A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions ...Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.展开更多
To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an...To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an ordered interconnected three-dimensional montmorillonite(MMT)aerogel network.The average pore diameter of the aerogels was successfully reduced from 11.53μm to 2.51μm by adjusting the ratio of poly(vinyl alcohol)(PVA)to MMT via directional freezing.Changes in the aerogel network were observed in field emission scanning electron microscope(FESEM)images.After vacuum impregnation,the aerogel network structure of the composites was observed using FESEM.Tensile tests indicated that as the pore diameter decreased,the elongation at break of the composites first increased to a peak of329.61%before decreasing,while the tensile strength and Young's modulus continuously increased to their maximum values of 6.29 MPa and24.67 MPa,respectively.Meanwhile,FESEM images of the tensile cracks and fracture surfaces showed that with a reduction in aerogel pore diameter,the degrees of crack deflection and interfacial debonding increased,presenting a rougher fracture surface.These phenomena enable the composites to dissipate substantial energy during tension,thus effectively improving the mechanical strength of the composites.The present work elucidates the bearing of ordered three-dimensional aerogel network structures on the performance of rubber matrices and provides crucial theoretical insights and technical guidance for the creation and optimization of high-performance PDMS-based composites.展开更多
To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and v...To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and vacuum hot pressing sintering techniques.The results show that introducing TiB and Si can reduce the steady-state creep rate by an order of magnitude at 600℃ compared to the alloy.However,the beneficial effect of Si can be maintained at 700℃ while the positive effect of TiB gradually diminishes due to the pores near TiB and interface debonding.The creep deformation mechanism of the as-sintered TiB/(TA15−Si)composite is primarily governed by dislocation climbing.The high creep resistance at 600℃ can be mainly attributed to the absence of grain boundaryαphases,load transfer by TiB whisker,and the hindrance of dislocation movement by silicides.The low steady-state creep rate at 700℃ is mainly resulted from the elimination of grain boundaryαphases as well as increased dynamic precipitation of silicides andα_(2).展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy...The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.展开更多
Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to s...Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.展开更多
In order to construct quasi-continuously networked reinforcement in titanium(Ti)matrix composites,in this study,Ti-6 Al-4 V spherical powders were uniformly coated with a graphene nanosheet(GNS)layer by high energy ba...In order to construct quasi-continuously networked reinforcement in titanium(Ti)matrix composites,in this study,Ti-6 Al-4 V spherical powders were uniformly coated with a graphene nanosheet(GNS)layer by high energy ball milling and then consolidated by spark plasma sintering.Results showed that the GNS layer on the powder surface inhibited continuous metallurgy bonding between powders during sintering,which led to the formation of quasi-networked hybrid reinforcement structure consisting of insitu Ti C and remained GNSs.The networked GNSs/Ti64 composite possessed noticeably higher tensile strength but similar ductility to the Ti64 alloy,leading to both better tensile strength and ductility than the GNSs/Ti composite with randomly dispersed GNSs and Ti C.The formation mechanism and the fracture mechanism of the networked hybrid reinforcement were discussed.The results provided a method to fabricate Ti matrix composites with high strength and good ductility.展开更多
Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure ...Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.展开更多
Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ide...Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ideal functional filler for fabricating thermally conductive polymer composites to provide efficient thermal management.Extensive studies have been focusing on constructing graphene networks in polymer composites to achieve high thermal conductivities.Compared with conventional composite fabrications by directly mixing graphene with polymers,preconstruction of three-dimensional graphene networks followed by backfilling polymers represents a promising way to produce composites with higher performances,enabling high manufacturing flexibility and controllability.In this review,we first summarize the factors that affect thermal conductivity of graphene composites and strategies for fabricating highly thermally conductive graphene/polymer composites.Subsequently,we give the reasoning behind using preconstructed three-dimensional graphene networks for fabricating thermally conductive polymer composites and highlight their potential applications.Finally,our insight into the existing bottlenecks and opportunities is provided for developing preconstructed porous architectures of graphene and their thermally conductive composites.展开更多
A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone （HAZ） in X70 pipeline ...A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone （HAZ） in X70 pipeline steels. The mass percent of chemical compositions （i. e. carbon equivalent based upon the International Institute of Welding equation （CEIIw）, the carbon equivalent based upon the chemical portion of the ho-Bessyo carbon equivalent equation （CEecm）, the sum of the niobium, vanadium and titanium concentrations （CvTaNb）, the sum of the niobium and vanadium concentrations （CNbv）, the sum of the chromium, molybdenum, nickel and copper concentrations （CcrMoNiCu））, yield strength （YS） at 0. 005 offset, ultimate tensile strength （UTS） and percent elongation （El） were considered as input parameters to the network, while Vickers microhardness with 10 N load was considered as its output. For the purpose of constructing this model, 104 different data were gathered from the experimental re- sul.ts. Scatter diagrams and two statistical criteria, i.e. absolute fraction of variance （R2 ） and mean relative error （MRE）, were used to evaluate the prediction performance of the developed model. The developed model can be fur- ther used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipe line steels.展开更多
Highly conductive polymer composites(CPCs) with excellent mechanical flexibility are ideal materials for designing excellent electromagnetic interference(EMI) shielding materials,which can be used for the electromagne...Highly conductive polymer composites(CPCs) with excellent mechanical flexibility are ideal materials for designing excellent electromagnetic interference(EMI) shielding materials,which can be used for the electromagnetic interference protection of flexible electronic devices.It is extremely urgent to fabricate ultra-strong EMI shielding CPCs with efficient conductive networks.In this paper,a novel silver-plated polylactide short fiber(Ag@PL ASF,AAF) was fabricated and was integrated with carbon nanotubes(CNT) to construct a multi-scale conductive network in polydimethylsiloxane(PDMS) matrix.The multi-scale conductive network endowed the flexible PDMS/AAF/CNT composite with excellent electrical conductivity of 440 S m-1and ultra-strong EMI shielding effectiveness(EMI SE) of up to 113 dB,containing only 5.0 vol% of AAF and 3.0 vol% of CNT(11.1wt% conductive filler content).Due to its excellent flexibility,the composite still showed 94% and 90% retention rates of EMI SE even after subjected to a simulated aging strategy(60℃ for 7 days) and 10,000 bending-releasing cycles.This strategy provides an important guidance for designing excellent EMI shielding materials to protect the workspace,environment and sensitive circuits against radiation for flexible electronic devices.展开更多
Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-por...Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.展开更多
Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The...Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.展开更多
TiAl-based composites reinforced with different high volume fractions of nearly network TizAIC phase have been successfully prepared by mechanical alloying and hot-pressing method.Their microstructure.mechanical and t...TiAl-based composites reinforced with different high volume fractions of nearly network TizAIC phase have been successfully prepared by mechanical alloying and hot-pressing method.Their microstructure.mechanical and tribological properties have been investigated.Ti2AIC network becomes continuous but the network wall grows thicker with increasing the Ti2AIC content.The continuity and wall size of the network Ti2AIC phase exert a significant influence on the mechanical properties.The bending strength of the composites first increases and then decreases with the Ti2A1C content.The compressive strength of the composite decreases slightly compared to the TiAI alloy,but the hardness is enhanced.Due to the high hardness and load-carrying capacity of the network structure,these composites have the better wear resistance.And this enhancement is more notable at low applied loads and high Ti2A1C content.The mechanisms simulating the role of network Ti2AIC phase on the wear behavior and the wear process of TiAl/Ti2AIC composites at different applied loads have been proposed.展开更多
Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, dif...Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.展开更多
Despite it is widely accepted that intrapopulation variation is fundamental to ecological and evolutionary processes,this level of information has only recently been included into network analysis of species/populatio...Despite it is widely accepted that intrapopulation variation is fundamental to ecological and evolutionary processes,this level of information has only recently been included into network analysis of species/population interactions.When done,it has revealed non-random patterns in the distribution of trophic resources.Nestedness in resource use among individuals is the most recurrent observed pattern,often accompanied by an absence of modularity,but no previous studies examine bipartite modularity.We use network analysis to describe the diet composition of the Balearic endemic lizard Podarcis lilfordi in 2 islets at population and individual levels,based on the occurrence of food items in fecal samples.Our objectives are to 1)compare niche structure at both levels,2)characterize niche partition using nestedness and modularity,and 3)assess how size,sex,season,and spatial location influence niche structure.At population-level niche width was wide,but narrow at the level of the individual.Both islet networks were nested,indicating similar ranking of the food preferences among individuals,but also modular,which was partially explained by seasonality.Sex and body size did not notably affect diet composition.Large niche overlap and therefore possibly relaxed competition were observed among females in one of the islets and during spring on both islets.Likewise,higher modularity in autumn suggests that higher competition could lead to specialization in both populations,because resources are usually scarce in this season.The absence of spatial location influence on niche might respond to fine-grained spatio-temporally distribution of food resources.Behavioral traits,not included in this study,could also influence resource partitioning.展开更多
Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power d...Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power devices is limited.To solve this problem,melamine sponge(MS)was used as the skeleton to wrap boron nitride nanosheets(BNNS)through repeated layering assembly,successfully preparing a three-dimensional(3D)boron nitride network(BNNS@MS),and PVA hydrogels were formed in the pores of the network.Due to the existence of the continuous phonon conduction network,the BNNS@MS/PVA exhibited an improvedκ.When the content of BNNS is about 6 wt.%,κof the hydrogel was increased to 1.12 W m^(-1)K^(-1),about two times higher than that of pure hydrogel.The solid heat conduction network and liquid convection network cooperate to achieve good thermal management ability.Combined with its high specific heat capacity,the composites have an important application prospect in the field of wearable flexible electronic thermal management.展开更多
With the increasing power density and integration of electronic devices,polymeric composites with high thermal conductivity(TC)are in urgent demand for solving heat accumulation issues.However,the direct introduction ...With the increasing power density and integration of electronic devices,polymeric composites with high thermal conductivity(TC)are in urgent demand for solving heat accumulation issues.However,the direct introduction of inorganic fillers into a polymer matrix at low filler content usually leads to low TC enhancement.In this work,an interconnected three-dimensional(3D)polysulfone/hexagonal boron nitride-carbon nanofiber(PSF/BN-CNF)skeleton was prepared via the salt templated method to address this issue.After embedding into the epoxy(EP),the EP/PSF/BN-CNF composite presents a high TC of 2.18 W m^(−1) K^(−1) at a low filler loading of 28.61 wt%,corresponding to a TC enhancement of 990%compared to the neat epoxy.The enhanced TC is mainly attributed to the fabricated 3D interconnected structure and the efficient synergistic effect of BN and CNF.In addition,the TC of the epoxy composites can be further increased to 2.85 W m^(−1) K^(−1) at the same filler loading through a post-heat treatment of the PSF/BN-CNF skeletons.After carbonization at 1500°C,the adhesive PSF was converted into carbonaceous layers,which could serve as a thermally conductive glue to connect the filler network,further decreasing the interfacial thermal resistance and promoting phonon transport.Besides,the good heat dissipation performance of the EP/C/BN-CNF composites was directly confirmed by thermal infrared imaging,indicating a bright and broad application in the thermal management of modern electronics and energy fields.展开更多
Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high ther...Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests performed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm-Levenberg-Marquardt （L-M） algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gently from 500℃ to 850 ~C. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.展开更多
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金supported by the Fundamental Research Funds of Chinese Academy of Forestry(Nos.CAFYBB2022SY037,CAFYBB2021ZA002 and CAFYBB2022QC002)the Basic Research Foundation of Yunnan Province(Grant No.202201AT070264).
文摘Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.
基金financially supported by the National Natural Science Foundation of China(Nos.21876164 and U2030203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘To address the poor mechanical properties of polydimethylsiloxane(PDMS)and enhance the understanding of the reinforcement mechanisms of aerogel network structures in rubber matrices,this study reinforced PDMS using an ordered interconnected three-dimensional montmorillonite(MMT)aerogel network.The average pore diameter of the aerogels was successfully reduced from 11.53μm to 2.51μm by adjusting the ratio of poly(vinyl alcohol)(PVA)to MMT via directional freezing.Changes in the aerogel network were observed in field emission scanning electron microscope(FESEM)images.After vacuum impregnation,the aerogel network structure of the composites was observed using FESEM.Tensile tests indicated that as the pore diameter decreased,the elongation at break of the composites first increased to a peak of329.61%before decreasing,while the tensile strength and Young's modulus continuously increased to their maximum values of 6.29 MPa and24.67 MPa,respectively.Meanwhile,FESEM images of the tensile cracks and fracture surfaces showed that with a reduction in aerogel pore diameter,the degrees of crack deflection and interfacial debonding increased,presenting a rougher fracture surface.These phenomena enable the composites to dissipate substantial energy during tension,thus effectively improving the mechanical strength of the composites.The present work elucidates the bearing of ordered three-dimensional aerogel network structures on the performance of rubber matrices and provides crucial theoretical insights and technical guidance for the creation and optimization of high-performance PDMS-based composites.
基金financially supported by the National Key R&D Program of China(No.2022YFB3707405)the National Natural Science Foundation of China(Nos.U22A20113,52171137,52071116)+1 种基金Heilongjiang Provincial Natural Science Foundation,China(No.TD2020E001)Heilongjiang Touyan Team Program,China.
文摘To assess the high-temperature creep properties of titanium matrix composites for aircraft skin,the TA15 alloy,TiB/TA15 and TiB/(TA15−Si)composites with network structure were fabricated using low-energy milling and vacuum hot pressing sintering techniques.The results show that introducing TiB and Si can reduce the steady-state creep rate by an order of magnitude at 600℃ compared to the alloy.However,the beneficial effect of Si can be maintained at 700℃ while the positive effect of TiB gradually diminishes due to the pores near TiB and interface debonding.The creep deformation mechanism of the as-sintered TiB/(TA15−Si)composite is primarily governed by dislocation climbing.The high creep resistance at 600℃ can be mainly attributed to the absence of grain boundaryαphases,load transfer by TiB whisker,and the hindrance of dislocation movement by silicides.The low steady-state creep rate at 700℃ is mainly resulted from the elimination of grain boundaryαphases as well as increased dynamic precipitation of silicides andα_(2).
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金supported by the National Natural Science Foundation of China(No.51601019)the Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515010233)+1 种基金the Key Project of Shaanxi Province of Qinchuangyuan“Scientist and Engineer”Team Construction,China(No.2023KXJ-123)the Natural Science Foundation of Shaanxi Province,China(No.2024JC-YBMS-014).
文摘The probability of phase formation was predicted using k-nearest neighbor algorithm(KNN)and artificial neural network algorithm(ANN).Additionally,the composition ranges of Ti,Cu,Ni,and Hf in 40 unknown amorphous alloy composites(AACs)were predicted using ANN.The predicted alloys were then experimentally verified through X-ray diffraction(XRD)and high-resolution transmission electron microscopy(HRTEM).The prediction accuracies of the ANN for AM and IM phases are 93.12%and 85.16%,respectively,while the prediction accuracies of KNN for AM and IM phases are 93%and 84%,respectively.It is observed that when the contents of Ti,Cu,Ni,and Hf fall within the ranges of 32.7−34.5 at.%,16.4−17.3 at.%,30.9−32.7 at.%,and 17.3−18.3 at.%,respectively,it is more likely to form AACs.Based on the results of XRD and HRTEM,the Ti_(34)Cu17Ni_(31.36)Hf_(17.64)and Ti_(36)Cu_(18)Ni_(29.44)Hf_(16.56)alloys are identified as good AACs,which are in closely consistent with the predicted amorphous alloy compositions.
文摘Neural-Network Response Surfaces （NNRS） is applied to replace the actual expensive finite element analysis during the composite structural optimization process. The Orthotropic Experiment Method （OEM） is used to select the most appropriate design samples for network training. The trained response surfaces can either be objective function or constraint conditions. Together with other conven- tional constraints, an optimization model is then set up and can be solved by Genetic Algorithm （GA）. This allows the separation between design analysis modeling and optimization searching. Through an example of a hat-stiffened composite plate design, the weight response surface is constructed to be objective function, and strength and buckling response surfaces as constraints; and all of them are trained through NASTRAN finite element analysis. The results of optimization study illustrate that the cycles of structural analysis ean be remarkably reduced or even eliminated during the optimization, thus greatly raising the efficiency of optimization process. It also observed that NNRS approximation can achieve equal or even better accuracy than conventional functional response surfaces.
基金financially supported by the Key Research and Development Plan of Shaanxi Province(No.2020KW-034)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(CX2021058)。
文摘In order to construct quasi-continuously networked reinforcement in titanium(Ti)matrix composites,in this study,Ti-6 Al-4 V spherical powders were uniformly coated with a graphene nanosheet(GNS)layer by high energy ball milling and then consolidated by spark plasma sintering.Results showed that the GNS layer on the powder surface inhibited continuous metallurgy bonding between powders during sintering,which led to the formation of quasi-networked hybrid reinforcement structure consisting of insitu Ti C and remained GNSs.The networked GNSs/Ti64 composite possessed noticeably higher tensile strength but similar ductility to the Ti64 alloy,leading to both better tensile strength and ductility than the GNSs/Ti composite with randomly dispersed GNSs and Ti C.The formation mechanism and the fracture mechanism of the networked hybrid reinforcement were discussed.The results provided a method to fabricate Ti matrix composites with high strength and good ductility.
基金the Karadeniz Technical University Research Fund for financially supporting this research (No: 2010.112.010.4)
文摘Al-Cu-Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al-Cu-Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al-Cu-Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.
文摘Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ideal functional filler for fabricating thermally conductive polymer composites to provide efficient thermal management.Extensive studies have been focusing on constructing graphene networks in polymer composites to achieve high thermal conductivities.Compared with conventional composite fabrications by directly mixing graphene with polymers,preconstruction of three-dimensional graphene networks followed by backfilling polymers represents a promising way to produce composites with higher performances,enabling high manufacturing flexibility and controllability.In this review,we first summarize the factors that affect thermal conductivity of graphene composites and strategies for fabricating highly thermally conductive graphene/polymer composites.Subsequently,we give the reasoning behind using preconstructed three-dimensional graphene networks for fabricating thermally conductive polymer composites and highlight their potential applications.Finally,our insight into the existing bottlenecks and opportunities is provided for developing preconstructed porous architectures of graphene and their thermally conductive composites.
文摘A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone （HAZ） in X70 pipeline steels. The mass percent of chemical compositions （i. e. carbon equivalent based upon the International Institute of Welding equation （CEIIw）, the carbon equivalent based upon the chemical portion of the ho-Bessyo carbon equivalent equation （CEecm）, the sum of the niobium, vanadium and titanium concentrations （CvTaNb）, the sum of the niobium and vanadium concentrations （CNbv）, the sum of the chromium, molybdenum, nickel and copper concentrations （CcrMoNiCu））, yield strength （YS） at 0. 005 offset, ultimate tensile strength （UTS） and percent elongation （El） were considered as input parameters to the network, while Vickers microhardness with 10 N load was considered as its output. For the purpose of constructing this model, 104 different data were gathered from the experimental re- sul.ts. Scatter diagrams and two statistical criteria, i.e. absolute fraction of variance （R2 ） and mean relative error （MRE）, were used to evaluate the prediction performance of the developed model. The developed model can be fur- ther used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipe line steels.
基金supported by the National Natural Science Foundation of China(Nos.51973142,52033005,52003169).
文摘Highly conductive polymer composites(CPCs) with excellent mechanical flexibility are ideal materials for designing excellent electromagnetic interference(EMI) shielding materials,which can be used for the electromagnetic interference protection of flexible electronic devices.It is extremely urgent to fabricate ultra-strong EMI shielding CPCs with efficient conductive networks.In this paper,a novel silver-plated polylactide short fiber(Ag@PL ASF,AAF) was fabricated and was integrated with carbon nanotubes(CNT) to construct a multi-scale conductive network in polydimethylsiloxane(PDMS) matrix.The multi-scale conductive network endowed the flexible PDMS/AAF/CNT composite with excellent electrical conductivity of 440 S m-1and ultra-strong EMI shielding effectiveness(EMI SE) of up to 113 dB,containing only 5.0 vol% of AAF and 3.0 vol% of CNT(11.1wt% conductive filler content).Due to its excellent flexibility,the composite still showed 94% and 90% retention rates of EMI SE even after subjected to a simulated aging strategy(60℃ for 7 days) and 10,000 bending-releasing cycles.This strategy provides an important guidance for designing excellent EMI shielding materials to protect the workspace,environment and sensitive circuits against radiation for flexible electronic devices.
基金This work was financially supported by the Natural Science Foundation of Shandong Province, China (Y2006F03).
文摘Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.
文摘Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.
基金supported financially by the National Natural Science Foundation of China(Nos.51505459 and 51675510)the National Basic Research Program of China(No.2013CB632300)
文摘TiAl-based composites reinforced with different high volume fractions of nearly network TizAIC phase have been successfully prepared by mechanical alloying and hot-pressing method.Their microstructure.mechanical and tribological properties have been investigated.Ti2AIC network becomes continuous but the network wall grows thicker with increasing the Ti2AIC content.The continuity and wall size of the network Ti2AIC phase exert a significant influence on the mechanical properties.The bending strength of the composites first increases and then decreases with the Ti2A1C content.The compressive strength of the composite decreases slightly compared to the TiAI alloy,but the hardness is enhanced.Due to the high hardness and load-carrying capacity of the network structure,these composites have the better wear resistance.And this enhancement is more notable at low applied loads and high Ti2A1C content.The mechanisms simulating the role of network Ti2AIC phase on the wear behavior and the wear process of TiAl/Ti2AIC composites at different applied loads have been proposed.
基金supported in part by National Natural Science Foundation of China(61502368,61303033,U1135002 and U1405255)the National High Technology Research and Development Program(863 Program)of China(No.2015AA017203)+1 种基金the Fundamental Research Funds for the Central Universities(XJS14072,JB150308)the Aviation Science Foundation of China(No.2013ZC31003,20141931001)
文摘Accelerate processor, efficient software and pervasive connections provide sensor nodes with more powerful computation and storage ability, which can offer various services to user. Based on these atomic services, different sensor nodes can cooperate and compose with each other to complete more complicated tasks for user. However, because of the regional characteristic of sensor nodes, merging data with different sensitivities become a primary requirement to the composite services, and information flow security should be intensively considered during service composition. In order to mitigate the great cost caused by the complexity of modeling and the heavy load of single-node verification to the energy-limited sensor node, in this paper, we propose a new distributed verification framework to enforce information flow security on composite services of smart sensor network. We analyze the information flows in composite services and specify security constraints for each service participant. Then we propose an algorithm over the distributed verification framework involving each sensor node to participate in the composite service verification based on the security constraints. The experimental results indicate that our approach can reduce the cost of verification and provide a better load balance.
基金This work is framed within projects CGL2017-88122-P and BFU 2009-09359 financed by the Spanish Government.
文摘Despite it is widely accepted that intrapopulation variation is fundamental to ecological and evolutionary processes,this level of information has only recently been included into network analysis of species/population interactions.When done,it has revealed non-random patterns in the distribution of trophic resources.Nestedness in resource use among individuals is the most recurrent observed pattern,often accompanied by an absence of modularity,but no previous studies examine bipartite modularity.We use network analysis to describe the diet composition of the Balearic endemic lizard Podarcis lilfordi in 2 islets at population and individual levels,based on the occurrence of food items in fecal samples.Our objectives are to 1)compare niche structure at both levels,2)characterize niche partition using nestedness and modularity,and 3)assess how size,sex,season,and spatial location influence niche structure.At population-level niche width was wide,but narrow at the level of the individual.Both islet networks were nested,indicating similar ranking of the food preferences among individuals,but also modular,which was partially explained by seasonality.Sex and body size did not notably affect diet composition.Large niche overlap and therefore possibly relaxed competition were observed among females in one of the islets and during spring on both islets.Likewise,higher modularity in autumn suggests that higher competition could lead to specialization in both populations,because resources are usually scarce in this season.The absence of spatial location influence on niche might respond to fine-grained spatio-temporally distribution of food resources.Behavioral traits,not included in this study,could also influence resource partitioning.
基金the National Natural Science Foundation of China(Nos.52173078,52130303,and 51803151)the Young Elite Scientists Sponsorship Program by CAST(No.2019QNRC001)。
文摘Polyvinyl alcohol hydrogels have been used in wearable devices due to their good flexibility and biocompatibility.However,due to the low thermal conductivity(κ)of pure hydrogel,its further application in high power devices is limited.To solve this problem,melamine sponge(MS)was used as the skeleton to wrap boron nitride nanosheets(BNNS)through repeated layering assembly,successfully preparing a three-dimensional(3D)boron nitride network(BNNS@MS),and PVA hydrogels were formed in the pores of the network.Due to the existence of the continuous phonon conduction network,the BNNS@MS/PVA exhibited an improvedκ.When the content of BNNS is about 6 wt.%,κof the hydrogel was increased to 1.12 W m^(-1)K^(-1),about two times higher than that of pure hydrogel.The solid heat conduction network and liquid convection network cooperate to achieve good thermal management ability.Combined with its high specific heat capacity,the composites have an important application prospect in the field of wearable flexible electronic thermal management.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.51925403)the Major Research Plan of the National Natural Science Foundation of China(No.91934302)+1 种基金the National Natural Science Foundation of China(Nos.21676052,21606042)the Funding for Exploratory Projects of the National Key Laboratory of Chemical Engineering(SKL-ChE-20T07).
文摘With the increasing power density and integration of electronic devices,polymeric composites with high thermal conductivity(TC)are in urgent demand for solving heat accumulation issues.However,the direct introduction of inorganic fillers into a polymer matrix at low filler content usually leads to low TC enhancement.In this work,an interconnected three-dimensional(3D)polysulfone/hexagonal boron nitride-carbon nanofiber(PSF/BN-CNF)skeleton was prepared via the salt templated method to address this issue.After embedding into the epoxy(EP),the EP/PSF/BN-CNF composite presents a high TC of 2.18 W m^(−1) K^(−1) at a low filler loading of 28.61 wt%,corresponding to a TC enhancement of 990%compared to the neat epoxy.The enhanced TC is mainly attributed to the fabricated 3D interconnected structure and the efficient synergistic effect of BN and CNF.In addition,the TC of the epoxy composites can be further increased to 2.85 W m^(−1) K^(−1) at the same filler loading through a post-heat treatment of the PSF/BN-CNF skeletons.After carbonization at 1500°C,the adhesive PSF was converted into carbonaceous layers,which could serve as a thermally conductive glue to connect the filler network,further decreasing the interfacial thermal resistance and promoting phonon transport.Besides,the good heat dissipation performance of the EP/C/BN-CNF composites was directly confirmed by thermal infrared imaging,indicating a bright and broad application in the thermal management of modern electronics and energy fields.
基金Henan Innovation Project for University Prominent Research Talents (2007KYCX008)Henan Major Science and Technol-ogy Project (0523021500)+1 种基金Henan University of Science and Technology Major Pre-research Foundation (2005ZD003)Henan University of Science and Technology Personnel Scientific Research Foundation
文摘Alumina dispersion strengthened copper composite （nano-Al2O3/Cu composite） was recently emerged as a kind of potentially viable and attractive engineering material for applications requiring high strength, high thermal and electrical conductivities and resistance to softening at elevated temperatures. The nano-Al2O3/Cu composite was produced by internal oxidation. The microstructures of the composite were analyzed by the TEM and its hot deformation behavior was investigated by means of continuous compression tests performed on a Gleeble 1500 thermo-simulator. Making use of the modified algorithm-Levenberg-Marquardt （L-M） algorithm BP neural network, a model for predicting the flow stresses during hot deformation was set up on the base of the experimental data. Results show that the microstructures of the composite are characterized by uniform distribution of nano-Al2O3 particles in Cu-matrix. The sliding of dislocations is the main deformation mechanism. The dynamic recovery is the main softening mode with the flow stress decreasing gently from 500℃ to 850 ~C. The recrystallization of Cu-matrix can be retarded late into as high as 850 ℃, when it happens only partially. The well-trained BP neural network model can accurately describe the influence of the temperature, strain rate, and true strain on the flow stresses, therefore, it can precisely predict the flow stresses of the composite under given deforming conditions and provide a new way to optimize hot deforming process parameters.
