In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their ...In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.展开更多
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware...The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.展开更多
The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.H...The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.展开更多
Ceramic 4D printing,which integrates dynamic deformation with additive manufacturing,demonstrates significant potential in intelligent manufacturing,on-demand shaping of complex structures,and multifunctional device d...Ceramic 4D printing,which integrates dynamic deformation with additive manufacturing,demonstrates significant potential in intelligent manufacturing,on-demand shaping of complex structures,and multifunctional device development.Its core advantage lies in endowing materials with environmentally responsive dynamic deformation capabilities.However,current technologies still face limitations in responsiveness,reversibility,and mechanical performance.To address these challenges,this study proposes a programmable ceramic precursor system based on synergistic reinforcement of phase-separating hydrogels and shape memory polymers,combined with a nano-ceramic particle enhancement strategy.Using stereolithography 3D printing,high-precision fabrication of complex structures was achieved.By adjusting precursor composition,programming time,and structural thickness,the phase-separation kinetics-driven delayed recovery mechanism was elucidated,enabling precise control over recovery onset time.Furthermore,the thermal response mechanism of the precursor materials is explored,along with their potential for multi-shape transformation in biomedical applications,which is further extended to shape memory polymer systems.By employing a layered printing strategy,the autonomous reversible deformation of ceramic precursors is realized,providing new possibilities for specific applications.展开更多
Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security...Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security threats.Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments,yet they are fundamentally limited in computational and memory resources.Accurate and memoryefficient persistent flow detection on programmable switches is therefore essential.However,existing approaches often rely on fixed-window sketches or multiple sketches instances,which either suffer from insufficient temporal precision or incur substantial memory overhead,making them ineffective on programmable switches.To address these challenges,we propose SP-Sketch,an innovative sliding-window-based sketch that leverages a probabilistic update mechanism to emulate slot expiration without maintaining multiple sketch instances.This innovative design significantly reduces memory consumption while preserving high detection accuracy across multiple time intervals.We provide rigorous theoretical analyses of the estimation errors,deriving precise error bounds for the proposed method,and validate our approach through comprehensive implementations on both P4 hardware switches(with Intel Tofino ASIC)and software switches(i.e.,BMv2).Experimental evaluations using real-world traffic traces demonstrate that SP-Sketch outperforms traditional methods,improving accuracy by up to 20%over baseline sliding window approaches and enhancing recall by 5%compared to non-sliding alternatives.Furthermore,SP-Sketch achieves a significant reduction in memory utilization,reducing memory consumption by up to 65%compared to traditional methods,while maintaining a robust capability to accurately track persistent flow behavior over extended time periods.展开更多
Kirigami,through introducing cuts into a thin sheet,can greatly improve the stretchability of structures and also generate complex patterns,showing potentials in various applications.Interestingly,even with the same c...Kirigami,through introducing cuts into a thin sheet,can greatly improve the stretchability of structures and also generate complex patterns,showing potentials in various applications.Interestingly,even with the same cutting pattern,the mechanical response of kirigami metamaterials can exhibit significant differences depending on the cutting angles in respect to the loading direction.In this work,we investigate the structural deformation of kirigami metamaterials with square domains and varied cutting angles of 0°and 45°.We further introduce a second level of cutting on the basis of the first cutting pattern.By combining experiments and finite element simulations,it is found that,compared to the commonly used 0°cuts,the two-level kirigami metamaterials with 45°cuts exhibit a unique alternating arrangement phenomenon of expanded/unexpanded states in the loading process,which also results in distinct stress–strain response.Through tuning the cutting patterns of metamaterials with 45°cuts,precise control of the rotation of the kirigami unit is realized,leading to kirigami metamaterials with encryption properties.The current work demonstrates the programmability of structural deformation in hierarchical kirigami metamaterials through controlling the local cutting modes.展开更多
Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-...Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-uniform rational B-spline(NURBS)curves are taken to replace the traditional ligament boundaries of the chiral structure.The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency.For the problem of finding structure configuration with specified mechanical properties,such as Young’s modulus,Poisson’s ratio or deformation,an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration.To satisfy some more complex deformation requirements,a non-homogeneous inverse design method is proposed and verified through simulation and experiments.Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.展开更多
This paper presents the development of a thermoplastic shape memory rubber that can be programmed at human body temperature for comfortable fitting applications.We hybridized commercially available thermoplastic rubbe...This paper presents the development of a thermoplastic shape memory rubber that can be programmed at human body temperature for comfortable fitting applications.We hybridized commercially available thermoplastic rubber(TPR)used in the footwear industry with un-crosslinked polycaprolactone(PCL)to create two samples,namely TP6040 and TP7030.The shape memory behavior,elasticity,and thermo-mechanical response of these rubbers were systematically investigated.The experimental results demonstrated outstanding shape memory performance,with both samples achieving shape fixity ratios(Rf)and shape recovery ratios(R_(r))exceeding 94%.TP6040 exhibited a fitting time of 80 s at body temperature(37℃),indicating a rapid response for shape fixing.The materials also showed good elasticity before and after programming,which is crucial for comfort fitting.These findings suggest that the developed shape memory thermoplastic rubber has potential applications in personalized comfort fitting products,offering advantages over traditional customization techniques in terms of efficiency and cost-effectiveness.展开更多
Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structur...Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.展开更多
A programmable low-profile array antenna based on nematic liquid crystals(NLCs)is proposed.Each antenna unit comprises a square patch radiating structure and a tunable NLC-based phase shifter capable of achieving a ph...A programmable low-profile array antenna based on nematic liquid crystals(NLCs)is proposed.Each antenna unit comprises a square patch radiating structure and a tunable NLC-based phase shifter capable of achieving a phase shift exceeding 360°with high linearity.First,the above 64 antenna units are periodically arranged into an 8×8 NLC-based antenna array,and the bias voltage of the NLC-based phase shifter loaded on the antenna unit is adjusted through the control of the field-programmable gate array(FPGA)programming sequences.This configuration enables precise phase changes for all 64 channels.Numerical simulation,sample processing,and experimental measurements of the antenna array are conducted to validate the performance of the antenna.The numerical and experimental results demonstrate that the proposed antenna performs well within the frequency range of 19.5-20.5 GHz,with a 3 dB relative bandwidth of 10%and a maximum main lobe gain of 14.1 dBi.A maximum scanning angle of±34°is achieved through the adjustment of the FPGA programming sequence.This NLC-based programmable array antenna shows promising potential for applications in satellite communication.展开更多
基于IPv6的段路由(segment routing over IPv6,SRv6)作为下一代网络架构的关键使能技术,通过引入灵活的段路由转发平面,为提升网络智能化水平、拓展业务服务能力带来革新机遇.旨在全面梳理近年来SRv6的演进趋势和研究现状.首先,系统总结...基于IPv6的段路由(segment routing over IPv6,SRv6)作为下一代网络架构的关键使能技术,通过引入灵活的段路由转发平面,为提升网络智能化水平、拓展业务服务能力带来革新机遇.旨在全面梳理近年来SRv6的演进趋势和研究现状.首先,系统总结SRv6在网络架构与性能、网络管理与运维以及新兴业务支撑等方面的应用,凸显了SRv6精细调度、灵活编程、服务融合等独特优势.与此同时,深入剖析SRv6在性能与效率、可靠性与安全性、部署与演进策略这3个方面所面临的关键挑战,并重点讨论当前主流的解决思路和发展趋势.最后,立足产业生态构建、人工智能引入、行业融合创新等视角,对SRv6未来的发展方向和挑战进行前瞻性思考和展望.研究成果将为运营商构建开放、智能、安全的新一代网络提供理论参考和实践指导.展开更多
基金supported by the National Key Research and Development Program of China under Grant 2023YFB2903902in part by the Science and Technology Innovation Leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘In the process of programmable networks simplifying network management and increasing network flexibility through custom packet behavior,security incidents caused by human logic errors are seriously threatening their safe operation,robust verificationmethods are required to ensure their correctness.As one of the formalmethods,symbolic execution offers a viable approach for verifying programmable networks by systematically exploring all possible paths within a program.However,its application in this field encounters scalability issues due to path explosion and complex constraint-solving.Therefore,in this paper,we propose NetVerifier,a scalable verification system for programmable networks.Tomitigate the path explosion issue,we developmultiple pruning strategies that strategically eliminate irrelevant execution paths while preserving verification integrity by precisely identifying the execution paths related to the verification purpose.To address the complex constraint-solving problem,we introduce an execution results reuse solution to avoid redundant computation of the same constraints.To apply these solutions intelligently,a matching algorithm is implemented to automatically select appropriate solutions based on the characteristics of the verification requirement.Moreover,Language Aided Verification(LAV),an assertion language,is designed to express verification intentions in a concise form.Experimental results on diverse open-source programs of varying scales demonstrate NetVerifier’s improvement in scalability and effectiveness in identifying potential network errors.In the best scenario,compared with ASSERT-P4,NetVerifier reduced the execution path,verification time,and memory occupation of the verification process by 99.92%,94.76%,and 65.19%,respectively.
基金co-supported by the National Natural Science Foundation of China(Grant Nos.62222404,T2450054,62304084,62504087,62361136587 and 92248304)the National Key Research and Development Plan of China(Grant No.2021YFB3601200)+3 种基金the Major Program of Hubei Province(Grant No.2023BAA009)the Research Grants Council of Hong Kong Postdoctoral Fellowship Scheme(Grant No.PDFS2223-4S06)the China Postdoctoral Science Foundation funded project(Grant No.2025M770530)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20250136).
文摘The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB2901501in part by the Science and Technology Innovation leading Talents Subsidy Project of Central Plains under Grant 244200510038.
文摘The rapid growth of distributed data-centric applications and AI workloads increases demand for low-latency,high-throughput communication,necessitating frequent and flexible updates to network routing configurations.However,maintaining consistent forwarding states during these updates is challenging,particularly when rerouting multiple flows simultaneously.Existing approaches pay little attention to multi-flow update,where improper update sequences across data plane nodes may construct deadlock dependencies.Moreover,these methods typically involve excessive control-data plane interactions,incurring significant resource overhead and performance degradation.This paper presents P4LoF,an efficient loop-free update approach that enables the controller to reroute multiple flows through minimal interactions.P4LoF first utilizes a greedy-based algorithm to generate the shortest update dependency chain for the single-flow update.These chains are then dynamically merged into a dependency graph and resolved as a Shortest Common Super-sequence(SCS)problem to produce the update sequence of multi-flow update.To address deadlock dependencies in multi-flow updates,P4LoF builds a deadlock-fix forwarding model that leverages the flexible packet processing capabilities of the programmable data plane.Experimental results show that P4LoF reduces control-data plane interactions by at least 32.6%with modest overhead,while effectively guaranteeing loop-free consistency.
基金supported by the National Natural Science Foundation of China(Grant Nos.52025053 and 52235006)the Jilin Provincial Scientific and Technological Development Program(20220204119YY)the Natural Science Foundation of Shandong Province(ZR2023ME154)。
文摘Ceramic 4D printing,which integrates dynamic deformation with additive manufacturing,demonstrates significant potential in intelligent manufacturing,on-demand shaping of complex structures,and multifunctional device development.Its core advantage lies in endowing materials with environmentally responsive dynamic deformation capabilities.However,current technologies still face limitations in responsiveness,reversibility,and mechanical performance.To address these challenges,this study proposes a programmable ceramic precursor system based on synergistic reinforcement of phase-separating hydrogels and shape memory polymers,combined with a nano-ceramic particle enhancement strategy.Using stereolithography 3D printing,high-precision fabrication of complex structures was achieved.By adjusting precursor composition,programming time,and structural thickness,the phase-separation kinetics-driven delayed recovery mechanism was elucidated,enabling precise control over recovery onset time.Furthermore,the thermal response mechanism of the precursor materials is explored,along with their potential for multi-shape transformation in biomedical applications,which is further extended to shape memory polymer systems.By employing a layered printing strategy,the autonomous reversible deformation of ceramic precursors is realized,providing new possibilities for specific applications.
基金supported by the National Undergraduate Innovation and Entrepreneurship Training Program of China(Project No.202510559076)at Jinan University,a nationwide initiative administered by the Ministry of Educationthe National Natural Science Foundation of China(NSFC)under Grant No.62172189.
文摘Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security threats.Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments,yet they are fundamentally limited in computational and memory resources.Accurate and memoryefficient persistent flow detection on programmable switches is therefore essential.However,existing approaches often rely on fixed-window sketches or multiple sketches instances,which either suffer from insufficient temporal precision or incur substantial memory overhead,making them ineffective on programmable switches.To address these challenges,we propose SP-Sketch,an innovative sliding-window-based sketch that leverages a probabilistic update mechanism to emulate slot expiration without maintaining multiple sketch instances.This innovative design significantly reduces memory consumption while preserving high detection accuracy across multiple time intervals.We provide rigorous theoretical analyses of the estimation errors,deriving precise error bounds for the proposed method,and validate our approach through comprehensive implementations on both P4 hardware switches(with Intel Tofino ASIC)and software switches(i.e.,BMv2).Experimental evaluations using real-world traffic traces demonstrate that SP-Sketch outperforms traditional methods,improving accuracy by up to 20%over baseline sliding window approaches and enhancing recall by 5%compared to non-sliding alternatives.Furthermore,SP-Sketch achieves a significant reduction in memory utilization,reducing memory consumption by up to 65%compared to traditional methods,while maintaining a robust capability to accurately track persistent flow behavior over extended time periods.
基金supported by the National Natural Science Foundation of China(Grant Nos.12102392 and 12272341)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ21A020008).
文摘Kirigami,through introducing cuts into a thin sheet,can greatly improve the stretchability of structures and also generate complex patterns,showing potentials in various applications.Interestingly,even with the same cutting pattern,the mechanical response of kirigami metamaterials can exhibit significant differences depending on the cutting angles in respect to the loading direction.In this work,we investigate the structural deformation of kirigami metamaterials with square domains and varied cutting angles of 0°and 45°.We further introduce a second level of cutting on the basis of the first cutting pattern.By combining experiments and finite element simulations,it is found that,compared to the commonly used 0°cuts,the two-level kirigami metamaterials with 45°cuts exhibit a unique alternating arrangement phenomenon of expanded/unexpanded states in the loading process,which also results in distinct stress–strain response.Through tuning the cutting patterns of metamaterials with 45°cuts,precise control of the rotation of the kirigami unit is realized,leading to kirigami metamaterials with encryption properties.The current work demonstrates the programmability of structural deformation in hierarchical kirigami metamaterials through controlling the local cutting modes.
基金supported by the National Natural Science Foundation of China(grant numbers 11972287 and 12072266)the State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ23106)+1 种基金the National Key Laboratory of Aircraft Configuration Design(No.2023-JCJQ-LB-070)the Fundamental Research Funds for the Central Universities.
文摘Chiral metamaterials are manmade structures with extraordinary mechanical properties derived from their special geometric design instead of chemical composition.To make the mechanical deformation programmable,the non-uniform rational B-spline(NURBS)curves are taken to replace the traditional ligament boundaries of the chiral structure.The Neural networks are innovatively inserted into the calculation of mechanical properties of the chiral structure instead of finite element methods to improve computational efficiency.For the problem of finding structure configuration with specified mechanical properties,such as Young’s modulus,Poisson’s ratio or deformation,an inverse design method using the Neural network-based proxy model is proposed to build the relationship between mechanical properties and geometric configuration.To satisfy some more complex deformation requirements,a non-homogeneous inverse design method is proposed and verified through simulation and experiments.Numerical and test results reveal the high computational efficiency and accuracy of the proposed method in the design of chiral metamaterials.
基金supported by the Aeronautical Science Foundation of China(Grant Nos.2024Z009052003,20230038052001 and 20230015052002)the Third Batch of Science and Technology Plan Projects in Changzhou City in 2023(Applied Basic Research,Grant No.CJ20230080).
文摘This paper presents the development of a thermoplastic shape memory rubber that can be programmed at human body temperature for comfortable fitting applications.We hybridized commercially available thermoplastic rubber(TPR)used in the footwear industry with un-crosslinked polycaprolactone(PCL)to create two samples,namely TP6040 and TP7030.The shape memory behavior,elasticity,and thermo-mechanical response of these rubbers were systematically investigated.The experimental results demonstrated outstanding shape memory performance,with both samples achieving shape fixity ratios(Rf)and shape recovery ratios(R_(r))exceeding 94%.TP6040 exhibited a fitting time of 80 s at body temperature(37℃),indicating a rapid response for shape fixing.The materials also showed good elasticity before and after programming,which is crucial for comfort fitting.These findings suggest that the developed shape memory thermoplastic rubber has potential applications in personalized comfort fitting products,offering advantages over traditional customization techniques in terms of efficiency and cost-effectiveness.
基金supported by the National Natural Science Foundation of China (Grant Nos.12102021,12372105,12172026,and 12225201)the Fundamental Research Funds for the Central Universities and the Academic Excellence Foundation of BUAA for PhD Students.
文摘Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.
基金The National Natural Science Foundation of China(No.62401168,62401139,62401170)China Postdoctoral Science Foundation(No.2023MD744197)+2 种基金Postdoctoral Fellowship Program of CPSF(No.GZC20230631)Project for Enhancing Young and Middle-aged Teacher’s Research Basis Ability in Colleges of Guangxi(No.2023KY0218)Guangxi Key Laboratory Foundation of Optoelectronic Information Processing(No.GD23102)。
文摘A programmable low-profile array antenna based on nematic liquid crystals(NLCs)is proposed.Each antenna unit comprises a square patch radiating structure and a tunable NLC-based phase shifter capable of achieving a phase shift exceeding 360°with high linearity.First,the above 64 antenna units are periodically arranged into an 8×8 NLC-based antenna array,and the bias voltage of the NLC-based phase shifter loaded on the antenna unit is adjusted through the control of the field-programmable gate array(FPGA)programming sequences.This configuration enables precise phase changes for all 64 channels.Numerical simulation,sample processing,and experimental measurements of the antenna array are conducted to validate the performance of the antenna.The numerical and experimental results demonstrate that the proposed antenna performs well within the frequency range of 19.5-20.5 GHz,with a 3 dB relative bandwidth of 10%and a maximum main lobe gain of 14.1 dBi.A maximum scanning angle of±34°is achieved through the adjustment of the FPGA programming sequence.This NLC-based programmable array antenna shows promising potential for applications in satellite communication.
文摘基于IPv6的段路由(segment routing over IPv6,SRv6)作为下一代网络架构的关键使能技术,通过引入灵活的段路由转发平面,为提升网络智能化水平、拓展业务服务能力带来革新机遇.旨在全面梳理近年来SRv6的演进趋势和研究现状.首先,系统总结SRv6在网络架构与性能、网络管理与运维以及新兴业务支撑等方面的应用,凸显了SRv6精细调度、灵活编程、服务融合等独特优势.与此同时,深入剖析SRv6在性能与效率、可靠性与安全性、部署与演进策略这3个方面所面临的关键挑战,并重点讨论当前主流的解决思路和发展趋势.最后,立足产业生态构建、人工智能引入、行业融合创新等视角,对SRv6未来的发展方向和挑战进行前瞻性思考和展望.研究成果将为运营商构建开放、智能、安全的新一代网络提供理论参考和实践指导.
