3-D rigid visco-plastic finite element method (FEM) is used in the analysisof metal forming processes, including strip and plate rolling, shape rolling, slab edging, specialstrip rolling. The shifted incomplete Choles...3-D rigid visco-plastic finite element method (FEM) is used in the analysisof metal forming processes, including strip and plate rolling, shape rolling, slab edging, specialstrip rolling. The shifted incomplete Cholesky decomposition of the stiffness matrix with thesolution of the equations for velocity increment by the conjugate gradient method is combined. Thistechnique, termed the shifted ICCG method, is then employed to solve the slab edging problem. Theperformance of this algorithm in terms of the number of iterations, friction variation, shiftedparameter psi and the results of simulation for processing parameters are analysed. Numerical testsand application of this technique verify the efficiency and stability of the shifted ICCG method inthe analysis of slab edging.展开更多
Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential in...Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.展开更多
Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
Superior strength and high-temperature performance make γ-TiAl vital for lightweight aero-engines. However, its inherent brittleness poses machining problems. This study employed Elliptical Ultrasonic Vibration Milli...Superior strength and high-temperature performance make γ-TiAl vital for lightweight aero-engines. However, its inherent brittleness poses machining problems. This study employed Elliptical Ultrasonic Vibration Milling (EUVM) to address these problems. Considering the influence of machining parameters on vibration patterns of EUVM, a separation time model was established to analyze the vibration evolutionary process, thereby instructing the cutting mechanism. On this basis, deep discussions regarding chip formation, cutting force, edge breakage, and subsurface layer deformation were conducted for EUVM and Conventional Milling (CM). Chip morphology showed the chip formation was rooted in the periodic brittle fracture. Local dimples proved that the thermal effect of high-speed cutting improved the plasticity of γ-TiAl. EUVM achieved a maximum 18.17% reduction in cutting force compared with CM. The force variation mechanism differed with changes in the cutting speed or the vibration amplitude, and its correlation with thermal softening, strain hardening, and vibratory cutting effects was analyzed. EUVM attained desirable edge breakage by achieving smaller fracture lengths. The fracture mechanisms of different phases were distinct, causing a surge in edge fracture size of γ-TiAl under microstructural differences. In terms of subsurface deformation, EUVM also showed strengthening effects. Noteworthy, the lamellar deformation patterns under the cutting removal state differed from the quasi-static, which was categorized by the orientation angles. Additionally, the electron backscattering diffraction provided details of the influence of microstructural difference on the orientation and the deformation of grains in the subsurface layer. The results demonstrate that EUVM is a promising machining method for γ-TiAl and guide further research and development of EUVM γ-TiAl.展开更多
The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport ...The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facili...The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.展开更多
As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language proc...The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the...Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the broken spatial translational symmetry.However,the intensity of first-order Raman scattering at crystal edges has been rarely explored,although the mechanical stress and edge characteristics have been thoroughly studied by the Raman peak shift and the spectral features of the edge-related Raman modes.Here,by taking Ga As crystal with a well-defined edge as an example,we reveal the intensity enhancement of Raman-active modes and the emergence of Raman-forbidden modes under specific polarization configurations at the edge.This is attributed to the presence of a hot spot at the edge due to the redistributed electromagnetic fields and electromagnetic wave propagations of incident laser and Raman signal near the edge,which are confirmed by the finite-difference time-domain simulations.Spatially-resolved Raman intensities of both Raman-active and Raman-forbidden modes near the edge are calculated based on the redistributed electromagnetic fields,which quantitatively reproduce the corresponding experimental results.These findings offer new insights into the intensity enhancement of Raman scattering at crystal edges and present a new avenue to manipulate light–matter interactions of crystal by manufacturing various types of edges and to characterize the edge structures in photonic and optoelectronic devices.展开更多
The intelligent operation management of distribution services is crucial for the stability of power systems.Integrating the large language model(LLM)with 6G edge intelligence provides customized management solutions.H...The intelligent operation management of distribution services is crucial for the stability of power systems.Integrating the large language model(LLM)with 6G edge intelligence provides customized management solutions.However,the adverse effects of false data injection(FDI)attacks on the performance of LLMs cannot be overlooked.Therefore,we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligenceempowered distribution power grids.First,we formulate a resource allocation optimization problem.The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption.Then,we decouple it based on virtual queues.We utilize an LLM-assisted deep Q network(DQN)to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path.Simulations demonstrate that the proposed algorithm has excellent performance in convergence,delay,and security.展开更多
Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims ...Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims to mitigate far-field noise.Among these cases,a reduction in the wavelength is found to be advantageous for noise suppression,with the smallest wavelength case achieving a maximum noise reduction of 1.9 dB.Furthermore,the noise radiation induced by WLEs is suppressed mainly at medium frequencies.The theory of multiprocess aeroacoustics is applied to reveal their underlying mechanisms.The dominant factor is the source cutoff effect,which significantly decreases the source strength on hills.Additionally,spanwise decoherence with phase interference serves as another crucial mechanism,particularly for reducing mid-frequency noise.展开更多
This paper aims to design a morphing wing with both Flexible Leading Edge(FLE)and Flexible Trailing Edge(FTE)by using cellular structures,which can help the wing boost the deformation to a greater extent on the premis...This paper aims to design a morphing wing with both Flexible Leading Edge(FLE)and Flexible Trailing Edge(FTE)by using cellular structures,which can help the wing boost the deformation to a greater extent on the premise that the weight is not changed,so as to play a greater role in aerodynamic control such as gust interference.First,as for structural design,based on NACA0012,a morphing wing model constructed by 3 forms of cell structures is proposed.Then,the aerodynamic characteristics under the interference of FLE and FTE are calculated by the Computational Fluid Dynamic(CFD)method.After the surrogate model is established to predict the lift coefficient of the wing effectively,the sensitivity analysis reveals that the main sensitivity index of FTE deflection angle β is 0.565,which has the greatest influence on the lift coefficient.And the total sensitivity index of FLE deflection angle γ is increased by 78.9%,which reveals a strong coupling relationship between FLE and FTE.Finally,using Finite Element Analysis(FEA)method and experiment,the deformation capability of the model under certain static loads are obtained.The results reveal that the maximum deflection angle of the morphing wing model can be±22°at FLE and±64°at FTE,indicating strong structural stiffness and resistance to bending breakage of the model.The presented results can be useful in the design of the cellular morphing wing with multiple flexible systems.展开更多
Non-Abelian topological insulators are characterized by matrix-valued,non-commuting topological charges with regard to more than one energy gap.Their descriptions go beyond the conventional topological band theory,in ...Non-Abelian topological insulators are characterized by matrix-valued,non-commuting topological charges with regard to more than one energy gap.Their descriptions go beyond the conventional topological band theory,in which an additive integer like the winding or Chern number is endowed separately with each(degenerate group of)energy band(s).In this work,we reveal that Floquet(time-periodic)driving could not only enrich the topology and phase transitions of non-Abelian topological matter,but also induce bulk-edge correspondence unique to nonequilibrium setups.Using a one-dimensional,three-band model as an illustrative example,we demonstrate that Floquet driving could reshuffle the phase diagram of the non-driven system,yielding both gapped and gapless Floquet band structures with non-Abelian topological charges.Moreover,by dynamically tuning the anomalous Floquet π-quasienergy gap,non-Abelian topological transitions inaccessible to static systems could arise,leading to much more complicated relations between non-Abelian topological charges and Floquet edge states.These discoveries put forth periodic driving as a powerful scheme of engineering non-Abelian topological phases and incubating unique non-Abelian band topology beyond equilibrium.展开更多
Mitral regurgitation (MR) is a highly prevalent valvular heart disease globally,with untreated severe cases demonstrating associations with elevated morbidity,mortality,and adverse cardiovascular outcomes.[1,2]While t...Mitral regurgitation (MR) is a highly prevalent valvular heart disease globally,with untreated severe cases demonstrating associations with elevated morbidity,mortality,and adverse cardiovascular outcomes.[1,2]While transcatheter edge-toedge repair (TEER) has emerged as an alternative option for high surgical risk patients with severe MR,[3,4]severe MR of Carpentier class IIIa (characterized by restricted leaflet motion during both systole and diastole) has been considered a relative contraindication for TEER interventions due to stenosis risk and procedural complexity.[5,6]展开更多
基金supported by Huo Yingdong Young Teachers Foundation,Ministry of State Education of ChinaNational Natural Science Foundation of China(No.59904003).
文摘3-D rigid visco-plastic finite element method (FEM) is used in the analysisof metal forming processes, including strip and plate rolling, shape rolling, slab edging, specialstrip rolling. The shifted incomplete Cholesky decomposition of the stiffness matrix with thesolution of the equations for velocity increment by the conjugate gradient method is combined. Thistechnique, termed the shifted ICCG method, is then employed to solve the slab edging problem. Theperformance of this algorithm in terms of the number of iterations, friction variation, shiftedparameter psi and the results of simulation for processing parameters are analysed. Numerical testsand application of this technique verify the efficiency and stability of the shifted ICCG method inthe analysis of slab edging.
基金supported by the National Key Research and Development Program of China(Nos.2021YFB2802000 and 2022YFB2804301)Shanghai Municipal Science and Technology Major Project,Science and Technology Commission of Shanghai Municipality(No.21DZ1100500)+2 种基金Shanghai Frontiers Science Center Program(2021-2025 No.20)National Natural Science Foundation of China(Nos.61975123 and 12072200)Science and Technology Development Foundation of Pudong New Area(No.PKX2021-D10)。
文摘Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution.In this work,an all-optical diffractive neural network(DPENet)based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed.Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges.Simulation results show that DPENet achieves F-scores of 0.9308(MNIST)and 0.9352(NIST)and enables real-time edge detection of biological cells,achieving an F-score of 0.7462.
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金co-supported by the Science Center for Gas Turbine Project, China(No. P2022-AB-IV-001-002)the National Natural Science Foundation of China (No. 91960203)+1 种基金the Fundamental Research Funds for the Central Universities (No. D5000230048)the Innovation Capability Support Program of Shaanxi (No. 2022TD-60)
文摘Superior strength and high-temperature performance make γ-TiAl vital for lightweight aero-engines. However, its inherent brittleness poses machining problems. This study employed Elliptical Ultrasonic Vibration Milling (EUVM) to address these problems. Considering the influence of machining parameters on vibration patterns of EUVM, a separation time model was established to analyze the vibration evolutionary process, thereby instructing the cutting mechanism. On this basis, deep discussions regarding chip formation, cutting force, edge breakage, and subsurface layer deformation were conducted for EUVM and Conventional Milling (CM). Chip morphology showed the chip formation was rooted in the periodic brittle fracture. Local dimples proved that the thermal effect of high-speed cutting improved the plasticity of γ-TiAl. EUVM achieved a maximum 18.17% reduction in cutting force compared with CM. The force variation mechanism differed with changes in the cutting speed or the vibration amplitude, and its correlation with thermal softening, strain hardening, and vibratory cutting effects was analyzed. EUVM attained desirable edge breakage by achieving smaller fracture lengths. The fracture mechanisms of different phases were distinct, causing a surge in edge fracture size of γ-TiAl under microstructural differences. In terms of subsurface deformation, EUVM also showed strengthening effects. Noteworthy, the lamellar deformation patterns under the cutting removal state differed from the quasi-static, which was categorized by the orientation angles. Additionally, the electron backscattering diffraction provided details of the influence of microstructural difference on the orientation and the deformation of grains in the subsurface layer. The results demonstrate that EUVM is a promising machining method for γ-TiAl and guide further research and development of EUVM γ-TiAl.
基金National Natural Science Foundation of China(U2468201,62122012,62221001).
文摘The rapid expansion of railways,especially High-Speed Railways(HSRs),has drawn considerable interest from both academic and industrial sectors.To meet the future vision of smart rail communications,the rail transport industry must innovate in key technologies to ensure high-quality transmissions for passengers and railway operations.These systems must function effectively under high mobility conditions while prioritizing safety,ecofriendliness,comfort,transparency,predictability,and reliability.On the other hand,the proposal of 6 G wireless technology introduces new possibilities for innovation in communication technologies,which may truly realize the current vision of HSR.Therefore,this article gives a review of the current advanced 6 G wireless communication technologies for HSR,including random access and switching,channel estimation and beamforming,integrated sensing and communication,and edge computing.The main application scenarios of these technologies are reviewed,as well as their current research status and challenges,followed by an outlook on future development directions.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
文摘The accelerated advancement of the Internet of Things(IoT)has generated substantial data,including sensitive and private information.Consequently,it is imperative to guarantee the security of data sharing.While facilitating fine-grained access control,Ciphertext Policy Attribute-Based Encryption(CP-ABE)can effectively ensure the confidentiality of shared data.Nevertheless,the conventional centralized CP-ABE scheme is plagued by the issues of keymisuse,key escrow,and large computation,which will result in security risks.This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues.The integrity and traceability of shared data are guaranteed by the use of blockchain technology to store and verify access transactions.The encryption and decryption operations of the CP-ABE algorithm have been implemented using elliptic curve scalarmultiplication to accommodate lightweight IoT devices,as opposed to themore arithmetic bilinear pairing found in the traditional CP-ABE algorithm.Additionally,a portion of the computation is delegated to the edge nodes to alleviate the computational burden on users.A distributed key management method is proposed to address the issues of key escrow andmisuse.Thismethod employs the edge blockchain to facilitate the storage and distribution of attribute private keys.Meanwhile,data security sharing is enhanced by combining off-chain and on-chain ciphertext storage.The security and performance analysis indicates that the proposed scheme is more efficient and secure.
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
基金the National Research Foundation(NRF)Singapore mid-sized center grant(NRF-MSG-2023-0002)FrontierCRP grant(NRF-F-CRP-2024-0006)+2 种基金A*STAR Singapore MTC RIE2025 project(M24W1NS005)IAF-PP project(M23M5a0069)Ministry of Education(MOE)Singapore Tier 2 project(MOE-T2EP50220-0014).
文摘The rise of large-scale artificial intelligence(AI)models,such as ChatGPT,Deep-Seek,and autonomous vehicle systems,has significantly advanced the boundaries of AI,enabling highly complex tasks in natural language processing,image recognition,and real-time decisionmaking.However,these models demand immense computational power and are often centralized,relying on cloud-based architectures with inherent limitations in latency,privacy,and energy efficiency.To address these challenges and bring AI closer to real-world applications,such as wearable health monitoring,robotics,and immersive virtual environments,innovative hardware solutions are urgently needed.This work introduces a near-sensor edge computing(NSEC)system,built on a bilayer AlN/Si waveguide platform,to provide real-time,energy-efficient AI capabilities at the edge.Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction,coupled with Si-based thermo-optic Mach-Zehnder interferometers for neural network computations,the system represents a transformative approach to AI hardware design.Demonstrated through multimodal gesture and gait analysis,the NSEC system achieves high classification accuracies of 96.77%for gestures and 98.31%for gaits,ultra-low latency(<10 ns),and minimal energy consumption(<0.34 pJ).This groundbreaking system bridges the gap between AI models and real-world applications,enabling efficient,privacy-preserving AI solutions for healthcare,robotics,and next-generation human-machine interfaces,marking a pivotal advancement in edge computing and AI deployment.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
基金Project supported by the National Key Research and Development Program of China(Grant No.2023YFA1407000)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0460000)+4 种基金the National Natural Science Foundation of China(Grant Nos.12322401,12127807,and 12393832)CAS Key Research Program of Frontier Sciences(Grant No.ZDBS-LY-SLH004)Beijing Nova Program(Grant No.20230484301)Youth Innovation Promotion Association,Chinese Academy of Sciences(Grant No.2023125)CAS Project for Young Scientists in Basic Research(Grant No.YSBR-026)。
文摘Edge structures are ubiquitous in the processing and fabrication of various optoelectronic devices.Novel physical properties and enhanced light–matter interactions are anticipated to occur at crystal edges due to the broken spatial translational symmetry.However,the intensity of first-order Raman scattering at crystal edges has been rarely explored,although the mechanical stress and edge characteristics have been thoroughly studied by the Raman peak shift and the spectral features of the edge-related Raman modes.Here,by taking Ga As crystal with a well-defined edge as an example,we reveal the intensity enhancement of Raman-active modes and the emergence of Raman-forbidden modes under specific polarization configurations at the edge.This is attributed to the presence of a hot spot at the edge due to the redistributed electromagnetic fields and electromagnetic wave propagations of incident laser and Raman signal near the edge,which are confirmed by the finite-difference time-domain simulations.Spatially-resolved Raman intensities of both Raman-active and Raman-forbidden modes near the edge are calculated based on the redistributed electromagnetic fields,which quantitatively reproduce the corresponding experimental results.These findings offer new insights into the intensity enhancement of Raman scattering at crystal edges and present a new avenue to manipulate light–matter interactions of crystal by manufacturing various types of edges and to characterize the edge structures in photonic and optoelectronic devices.
基金supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010(5400-202199534A-0-5-ZN).
文摘The intelligent operation management of distribution services is crucial for the stability of power systems.Integrating the large language model(LLM)with 6G edge intelligence provides customized management solutions.However,the adverse effects of false data injection(FDI)attacks on the performance of LLMs cannot be overlooked.Therefore,we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligenceempowered distribution power grids.First,we formulate a resource allocation optimization problem.The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption.Then,we decouple it based on virtual queues.We utilize an LLM-assisted deep Q network(DQN)to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path.Simulations demonstrate that the proposed algorithm has excellent performance in convergence,delay,and security.
基金supported by the National Natural Science Foundation of China(12322210,12172351,92252202,and 12388101)the Fundamental Research Funds for the Central Universities.
文摘Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims to mitigate far-field noise.Among these cases,a reduction in the wavelength is found to be advantageous for noise suppression,with the smallest wavelength case achieving a maximum noise reduction of 1.9 dB.Furthermore,the noise radiation induced by WLEs is suppressed mainly at medium frequencies.The theory of multiprocess aeroacoustics is applied to reveal their underlying mechanisms.The dominant factor is the source cutoff effect,which significantly decreases the source strength on hills.Additionally,spanwise decoherence with phase interference serves as another crucial mechanism,particularly for reducing mid-frequency noise.
基金co-supported by the National Natural Science Foundation of China(No.52402460)project funded by the China Postdoctoral Science Foundation(No.2024T171113)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This paper aims to design a morphing wing with both Flexible Leading Edge(FLE)and Flexible Trailing Edge(FTE)by using cellular structures,which can help the wing boost the deformation to a greater extent on the premise that the weight is not changed,so as to play a greater role in aerodynamic control such as gust interference.First,as for structural design,based on NACA0012,a morphing wing model constructed by 3 forms of cell structures is proposed.Then,the aerodynamic characteristics under the interference of FLE and FTE are calculated by the Computational Fluid Dynamic(CFD)method.After the surrogate model is established to predict the lift coefficient of the wing effectively,the sensitivity analysis reveals that the main sensitivity index of FTE deflection angle β is 0.565,which has the greatest influence on the lift coefficient.And the total sensitivity index of FLE deflection angle γ is increased by 78.9%,which reveals a strong coupling relationship between FLE and FTE.Finally,using Finite Element Analysis(FEA)method and experiment,the deformation capability of the model under certain static loads are obtained.The results reveal that the maximum deflection angle of the morphing wing model can be±22°at FLE and±64°at FTE,indicating strong structural stiffness and resistance to bending breakage of the model.The presented results can be useful in the design of the cellular morphing wing with multiple flexible systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.12275260 and 11905211)the Fundamental Research Funds for the Central Universities(Grant No.202364008)the Young Talents Project of Ocean University of China。
文摘Non-Abelian topological insulators are characterized by matrix-valued,non-commuting topological charges with regard to more than one energy gap.Their descriptions go beyond the conventional topological band theory,in which an additive integer like the winding or Chern number is endowed separately with each(degenerate group of)energy band(s).In this work,we reveal that Floquet(time-periodic)driving could not only enrich the topology and phase transitions of non-Abelian topological matter,but also induce bulk-edge correspondence unique to nonequilibrium setups.Using a one-dimensional,three-band model as an illustrative example,we demonstrate that Floquet driving could reshuffle the phase diagram of the non-driven system,yielding both gapped and gapless Floquet band structures with non-Abelian topological charges.Moreover,by dynamically tuning the anomalous Floquet π-quasienergy gap,non-Abelian topological transitions inaccessible to static systems could arise,leading to much more complicated relations between non-Abelian topological charges and Floquet edge states.These discoveries put forth periodic driving as a powerful scheme of engineering non-Abelian topological phases and incubating unique non-Abelian band topology beyond equilibrium.
基金supported by the 1·3·5 Project for Disciplines of Excellence from West China Hospital of Sichuan University(ZYGD23021&23HXFH009)Sichuan Science and Technology Program(No.2025ZNSFSC1698)the Sichuan Provincial Cadre Health Research Program(ZH2024-103).
文摘Mitral regurgitation (MR) is a highly prevalent valvular heart disease globally,with untreated severe cases demonstrating associations with elevated morbidity,mortality,and adverse cardiovascular outcomes.[1,2]While transcatheter edge-toedge repair (TEER) has emerged as an alternative option for high surgical risk patients with severe MR,[3,4]severe MR of Carpentier class IIIa (characterized by restricted leaflet motion during both systole and diastole) has been considered a relative contraindication for TEER interventions due to stenosis risk and procedural complexity.[5,6]
