As an advanced device for observing atmospheric winds,the spaceborne Doppler Asymmetric Spatial Heterodyne(DASH)interferometer also encounters challenges associated with phase distortion,par-ticularly in limb sounding...As an advanced device for observing atmospheric winds,the spaceborne Doppler Asymmetric Spatial Heterodyne(DASH)interferometer also encounters challenges associated with phase distortion,par-ticularly in limb sounding scenarios.This paper discusses interferogram modeling and phase distortion cor-rection techniques for spaceborne DASH interferometers.The modeling of phase distortion interferograms with and without Doppler shift for limb observation was conducted,and the effectiveness of the analytical expression was verified through numerical simulation.The simulation results indicate that errors propagate layer by layer while using the onion-peeling inversion algorithm to handle phase-distorted interferograms.In contrast,the phase distortion correction algorithm can achieve effective correction.This phase correction method can be successfully applied to correct phase distortions in the interferograms of the spaceborne DASH interferometer,providing a feasible solution to enhance its measurement accuracy.展开更多
It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sens...It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.展开更多
Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the d...Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the demand for high-quality multiplexers and demultiplexers.However,the criteria for ideal-mode multiplexers/demultiplexers,such as performance,scalability,compatibility,and ultra-compactness,have only partially been achieved using conventional bulky devices(e.g.,waveguides,grat-ings,and free space optics)—an issue that will substantially restrict the application of MDM techniques.Here,we present a neuro-meta-router(NMR)optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication,potentially offering scalability,com-patibility,and ultra-compactness.An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission,showcasing a capacity of up to 100 gigabits per second(Gbps)and a symbol error rate down to the order of 104,all achieved without any compensation technologies or correlation devices.Our work presents a paradigm that merges metasurfaces,fiber communications,and deep learning,with potential applications in intelligent metasurface-aided optical interconnection,as well as all-optical pat-tern recognition and classification.展开更多
Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by ...Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.展开更多
The performance of traditional regular Intelligent Reflecting Surface(IRS)improves as the number of IRS elements increases,but more reflecting elements lead to higher IRS power consumption and greater overhead of chan...The performance of traditional regular Intelligent Reflecting Surface(IRS)improves as the number of IRS elements increases,but more reflecting elements lead to higher IRS power consumption and greater overhead of channel estimation.The Irregular Intelligent Reflecting Surface(IIRS)can enhance the performance of the IRS as well as boost the system performance when the number of reflecting elements is limited.However,due to the lack of radio frequency chain in IRS,it is challenging for the Base Station(BS)to gather perfect Channel State Information(CSI),especially in the presence of Eavesdroppers(Eves).Therefore,in this paper we investigate the minimum transmit power problem of IIRS-aided Simultaneous Wireless Information and Power Transfer(SWIPT)secure communication system with imperfect CSI of BS-IIRS-Eves links,which is subject to the rate outage probability constraints of the Eves,the minimum rate constraints of the Information Receivers(IRs),the energy harvesting constraints of the Energy Receivers(ERs),and the topology matrix constraints.Afterward,the formulated nonconvex problem can be efficiently tackled by employing joint optimization algorithm combined with successive refinement method and adaptive topology design method.Simulation results demonstrate the effectiveness of the proposed scheme and the superiority of IIRS.展开更多
High-fidelity tactile rendering offers significant potential for improving the richness and immersion of touchscreen interactions.This study focuses on a quantitative description of tactile rendering fidelity using a ...High-fidelity tactile rendering offers significant potential for improving the richness and immersion of touchscreen interactions.This study focuses on a quantitative description of tactile rendering fidelity using a custom-designed hybrid electrovibration and mechanical vibration(HEM)device.An electrovibration and mechanical vibration(EMV)algorithm that renders 3D gratings with different physical heights was proposed and shown to achieve 81%accuracy in shape recognition.Models of tactile rendering fidelity were established based on the evaluation of the height discrimination threshold,and the psychophysical-physical relationships between the discrimination and reference heights were well described by a modification of Weber’s law,with correlation coefficients higher than 0.9.The physiological-physical relationship between the pulse firing rate and the physical stimulation voltage was modeled using the Izhikevich spiking model with a logarithmic relationship.展开更多
Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based classification.Existing methods mainly tackle this problem by reweighting the loss fun...Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based classification.Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier.However,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution.In this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed distributions.Nevertheless,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm.To tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(i.e.the magnitude of the classifier vector),feature norm(i.e.the magnitude of the feature vector),and cosine similarity between the classifier vector and feature vector.In this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature norm.Drawing from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm regularisation.Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space.The authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist 2018.The experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,that is,by improving upon the best-performing baselines on CIFAR-100-LT by 1.34,1.41,1.41 and 1.33,respectively.展开更多
Current research on reconfigurable parallel mechanisms(RPMs)primarily focuses on achieving limited configuration changes,while mechanisms capable of extensive mode switching with distinct motion branches remain challe...Current research on reconfigurable parallel mechanisms(RPMs)primarily focuses on achieving limited configuration changes,while mechanisms capable of extensive mode switching with distinct motion branches remain challenging to design.Conventional kinematotropic chains offer limited reconfigurability,underscoring the need for novel designs that enable broader operational adaptability.In this research,a novel diamond-like chain(DLC)with metamorphic units is proposed developed from generalized diamond kinematotropic chains.By altering the axes of the metamorphic units,the DLC realizes three distinct configurations,each corresponding to one of five motion branches characterized by bifurcation and metamorphic transitions.This DLC serves as the fundamental building block for constructing a reconfigurable hybrid limb.Using screw theory,the constraint properties of the limb in its five phases are analyzed and classified into three types:unconstrained limbs,limbs applying constraint forces,and limbs applying constraint couples.Based on this analysis,a RPM consisting of three reconfigurable limbs is developed.Its reconfigurability stems from the inherent bifurcation and metamorphic capabilities of the DLC-based limbs.This research introduces a RPM capable of controlled switching among ten distinct motion modes,with mobility ranging from three to six degrees of freedom.The proposed mechanism demonstrates high versatility and practical feasibility,offering a promising solution for applications requiring variable motion characteristics and adaptive performance.展开更多
With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications a...With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.展开更多
To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters.Furthermore,the ...To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters.Furthermore,the introduction of artificial intelligence(AI)facilitates the learning of the distribution characteristics of channel fingerprints,effectively addressing the uncertainties and unknown dynamic challenges in wireless link modeling.This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network(GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile users.Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy.Furthermore,this paper outlines the future development directions of PLA.展开更多
In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users t...In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the UAV.To solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based approaches.Specifically,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL approach.The simulation results show that both approaches can solve the proposed optimization problem and obtain good results.Moreover,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.展开更多
In this study,we investigate the potential of mark-weighted angular correlation functions,which integrateβ-cosmic-web classification with angular correlation function analysis to improve cosmological constraints.Usin...In this study,we investigate the potential of mark-weighted angular correlation functions,which integrateβ-cosmic-web classification with angular correlation function analysis to improve cosmological constraints.Using SDSS DR12 CMASS-NGC galaxies and mock catalogs withΩ_(m)varying from 0.25 to 0.40,we assess the discriminative power of different statistics via the average improvement in chi-squared,ΔX^(2),across six redshift bins.This metric quantifies how effectively each statistic distinguishes between different cosmological models.Incorporating cosmic-web weights leads to substantial improvements.Using statistics weighted by the mean neighbor distance(Dnei)increasesΔX^(2)by approximately 40%–130%,while applying inverse mean neighbor distance weighting(1/Dnei)yields even larger gains,boostingΔX^(2)by a factor of 2–3 compared to traditional unweighted angular statistics.These enhancements are consistent with previous 3D clustering results,demonstrating the superior sensitivity of theβ-weighted approaches.Our method,based on thin redshift slices,is particularly suitable for slitless surveys(e.g.,Euclid,CSST)where redshift uncertainties limit 3D analyses.This study also offers a framework for applying marked statistics to 2D angular clustering.展开更多
Computational solid mechanics has become an indispensable approach in engineering,and numerical investigation of fracturing in composites is essential,as composites are widely used in structural applications.Crack evo...Computational solid mechanics has become an indispensable approach in engineering,and numerical investigation of fracturing in composites is essential,as composites are widely used in structural applications.Crack evolution in composites is the path to elucidating the relationship between microstructures and fracture performance,but crack-based finite-element methods are computationally expensive and time-consuming,which limits their application in computation-intensive scenarios.Consequently,this study proposes a deep learning framework called Crack-Net for instant prediction of the dynamic crack growth process,as well as its strain-stress curve.Specifically,Crack-Net introduces an implicit constraint technique,which incorporates the relationship between crack evolution and stress response into the network architecture.This technique substantially reduces data requirements while improving predictive accuracy.The transfer learning technique enables Crack-Net to handle composite materials with reinforcements of different strengths.Trained on high-accuracy fracture development datasets from phase field simulations,the proposed framework is capable of tackling intricate scenarios,involving materials with diverse interfaces,varying initial conditions,and the intricate elastoplastic fracture process.The proposed Crack-Net holds great promise for practical applications in engineering and materials science,in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design.展开更多
In the context of current global warming,understanding urban thermal resilience(UTR)dynamics across dif-ferent climatic zones is crucial.This study aims to examine the complex interactions among urban morphology,green...In the context of current global warming,understanding urban thermal resilience(UTR)dynamics across dif-ferent climatic zones is crucial.This study aims to examine the complex interactions among urban morphology,green-blue infrastructure,and climate factors affecting UTR.Moving beyond traditional methods that compare urban and rural thermal differences,our research innovatively measures UTR by evaluating urban disturbances caused by extreme thermal events.To improve accuracy and reliability,we utilize an AI-powered Monte Carlo Simulation framework.Our findings emphasize the critical role of blue-green spaces in boosting UTR,whereas urban morphology often has a suppressive impact.Additionally,atmospheric humidity is identified as a critical factor affecting UTR.The study interestingly finds varied climatic responses:dense urban areas enhance resilience in arid and cold regions but reduce it in tropical and temperate zones.These findings highlight the need for a balance between sustainable urban living and infrastructure development.展开更多
Dear Editor,People are often optimistic when forecasting the future,such that they believe they are less likely to encounter adverse life events.This phenomenon,known as optimism bias,emerges due to preferential encod...Dear Editor,People are often optimistic when forecasting the future,such that they believe they are less likely to encounter adverse life events.This phenomenon,known as optimism bias,emerges due to preferential encoding and consolidation of desirable over undesirable information[1,2],engaging frontal brain regions such as the anterior cingulate cortex and inferior frontal gyrus[1].An optimism bias is instrumental in supporting mental health:reduction or absence of optimism biases is associated with mood disorders such as depression,characterized by pessimistic thinking about the future[3].This relationship highlights the potential value of interventions that could enhance optimism bias[4].展开更多
In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during th...In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level flight.Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is employed.Particle Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are compared.In addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control system.The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.展开更多
This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft...This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform.Secondly,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be obtained.Then,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats.Finally,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by TLPIO.Significantly,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization algorithm.Besides,the convergence and time complexity of TLPIO are discussed.Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO.In the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive conditions.The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.展开更多
The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and productio...The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.展开更多
THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:&...THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".展开更多
The rendezvous and proximity operations with respect to a tumbling non-cooperative target pose high requirement for the position and attitude control accuracy of servicing spacecraft.However,multiple disturbances incl...The rendezvous and proximity operations with respect to a tumbling non-cooperative target pose high requirement for the position and attitude control accuracy of servicing spacecraft.However,multiple disturbances including parametric uncertainties,flexible vibration,and unknown nonlinear dynamics degrade the control performance significantly.In order to enhance the system anti-disturbance ability,this paper proposes a composite anti-disturbance control law for the spacecraft position and attitude tracking.Firstly,the relative position and attitude dynamic models with multiple disturbances are established,where the refined descriptions of multiple disturbances are accomplished based on their characteristics.Then,by combining a dual Disturbance ObserverBased Control(DOBC)and a sliding mode control,a composite controller with hierarchical architecture is proposed,where the dual DOBC in the feedforward channel is used to reject the flexible vibration,environment disturbance,and complicated nonlinear dynamics,while the parametric uncertainties are attenuated by the sliding mode control in the feedback channel.Stability analysis is carried out for the closed-loop system by unifying the sliding mode dynamics and observer dynamics.Finally,the effectiveness of the proposed controller is verified via numerical simulation and hardware-in-the-loop test.展开更多
文摘As an advanced device for observing atmospheric winds,the spaceborne Doppler Asymmetric Spatial Heterodyne(DASH)interferometer also encounters challenges associated with phase distortion,par-ticularly in limb sounding scenarios.This paper discusses interferogram modeling and phase distortion cor-rection techniques for spaceborne DASH interferometers.The modeling of phase distortion interferograms with and without Doppler shift for limb observation was conducted,and the effectiveness of the analytical expression was verified through numerical simulation.The simulation results indicate that errors propagate layer by layer while using the onion-peeling inversion algorithm to handle phase-distorted interferograms.In contrast,the phase distortion correction algorithm can achieve effective correction.This phase correction method can be successfully applied to correct phase distortions in the interferograms of the spaceborne DASH interferometer,providing a feasible solution to enhance its measurement accuracy.
文摘It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.
基金supported by the National Key Research and Development Program of China(2023YFB2804704)the National Natural Science Foundation of China(12174292,12374278,and 62105250).
文摘Advancements in mode-division multiplexing(MDM)techniques,aimed at surpassing the Shannon limit and augmenting transmission capacity,have garnered significant attention in optical fiber communica-tion,propelling the demand for high-quality multiplexers and demultiplexers.However,the criteria for ideal-mode multiplexers/demultiplexers,such as performance,scalability,compatibility,and ultra-compactness,have only partially been achieved using conventional bulky devices(e.g.,waveguides,grat-ings,and free space optics)—an issue that will substantially restrict the application of MDM techniques.Here,we present a neuro-meta-router(NMR)optimized through deep learning that achieves spatial multi-mode division and supports multi-channel communication,potentially offering scalability,com-patibility,and ultra-compactness.An MDM communication system based on an NMR is theoretically designed and experimentally demonstrated to enable simultaneous and independent multi-dataset transmission,showcasing a capacity of up to 100 gigabits per second(Gbps)and a symbol error rate down to the order of 104,all achieved without any compensation technologies or correlation devices.Our work presents a paradigm that merges metasurfaces,fiber communications,and deep learning,with potential applications in intelligent metasurface-aided optical interconnection,as well as all-optical pat-tern recognition and classification.
基金supported by the Key Program of the National Natural Science Foundation of China(Grant No.62031013)Guangdong Province Key Construction Discipline Scientific Research Capacity Improvement Project(Grant No.2022ZDJS117).
文摘Nonlinear transforms have significantly advanced learned image compression(LIC),particularly using residual blocks.This transform enhances the nonlinear expression ability and obtain compact feature representation by enlarging the receptive field,which indicates how the convolution process extracts features in a high dimensional feature space.However,its functionality is restricted to the spatial dimension and network depth,limiting further improvements in network performance due to insufficient information interaction and representation.Crucially,the potential of high dimensional feature space in the channel dimension and the exploration of network width/resolution remain largely untapped.In this paper,we consider nonlinear transforms from the perspective of feature space,defining high-dimensional feature spaces in different dimensions and investigating the specific effects.Firstly,we introduce the dimension increasing and decreasing transforms in both channel and spatial dimensions to obtain high dimensional feature space and achieve better feature extraction.Secondly,we design a channel-spatial fusion residual transform(CSR),which incorporates multi-dimensional transforms for a more effective representation.Furthermore,we simplify the proposed fusion transform to obtain a slim architecture(CSR-sm),balancing network complexity and compression performance.Finally,we build the overall network with stacked CSR transforms to achieve better compression and reconstruction.Experimental results demonstrate that the proposed method can achieve superior ratedistortion performance compared to the existing LIC methods and traditional codecs.Specifically,our proposed method achieves 9.38%BD-rate reduction over VVC on Kodak dataset.
基金supported in part by the Shenzhen Basic Research Program under Grant JCYJ20220531103008018,and Grants 20231120142345001 and 20231127144045001the Natural Science Foundation of China under Grant U20A20156.
文摘The performance of traditional regular Intelligent Reflecting Surface(IRS)improves as the number of IRS elements increases,but more reflecting elements lead to higher IRS power consumption and greater overhead of channel estimation.The Irregular Intelligent Reflecting Surface(IIRS)can enhance the performance of the IRS as well as boost the system performance when the number of reflecting elements is limited.However,due to the lack of radio frequency chain in IRS,it is challenging for the Base Station(BS)to gather perfect Channel State Information(CSI),especially in the presence of Eavesdroppers(Eves).Therefore,in this paper we investigate the minimum transmit power problem of IIRS-aided Simultaneous Wireless Information and Power Transfer(SWIPT)secure communication system with imperfect CSI of BS-IIRS-Eves links,which is subject to the rate outage probability constraints of the Eves,the minimum rate constraints of the Information Receivers(IRs),the energy harvesting constraints of the Energy Receivers(ERs),and the topology matrix constraints.Afterward,the formulated nonconvex problem can be efficiently tackled by employing joint optimization algorithm combined with successive refinement method and adaptive topology design method.Simulation results demonstrate the effectiveness of the proposed scheme and the superiority of IIRS.
基金Supported by the National Natural Science Foundation of China under Grants 61631010 and 61806085.
文摘High-fidelity tactile rendering offers significant potential for improving the richness and immersion of touchscreen interactions.This study focuses on a quantitative description of tactile rendering fidelity using a custom-designed hybrid electrovibration and mechanical vibration(HEM)device.An electrovibration and mechanical vibration(EMV)algorithm that renders 3D gratings with different physical heights was proposed and shown to achieve 81%accuracy in shape recognition.Models of tactile rendering fidelity were established based on the evaluation of the height discrimination threshold,and the psychophysical-physical relationships between the discrimination and reference heights were well described by a modification of Weber’s law,with correlation coefficients higher than 0.9.The physiological-physical relationship between the pulse firing rate and the physical stimulation voltage was modeled using the Izhikevich spiking model with a logarithmic relationship.
基金National Key Research and Development Program of China,Grant/Award Numbers:2022YFB3103900,2023YFB3106504Major Key Project of PCL,Grant/Award Numbers:PCL2022A03,PCL2023A09+5 种基金Shenzhen Basic Research,Grant/Award Number:JCYJ20220531095214031Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies,Grant/Award Number:2022B1212010005Shenzhen International Science and Technology Cooperation Project,Grant/Award Number:GJHZ20220913143008015Natural Science Foundation of Guangdong Province,Grant/Award Number:2023A1515011959Shenzhen-Hong Kong Jointly Funded Project,Grant/Award Number:SGDX20230116091246007Shenzhen Science and Technology Program,Grant/Award Numbers:RCBS20221008093131089,ZDSYS20210623091809029。
文摘Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based classification.Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier.However,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution.In this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed distributions.Nevertheless,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm.To tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(i.e.the magnitude of the classifier vector),feature norm(i.e.the magnitude of the feature vector),and cosine similarity between the classifier vector and feature vector.In this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature norm.Drawing from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm regularisation.Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space.The authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist 2018.The experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,that is,by improving upon the best-performing baselines on CIFAR-100-LT by 1.34,1.41,1.41 and 1.33,respectively.
基金Supported by National Natural Science Foundation of China(Grant No.52175019)Beijing Municipal Natural Science Foundation(Grant Nos.L222038,20240484699)+1 种基金Joint Funds of Industry-university-research of Shanghai Academy of Spaceflight Technology(Grant No.SAST2022-017)Beijing Municipal Key Laboratory of Space-ground Interconnection and Convergence of China and Key Laboratory of IoT Monitoring and Early Warning,Ministry of Emergency Management,Project‘Vice President of Science and Technology’of Changping District,Beijing.
文摘Current research on reconfigurable parallel mechanisms(RPMs)primarily focuses on achieving limited configuration changes,while mechanisms capable of extensive mode switching with distinct motion branches remain challenging to design.Conventional kinematotropic chains offer limited reconfigurability,underscoring the need for novel designs that enable broader operational adaptability.In this research,a novel diamond-like chain(DLC)with metamorphic units is proposed developed from generalized diamond kinematotropic chains.By altering the axes of the metamorphic units,the DLC realizes three distinct configurations,each corresponding to one of five motion branches characterized by bifurcation and metamorphic transitions.This DLC serves as the fundamental building block for constructing a reconfigurable hybrid limb.Using screw theory,the constraint properties of the limb in its five phases are analyzed and classified into three types:unconstrained limbs,limbs applying constraint forces,and limbs applying constraint couples.Based on this analysis,a RPM consisting of three reconfigurable limbs is developed.Its reconfigurability stems from the inherent bifurcation and metamorphic capabilities of the DLC-based limbs.This research introduces a RPM capable of controlled switching among ten distinct motion modes,with mobility ranging from three to six degrees of freedom.The proposed mechanism demonstrates high versatility and practical feasibility,offering a promising solution for applications requiring variable motion characteristics and adaptive performance.
基金supported by Future Network Scientific Research Fund Project (FNSRFP-2021-ZD-4)National Natural Science Foundation of China (No.61991404,61902182)+1 种基金National Key Research and Development Program of China under Grant 2020YFB1600104Key Research and Development Plan of Jiangsu Province under Grant BE2020084-2。
文摘With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay.
文摘To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters.Furthermore,the introduction of artificial intelligence(AI)facilitates the learning of the distribution characteristics of channel fingerprints,effectively addressing the uncertainties and unknown dynamic challenges in wireless link modeling.This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network(GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile users.Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy.Furthermore,this paper outlines the future development directions of PLA.
基金supported in part by the Shenzhen Basic Research Project under Grant JCYJ20220531103008018 and Grant 20200812112423002in part by the Guangdong Basic Research Program under Grant 2019A1515110358,2021A1515012097in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University (No.2021D16)。
文摘In this paper,we investigate the application of the Unmanned Aerial Vehicle(UAV)-enabled relaying system in emergency communications,where one UAV is applied as a relay to help transmit information from ground users to a Base Station(BS).We maximize the total transmitted data from the users to the BS,by optimizing the user communication scheduling and association along with the power allocation and the trajectory of the UAV.To solve this non-convex optimization problem,we propose the traditional Convex Optimization(CO)and the Reinforcement Learning(RL)-based approaches.Specifically,we apply the block coordinate descent and successive convex approximation techniques in the CO approach,while applying the soft actor-critic algorithm in the RL approach.The simulation results show that both approaches can solve the proposed optimization problem and obtain good results.Moreover,the RL approach establishes emergency communications more rapidly than the CO approach once the training process has been completed.
基金supported by the Ministry of Science and Technology of China(2020SKA0110401,2020SKA0110402 and 2020SKA0110100)the National Key Research and Development Program of China(2018YFA0404504,2018YFA0404601 and 2020YFC2201600)+2 种基金the National Natural Science Foundation of China(12373005,11890691,12205388,12220101003 and 12473097)the China Manned Space Project with numbers CMS-CSST-2021(A02,A03,B01)Guangdong Basic and Applied Basic Research Foundation(2024A1515012309)。
文摘In this study,we investigate the potential of mark-weighted angular correlation functions,which integrateβ-cosmic-web classification with angular correlation function analysis to improve cosmological constraints.Using SDSS DR12 CMASS-NGC galaxies and mock catalogs withΩ_(m)varying from 0.25 to 0.40,we assess the discriminative power of different statistics via the average improvement in chi-squared,ΔX^(2),across six redshift bins.This metric quantifies how effectively each statistic distinguishes between different cosmological models.Incorporating cosmic-web weights leads to substantial improvements.Using statistics weighted by the mean neighbor distance(Dnei)increasesΔX^(2)by approximately 40%–130%,while applying inverse mean neighbor distance weighting(1/Dnei)yields even larger gains,boostingΔX^(2)by a factor of 2–3 compared to traditional unweighted angular statistics.These enhancements are consistent with previous 3D clustering results,demonstrating the superior sensitivity of theβ-weighted approaches.Our method,based on thin redshift slices,is particularly suitable for slitless surveys(e.g.,Euclid,CSST)where redshift uncertainties limit 3D analyses.This study also offers a framework for applying marked statistics to 2D angular clustering.
基金supported and partially funded by the National Natural Science Foundation of China(52288101)the China Postdoctoral Science Foundation(2024M761535)supported by the High Performance Computing Centers at Eastern Institute of Technology,Ningbo,and Ningbo Institute of Digital Twin.
文摘Computational solid mechanics has become an indispensable approach in engineering,and numerical investigation of fracturing in composites is essential,as composites are widely used in structural applications.Crack evolution in composites is the path to elucidating the relationship between microstructures and fracture performance,but crack-based finite-element methods are computationally expensive and time-consuming,which limits their application in computation-intensive scenarios.Consequently,this study proposes a deep learning framework called Crack-Net for instant prediction of the dynamic crack growth process,as well as its strain-stress curve.Specifically,Crack-Net introduces an implicit constraint technique,which incorporates the relationship between crack evolution and stress response into the network architecture.This technique substantially reduces data requirements while improving predictive accuracy.The transfer learning technique enables Crack-Net to handle composite materials with reinforcements of different strengths.Trained on high-accuracy fracture development datasets from phase field simulations,the proposed framework is capable of tackling intricate scenarios,involving materials with diverse interfaces,varying initial conditions,and the intricate elastoplastic fracture process.The proposed Crack-Net holds great promise for practical applications in engineering and materials science,in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design.
基金financed by‘Data Analysis of Thermal Environment and Low-Carbon Intelligent Optimization Design of Urban Ecological Layout’s Impact’under National Natural Science Foundation of China(Grant No.524B200113)‘Basic Theory of Sustainable Urban Planning,Construction,and Governance’under the 14th Five-Year Plan of the State Key Research and Development Program of the People’s Republic of China(Grant No.2022YFC3800205)+1 种基金‘Key Technologies for Regional Carbon Neutral Mega-City Planning and Design’under Shanghai Science and Technology Support Program for Carbon(Grant No.22DZ1207800)Shanghai Intelligent Science and Technology IV Summit Discipline‘Cross-Innovation Science and Education Integration Fund’.
文摘In the context of current global warming,understanding urban thermal resilience(UTR)dynamics across dif-ferent climatic zones is crucial.This study aims to examine the complex interactions among urban morphology,green-blue infrastructure,and climate factors affecting UTR.Moving beyond traditional methods that compare urban and rural thermal differences,our research innovatively measures UTR by evaluating urban disturbances caused by extreme thermal events.To improve accuracy and reliability,we utilize an AI-powered Monte Carlo Simulation framework.Our findings emphasize the critical role of blue-green spaces in boosting UTR,whereas urban morphology often has a suppressive impact.Additionally,atmospheric humidity is identified as a critical factor affecting UTR.The study interestingly finds varied climatic responses:dense urban areas enhance resilience in arid and cold regions but reduce it in tropical and temperate zones.These findings highlight the need for a balance between sustainable urban living and infrastructure development.
基金supported by the Ministry of Science and Technology of China STI2030-Major Projects(2022ZD0214100)the National Natural Science Foundation of China(32171056 and 82272114)+2 种基金the General Research Fund of Hong Kong Research Grants Council(17614922)the Shenzhen Soft Science Research Program Project(RKX20220705152815035)the Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions(2023SHIBS0003).
文摘Dear Editor,People are often optimistic when forecasting the future,such that they believe they are less likely to encounter adverse life events.This phenomenon,known as optimism bias,emerges due to preferential encoding and consolidation of desirable over undesirable information[1,2],engaging frontal brain regions such as the anterior cingulate cortex and inferior frontal gyrus[1].An optimism bias is instrumental in supporting mental health:reduction or absence of optimism biases is associated with mood disorders such as depression,characterized by pessimistic thinking about the future[3].This relationship highlights the potential value of interventions that could enhance optimism bias[4].
基金supported by Science and Technology Innovation 2030-Key Project of"New Generation Artificial Intelli-gence",China(No.2018AAA0100803)National Natural Science Foundation of China(Nos.U20B2071,91948204,U1913602)Aeronautical Foundation of China(No.20185851022).
文摘In this paper.Active Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level flight.Considering the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is employed.Particle Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are compared.In addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control system.The effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.
基金the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”(2018AAA0100803)the National Natural Science Foundation of China(U20B2071,91948204,T2121003,U1913602)。
文摘This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform.Secondly,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be obtained.Then,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats.Finally,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by TLPIO.Significantly,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization algorithm.Besides,the convergence and time complexity of TLPIO are discussed.Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO.In the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive conditions.The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.
基金financially supported by the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(No.61860206014)the Basic Science Research Center Program of National Natural Science Foundation of China(No.61988101)+2 种基金National Key Research and Development Program(No.2020YFB1713700)National Natural Science Foundation of China(Nos.61973321 and 62073342)Science and Technology Innovation Program of Hunan Province(No.2021RC4054).
文摘The nonferrous metallurgical(NFM)industry is a cornerstone industry for a nation’s economy.With the development of artificial technologies and high requirements on environment protection,product quality,and production efficiency,the importance of applying smart manufacturing technologies to comprehensively percept production states and intelligently optimize process operations is becoming widely recognized by the industry.As a brief summary of the smart and optimal manufacturing of the NFM industry,this paper first reviews the research progress on some key facets of the operational optimization of NFM processes,including production and management,blending optimization,modeling,process monitoring,optimization,and control.Then,it illustrates the perspectives of smart and optimal manufacturing of the NFM industry.Finally,it discusses the major research directions and challenges of smart and optimal manufacturing for the NFM industry.This paper will lay a foundation for the realization of smart and optimal manufacturing in nonferrous metallurgy in the future.
基金partially supported by the National Key R&D Program of China(2020YFB2104001)the National Natural Science Foundation of China(62271485,61903363,62203250,U1811463)。
文摘THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".
基金supported by the China National Postdoctoral Program for Innovative Talents(No.BX20200031)the National Natural Science Foundation of China(Nos.62103013,61633003,61973012)the Program for Changjiang Scholars and Innovative Research Team,China(No.IRT 16R03).
文摘The rendezvous and proximity operations with respect to a tumbling non-cooperative target pose high requirement for the position and attitude control accuracy of servicing spacecraft.However,multiple disturbances including parametric uncertainties,flexible vibration,and unknown nonlinear dynamics degrade the control performance significantly.In order to enhance the system anti-disturbance ability,this paper proposes a composite anti-disturbance control law for the spacecraft position and attitude tracking.Firstly,the relative position and attitude dynamic models with multiple disturbances are established,where the refined descriptions of multiple disturbances are accomplished based on their characteristics.Then,by combining a dual Disturbance ObserverBased Control(DOBC)and a sliding mode control,a composite controller with hierarchical architecture is proposed,where the dual DOBC in the feedforward channel is used to reject the flexible vibration,environment disturbance,and complicated nonlinear dynamics,while the parametric uncertainties are attenuated by the sliding mode control in the feedback channel.Stability analysis is carried out for the closed-loop system by unifying the sliding mode dynamics and observer dynamics.Finally,the effectiveness of the proposed controller is verified via numerical simulation and hardware-in-the-loop test.
