provide support for the resilient city construction.[Methods]Through literature retrieval and analysis,the relevant research and progress related to the layout optimization and joint scheduling optimization of the GGBintegrated system were systematically reviewed.In response to existing limitations and future engineering application requirements,key supporting technologies including the utilization of overground emergency storage spaces,safety protection of underground important infrastructure and multi-departmental collaboration,were proposed.A layout optimization framework and a joint scheduling framework for the GGB-integrated system were also developed.[Results]Current research on layout optimization predominantly focuses on the integration of green system and grey system,with relatively fewer studies incorporating blue system infrastructure into the optimization process.Moreover,these studies tend to be on a smaller scale with simpler scenarios,which do not fully capture the complexity of real-world systems.Additionally,optimization objective tend to prioritize environmental and economic goals,while social and ecological factors are less frequently considered.Current research on joint scheduling optimization is often limited to small-scale plots,with insufficient attention paid to the entire system.There is a deficiency in method for real-time,automated determination of optimal control strategies for combinations of multiple system facilities based on actual rainfall-runoff processes.Additionally,the application of emergency facilities during extreme conditions is not sufficiently addressed.Furthermore,both layout optimization and joint scheduling optimization lack consideration of the mute feed effect of flood and waterlogging in urban,watershed and regional scales.[Conclusion]Future research needs to improve the theoretical framework for layout optimization and joint scheduling optimization of GGB-integrated system.Through the comprehensive application of the Internet of things,artificial intelligence,coupling model development,multi-scale analysis,multi-scenario simulation,and the establishment of multi-departmental collaboration mechanisms,it can enhance the flood resilience of urban areas in response to rainfall events of varying intensities,particularly extreme rainfall events.展开更多
Propeller design is a highly intricate and interdisciplinary task that necessitates careful trade-offs between radiated noise levels and aerodynamic efficiency.To achieve efficient trade-off designs,an enhanced on-the...Propeller design is a highly intricate and interdisciplinary task that necessitates careful trade-offs between radiated noise levels and aerodynamic efficiency.To achieve efficient trade-off designs,an enhanced on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is developed,which maintains the fidelity of the Navier-Stokes solution for unsteady flow and of the moving-medium Ffowcs Williams-Hawkings(FW-H)formulation for capturing tonal noise.Furthermore,this on-the-fly approach enables a unified architecture for discreteadjoint sensitivity analysis encompassing both aerodynamics and aeroacoustics,facilitating effective multi-objective weighted optimizations.Subsequently,this proposed methodology is applied to perform trade-off optimizations between aerodynamics and aeroacoustics for a propeller by employing varying weighting factors to comprehend their influence on optimal configurations.The results demonstrate a positive correlation between efficiency and noise sensitivities,and thus indicate an inherent synchronicity where pursing noise reduction through purely aeroacoustic optimization inevitably entails sacrificing aerodynamic efficiency.However,by effectively incorporating appropriate weighting factors(recommended to range from 0.25 to 0.5)into the multi-objective function combined with both aerodynamics and aeroacoustics,it becomes feasible to achieve efficiency enhancement and noise reduction simultaneously.Key findings show that reducing blade planform size and equipping“rotated-S”shaped airfoil profiles in the tip region can effectively restrain noise levels while maintaining aerodynamic performance.展开更多
The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless cove...The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.展开更多
In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication a...In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.展开更多
With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequ...With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequently,safeguarding com-munication information in the NTN has emerged as a critical challenge.To address this issue,we pro-pose a beamforming and horizontal trajectory joint op-timization method for unmanned aerial vehicle(UAV)covert communications in the NTN.First,we formu-late an optimization problem that considers constraints such as the transmitting power and the distance.More-over,we employ the integrated communication and jamming(ICAJ)signal as Alice’s transmitting signal,further protecting the content of communication in-formation.Next,we construct two subproblems,and we propose an alternate optimization(AO)algorithm based on quadratic transform and penalty term method to solve the proposed two subproblems.Simulation re-sults demonstrate that the proposed method is effective and has better performance than benchmarks.展开更多
This paper proposes the Unmanned Aerial Vehicle(UAV)-assisted Full-Duplex(FD)Integrated Sensing And Communication(ISAC)system.In this system,the UAV integrates sensing and communication functions,capable of receiving ...This paper proposes the Unmanned Aerial Vehicle(UAV)-assisted Full-Duplex(FD)Integrated Sensing And Communication(ISAC)system.In this system,the UAV integrates sensing and communication functions,capable of receiving transmission signals from Uplink(UL)users and echo signal from target,while communicating with Downlink(DL)users and simultaneously detecting target.With the objective of maximizing the Average Sum Rate(ASR)for both UL and DL users,a composite non-convex optimization problem is established,which is decomposed into sub-problems of communication scheduling optimization,transceiver beamforming design,and UAV trajectory optimization.An alternating iterative algorithm is proposed,employing relaxation optimization,extremum traversal search,augmented weighted minimum mean square error,and successive convex approximation methods to solve the aforementioned sub-problems.Simulation results demonstrate that,compared to the traditional UAV-assisted Half-Duplex(HD)ISAC scheme,the proposed FD ISAC scheme effectively improves the ASR.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta...Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown ...At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint ...The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...展开更多
基金supported by Project funded in part by China Postdoctoral Science Foundation(No.2021MD703980)in part by the National Natural Science Foundation of China(No.61901502).
文摘Due to the extraordinary advantages,un-manned aerial vehicle(UAV)can be utilized as aerial base station(BS)to provide temporary and on-demand wireless connections for user equipments in the cover-age area.This article specifically considers the UAV-enabled orthogonal frequency division multiple access(OFDMA)wireless communication network.Consid-ering a practical scenario,a joint resource allocation and trajectory design optimization problem with the constraints on UAV mobility,limited total resource and backhaul link rate has been formulated,which aims to maximize the minimum achievable average rate of the users.To tackle the coupling and non-convexity of the proposed problem,an efficient opti-mization algorithm has been proposed based on alter-nating optimization,successive convex approximation and introducing slack variable techniques.Simulation results illustrate that the proposed optimization algo-rithm can effectively improve the system performance.Also,the numerical results unveil that joint optimiza-tion is superior to baseline schemes.
基金co-supported by the National Natural Science Foundation of China(No.62271093)the Natural Science Foundation of Chongqing,China(No.CSTB2023NSCQ-LZX0108)the Chongqing Graduate Research Innovation Project,China(No.CYS23093).
文摘In this work,we consider an Unmanned Aerial Vehicle(UAV)aided covert edge computing architecture,where multiple sensors are scattered with a certain distance on the ground.The sensor can implement several computation tasks.In an emergency scenario,the computational capabilities of sensors are often limited,as seen in vehicular networks or Internet of Things(IoT)networks.The UAV can be utilized to undertake parts of the computation tasks,i.e.,edge computing.While various studies have advanced the performance of UAV-based edge computing systems,the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature,yet it has not received adequate attention.In this paper,we improve the covert performance in a UAV aided edge computing system.Parts of the computation tasks of multiple ground sensors are offloaded to the UAV,where the sensors offload the computing tasks to the UAV,and Willie around detects the transmissions.The transmit power of sensors,the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance,we propose a deep reinforcement learning framework to jointly optimize them.The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint.Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.
基金supported by National Key R&D Program of China with Grant number 2019YFB1803400in part by Sichuan Science and Technology Program under Grant 2024NSFSC0472。
文摘As a novel signaling technology,the power splitting receiver(PSR)simultaneously employs both the coherent and non-coherent signal processing.In order to improve its communication performance,an intelligent reflecting surface(IRS)is introduced into its signal propagation path.Consequently,an IRSaided PSR is concerned for a point-to-point(P2P)data link,where both the single-antenna and multiantenna deployments on the receiver are discussed.We aim at maximizing the capacity of the concerned P2P data-link by jointly optimizing the passive beamforming of IRS and the splitting ratio of PSR,either in single-antenna or multi-antenna case.However,owing to the coupling of multiple variables,the optimization problems are non-convex and challenging,especially in the later multi-antenna case.The proposed alternating-approximating algorithm(A-A),aided by semi-definite relaxation(SDR)and successive convex approximation(SCA)methods,etc.,successfully overcomes these challenges.We compare the IRS-aided PSR system that optimized by our proposed algorithm to the systems without IRS or PSR,and the systems without joint optimization.The simulation results show that our proposal has a better performance.
基金supported by Fundamental Research Funds for the Central Universities,CHD300102264715National Key Research and Development Program of China under Grant 2021YFA0716902Natural Science Basic Research Program of Shaanxi 2024JCYBMS-199。
文摘Young's modulus and Poisson's ratio are crucial parameters for reservoir characterization and rock brittleness evaluation.Conventional methods often rely on indirect computation or approximations of the Zoeppritz equations to estimate Young's modulus,which can introduce cumulative errors and reduce the accuracy of inversion results.To address these issues,this paper introduces the analytical solution of the Zoeppritz equation into the inversion process.The equation is re-derived and expressed in terms of Young's modulus,Poisson's ratio,and density.Within the Bayesian framework,we construct an objective function for the joint inversion of PP and PS waves.Traditional gradient-based algorithms often suffer from low precision and the computational complexity.In this study,we address limitations of conventional approaches related to low precision and complicated code by using Circle chaotic mapping,Levy flights,and Gaussian mutation to optimize the quantum particle swarm optimization(QPSO),named improved quantum particle swarm optimization(IQPSO).The IQPSO demonstrates superior global optimization capabilities.We test the proposed inversion method with both synthetic and field data.The test results demonstrate the proposed method's feasibility and effectiveness,indicating an improvement in inversion accuracy over traditional methods.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
文摘[Objective]Under the combined impact of climate change and urbanization,urban rainstorm flood disasters occur frequently,seriously restricting urban safety and sustainable development.Relying on traditional grey infrastructure such as pipe networks for urban stormwater management is not enough to deal with urban rainstorm flood disasters under extreme rainfall events.The integration of green,grey and blue systems(GGB-integrated system)is gradually gaining recognition in the field of global flood prevention.It is necessary to further clarify the connotation,technical and engineering implementation strategies of the GGB-integrated system,to provide support for the resilient city construction.[Methods]Through literature retrieval and analysis,the relevant research and progress related to the layout optimization and joint scheduling optimization of the GGBintegrated system were systematically reviewed.In response to existing limitations and future engineering application requirements,key supporting technologies including the utilization of overground emergency storage spaces,safety protection of underground important infrastructure and multi-departmental collaboration,were proposed.A layout optimization framework and a joint scheduling framework for the GGB-integrated system were also developed.[Results]Current research on layout optimization predominantly focuses on the integration of green system and grey system,with relatively fewer studies incorporating blue system infrastructure into the optimization process.Moreover,these studies tend to be on a smaller scale with simpler scenarios,which do not fully capture the complexity of real-world systems.Additionally,optimization objective tend to prioritize environmental and economic goals,while social and ecological factors are less frequently considered.Current research on joint scheduling optimization is often limited to small-scale plots,with insufficient attention paid to the entire system.There is a deficiency in method for real-time,automated determination of optimal control strategies for combinations of multiple system facilities based on actual rainfall-runoff processes.Additionally,the application of emergency facilities during extreme conditions is not sufficiently addressed.Furthermore,both layout optimization and joint scheduling optimization lack consideration of the mute feed effect of flood and waterlogging in urban,watershed and regional scales.[Conclusion]Future research needs to improve the theoretical framework for layout optimization and joint scheduling optimization of GGB-integrated system.Through the comprehensive application of the Internet of things,artificial intelligence,coupling model development,multi-scale analysis,multi-scenario simulation,and the establishment of multi-departmental collaboration mechanisms,it can enhance the flood resilience of urban areas in response to rainfall events of varying intensities,particularly extreme rainfall events.
基金supported by the National Science and Technology Major Project,China(No.Y2019-I-0018-0017)the National Natural Science Foundation of China(No.11602200)+1 种基金Hunan Innovative Province Construction Special Fund,China(No.2021GK1020)the Priority Academic Program Development of Jiangsu Higher Education Institutions,China。
文摘Propeller design is a highly intricate and interdisciplinary task that necessitates careful trade-offs between radiated noise levels and aerodynamic efficiency.To achieve efficient trade-off designs,an enhanced on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is developed,which maintains the fidelity of the Navier-Stokes solution for unsteady flow and of the moving-medium Ffowcs Williams-Hawkings(FW-H)formulation for capturing tonal noise.Furthermore,this on-the-fly approach enables a unified architecture for discreteadjoint sensitivity analysis encompassing both aerodynamics and aeroacoustics,facilitating effective multi-objective weighted optimizations.Subsequently,this proposed methodology is applied to perform trade-off optimizations between aerodynamics and aeroacoustics for a propeller by employing varying weighting factors to comprehend their influence on optimal configurations.The results demonstrate a positive correlation between efficiency and noise sensitivities,and thus indicate an inherent synchronicity where pursing noise reduction through purely aeroacoustic optimization inevitably entails sacrificing aerodynamic efficiency.However,by effectively incorporating appropriate weighting factors(recommended to range from 0.25 to 0.5)into the multi-objective function combined with both aerodynamics and aeroacoustics,it becomes feasible to achieve efficiency enhancement and noise reduction simultaneously.Key findings show that reducing blade planform size and equipping“rotated-S”shaped airfoil profiles in the tip region can effectively restrain noise levels while maintaining aerodynamic performance.
文摘The rapid evolution of Fifth-Generation(5G)networks and the strategic development of Sixth-Generation(6G)technologies have significantly advanced the implementation of air-ground integrated networks with seamless coverage.Unmanned Aerial Vehicles(UAVs),serving as high-mobility aerial platforms,are extensively utilized to enhance coverage in long-distance emergency communication scenarios.The resource-constrained communication environments in emergencies by classifying UAVs into swarm UAVs and relay UAVs as aerial communication nodes is inversitgated.A horizontal deployment strategy for swarm UAVs is formulated through K-means clustering algorithm optimization,while a vertical deployment scheme is established using convex optimization methods.The minimum-path trajectory planning for relay UAVs is optimized via the Particle Swarm Optimization(PSO)algorithm,enhancing communication reliability between UAV swarms and terrestrial base stations.A three-dimensional heterogeneous network architecture is realized by modeling spatial multi-hop relay links.Experimental results demonstrate that the proposed joint UAV relay optimization framework outperforms conventional algorithms in both coverage performance and relay capability during video stream transmission,achieving significant improvements in coverage enhancement and relay efficiency.This work provides technical foundations for constructing high-reliability air-ground cooperative systems in emergency communications.
基金supported by the National Key R&D Program of China (2023YFB2905605)the National Natural Science Foundation of China (62072229)。
文摘In this paper,we investigate a distributed multi-input multi-output and orthogonal frequency division multiplexing(MIMO-OFDM) dual-functional radar-communication(DFRC) system,which enables simultaneous communication and sensing in different subcarrier sets.To obtain the best tradeoff between communication and sensing performance,we first derive Cramer-Rao Bound(CRB) of targets in detection area,and then maximize the transmission rate by jointly optimizing the power/subcarriers allocation and the selection of radar receivers under the constraints of detection performance and total transmit power.To tackle the non-convex mixed integer programming problem,we decompose the original problem into a semidefinite programming(SDP) problem and a convex quadratic integer problem and solve them iteratively.The numerical results demonstrate the effectiveness of our proposed algorithm,as well as the performance improvement brought by optimizing radar receivers selection.
基金supported in part by the National Natural Science Foundation of China under Grant U2441250 and 62231027in part by Natural Science Basic Research Programof Shaanxi under Grant 2024JC-JCQN-63+2 种基金in part by InnovationCapability Support Program of Shaanxi under Grant2024RS-CXTD-01in part by New Technology Research University Cooperation Project under Grant SKX242010031in part by the FundamentalResearch Funds for the Central Universities and theInnovation Fund of Xidian University under GrantYJSJ25007.
文摘With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequently,safeguarding com-munication information in the NTN has emerged as a critical challenge.To address this issue,we pro-pose a beamforming and horizontal trajectory joint op-timization method for unmanned aerial vehicle(UAV)covert communications in the NTN.First,we formu-late an optimization problem that considers constraints such as the transmitting power and the distance.More-over,we employ the integrated communication and jamming(ICAJ)signal as Alice’s transmitting signal,further protecting the content of communication in-formation.Next,we construct two subproblems,and we propose an alternate optimization(AO)algorithm based on quadratic transform and penalty term method to solve the proposed two subproblems.Simulation re-sults demonstrate that the proposed method is effective and has better performance than benchmarks.
基金supported in part by Sub Project of National Key Research and Development Plan in 2020.NO.2020YFC1511704Beijing Information Science&Technology University.NO.2020KYNH212,NO.2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.62301058).
文摘This paper proposes the Unmanned Aerial Vehicle(UAV)-assisted Full-Duplex(FD)Integrated Sensing And Communication(ISAC)system.In this system,the UAV integrates sensing and communication functions,capable of receiving transmission signals from Uplink(UL)users and echo signal from target,while communicating with Downlink(DL)users and simultaneously detecting target.With the objective of maximizing the Average Sum Rate(ASR)for both UL and DL users,a composite non-convex optimization problem is established,which is decomposed into sub-problems of communication scheduling optimization,transceiver beamforming design,and UAV trajectory optimization.An alternating iterative algorithm is proposed,employing relaxation optimization,extremum traversal search,augmented weighted minimum mean square error,and successive convex approximation methods to solve the aforementioned sub-problems.Simulation results demonstrate that,compared to the traditional UAV-assisted Half-Duplex(HD)ISAC scheme,the proposed FD ISAC scheme effectively improves the ASR.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
基金funded by National Natural Science Foundation of China(Nos.12402142,11832013 and 11572134)Natural Science Foundation of Hubei Province(No.2024AFB235)+1 种基金Hubei Provincial Department of Education Science and Technology Research Project(No.Q20221714)the Opening Foundation of Hubei Key Laboratory of Digital Textile Equipment(Nos.DTL2023019 and DTL2022012).
文摘Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
文摘At present,energy consumption is one of the main bottlenecks in autonomous mobile robot development.To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments,this paper proposes an Attention-Enhanced Dueling Deep Q-Network(ADDueling DQN),which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework.A multi-objective reward function,centered on energy efficiency,is designed to comprehensively consider path length,terrain slope,motion smoothness,and obstacle avoidance,enabling optimal low-energy trajectory generation in 3D space from the source.The incorporation of a multihead attention mechanism allows the model to dynamically focus on energy-critical state features—such as slope gradients and obstacle density—thereby significantly improving its ability to recognize and avoid energy-intensive paths.Additionally,the prioritized experience replay mechanism accelerates learning from key decision-making experiences,suppressing inefficient exploration and guiding the policy toward low-energy solutions more rapidly.The effectiveness of the proposed path planning algorithm is validated through simulation experiments conducted in multiple off-road scenarios.Results demonstrate that AD-Dueling DQN consistently achieves the lowest average energy consumption across all tested environments.Moreover,the proposed method exhibits faster convergence and greater training stability compared to baseline algorithms,highlighting its global optimization capability under energy-aware objectives in complex terrains.This study offers an efficient and scalable intelligent control strategy for the development of energy-conscious autonomous navigation systems.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金National Natural Science Foundation of China (10872093)
文摘The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...
