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.
