Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant li...Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant limitation in the growth of high-quality in-plane GaAs NW networks on such substrates.Here,wereport on the selective area growth of anti-phase domain-free in-plane GaAs NWs and NW networks on Ge(111)substrates.Detailed structural studies confirm that the GaAs NW grown using a large pattern period and GaAsNW networks grown by adding the Sb are both high-quality pure zinc-blende single crystals free of stackingfaults,twin defects,and anti-phase domain defects.Room-temperature photoluminescence measurements show asubstantial improvement in crystal quality and good consistency and uniformity of the GaAs NW networks.Ourwork provides useful insights into the controlled growth of high-quality anti-phase domain-defects-free in-planeIII-V NWs and NW networks.展开更多
In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser...In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.展开更多
We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linkin...We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linking of polystyrene（PS） shell layer in the core-shell bottlebrush copolymers led to the formation of micropores and large-sized nanopores（meso/macrospores） in MONNs, respectively, while selective removal of polylactide（PLA） core layer generated mesoporous tubular structure. The size of PLA-templated mesoporous cores and porous structure both at micro-and meso-scale could be controlled by simple tuning of the ratio of core/shell or the PLA core fraction in the bottlebrush precursors. Moreover, the resultant MONNs showed a highly selective adsorption capacity for the positively charged dyes on the basis of multi-porosity and carboxylate group-rich structure. In addition, MONNs also exhibited effective performance in size-selective adsorption of biomacromolecules. This work represents a new avenue for the preparation of MONNs and also provides a new application for molecular bottlebrushes in nanotechnology.展开更多
Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and pea...Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.展开更多
A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The freq...A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated.展开更多
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ...With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.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
Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the se...Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.展开更多
We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the propo...We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the proposed network to improve the average end-to-end throughput performance.More specifically,by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols,we derive the expressions of system instantaneous bit-error-rate(BER),instantaneous end-to-end throughput,average end-to-end throughput,single node detection(SND)occurrence probability and average end-to-end BER over non-identical fading channels.Finally,theoretical analyses and Monte Carlo simulation results are presented.Evaluations show that:1) SPNC protocol outperforms the conventional physical-layer network coding(PNC) protocol in infrequent light shadowing(ILS),average shadowing(AS) and frequent heavy shadowing(FHS) Shadowed-Rician fading channels.2) As the satellite-relay channel fading gets more sewere,SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively.3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio(SNR) regime,while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in highSNR regime.展开更多
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o...Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning...The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.展开更多
It is a hot issue in communication research field to select the best network for Heterogeneous Wireless Networks(HWNs),and it is also a difficult problem to reduce the handoff number of vertical handoff.In order to so...It is a hot issue in communication research field to select the best network for Heterogeneous Wireless Networks(HWNs),and it is also a difficult problem to reduce the handoff number of vertical handoff.In order to solve this problem,the paper proposes a multiple attribute network selection algorithm based on Analytic Hierarchy Process(AHP)and synergetic theory.The algorithm applies synergetics to network selection,considering the candidate network as a compound system composed of multiple attribute subsystems,and combines the subsystem order degree with AHP weight to obtain entropy of the compound system,which is opposite the synergy degree of a network system.The greater the synergy degree,the better the network performance.The algorithm takes not only the coordination of objective attributes but also Quality of Service(QoS)requirements into consideration,ensuring that users select the network with overall good performance.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide uses with satisfactory QoS according to different services.展开更多
This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme...This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme by combining the network properties and the users' requirement accurately and decrease ping-pong effect. The method of entropy and FAHP( Fuzzy Analytic Hierarchy Process) are used to calculate weight value and the sojourn time calculation is used to avoid ping-pang effect. The simulation results show that the improved scheme enhances the more accuracy of network selection than the existing methods and reduces the number of ping-pang effect.展开更多
Even though various wireless Net- work Access Technologies （NATs） with dif- ferent specifications and applications have been developed in the recent years, no single wireless technology alone can satisfy the any- ti...Even though various wireless Net- work Access Technologies （NATs） with dif- ferent specifications and applications have been developed in the recent years, no single wireless technology alone can satisfy the any- time, anywhere, and any service wire- less-access needs of mobile users. A real seamless wireless mobile environment is only realized by considering vertical and horizontal handoffs together. One of the major design issues in heterogeneous wireless networks is the support of Vertical Handoff （VHO）. VHO occurs when a multi-interface enabled mobile terminal changes its Point of Attachment （PoA） from one type of wireless access technology to another, while maintaining an active session. In this paper we present a novel multi-criteria VHO algorithm, which chooses the target NAT based on several factors such as user preferences, system parameters, and traf- tic-types with varying Quality of Service （QoS） requirements. Two modules i.e., VHO Neces- sity Estimation （VHONE） module and target NAT selection module, are designed. Both modules utilize several ＂weighted＂ users＇ and system＇s parameters. To improve the robust- ness of the proposed algorithm, the weighting system is designed based on the concept of fuzzy linguistic variables.展开更多
In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in ...In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.展开更多
In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN i...In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.展开更多
Heterogeneous wireless access technologies will coexist in next generation wireless networks.These technologies form integrated networks,and these networks support multiple services with high quality level.Various acc...Heterogeneous wireless access technologies will coexist in next generation wireless networks.These technologies form integrated networks,and these networks support multiple services with high quality level.Various access technologies allow users to select the best available access network to meet the requirements of each type of communication service.Being always best connected anytime and anywhere is a major concern in a heterogeneous wireless networks environment.Always best connected enables network selection mechanisms to keep mobile users always connected to the best network.We present an overview of the network selection and prediction problems and challenges.In addition,we discuss a comprehensive classification of related theoretic approaches,and also study the integration between these methods,finding the best solution of network selection and prediction problems.The optimal solution can fulfill the requirements of the next generation wireless networks.展开更多
This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly...This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.展开更多
The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea.The existing works focused on vessels collaboratively served by UAV-enabled aerial base station(ABSs)and te...The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea.The existing works focused on vessels collaboratively served by UAV-enabled aerial base station(ABSs)and terrestrial base stations(TBSs)deployed along the coast,and proved that data rate could be improved by optimizing transmit power and ABS’s position.In practice,users on a vessel can be collaboratively served by an ABS and a vesselenabled base station(VBS)in different networks.In this case,how to select the network for users on a vessel is still an open issue.In this paper,a TBS and a satellite respectively provide wireless backhaul for the ABS and the VBS.The network selection is jointly optimized with transmit power of ABS and VBS,and ABS’s position for improving data rate of all users.We solve it by finding candidates for network selection and iteratively solving transmit power and ABS’s position for each candidate.Simulation results demonstrate that data rate can be improved by collaborative coverage for users on a vessel.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12374459,61974138,and 92065106)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302400)+1 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB0460000)the support from the Youth Innovation Promotion Association,Chinese Academy of Sciences(Grant Nos.2017156 and Y2021043)。
文摘Anti-phase domain defects easily form in the in-plane GaAs nanowires(NWs)grown on CMOS-compatiblegroup IV substrates,which makes it difficult to obtain GaAs NWs with a designed length and also leads to asignificant limitation in the growth of high-quality in-plane GaAs NW networks on such substrates.Here,wereport on the selective area growth of anti-phase domain-free in-plane GaAs NWs and NW networks on Ge(111)substrates.Detailed structural studies confirm that the GaAs NW grown using a large pattern period and GaAsNW networks grown by adding the Sb are both high-quality pure zinc-blende single crystals free of stackingfaults,twin defects,and anti-phase domain defects.Room-temperature photoluminescence measurements show asubstantial improvement in crystal quality and good consistency and uniformity of the GaAs NW networks.Ourwork provides useful insights into the controlled growth of high-quality anti-phase domain-defects-free in-planeIII-V NWs and NW networks.
基金National Natural Science Foundation of China(No.51475315)Innovative Project on the Integration of Industry,Education and Research of Jiangsu Province,China(No.BY2014059-10)
文摘In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.
基金financially supported by the National Natural Science Foundation of China (Nos. 51273066 and 21574042)Shanghai Pujiang Program (No. 13PJ1402300)
文摘We report a synthesis of microporous organic nanotube networks（MONNs） by a combination of hyper cross-linking and molecular templating of core-shell bottlebrush copolymers. The intrabrush and interbrush cross-linking of polystyrene（PS） shell layer in the core-shell bottlebrush copolymers led to the formation of micropores and large-sized nanopores（meso/macrospores） in MONNs, respectively, while selective removal of polylactide（PLA） core layer generated mesoporous tubular structure. The size of PLA-templated mesoporous cores and porous structure both at micro-and meso-scale could be controlled by simple tuning of the ratio of core/shell or the PLA core fraction in the bottlebrush precursors. Moreover, the resultant MONNs showed a highly selective adsorption capacity for the positively charged dyes on the basis of multi-porosity and carboxylate group-rich structure. In addition, MONNs also exhibited effective performance in size-selective adsorption of biomacromolecules. This work represents a new avenue for the preparation of MONNs and also provides a new application for molecular bottlebrushes in nanotechnology.
基金supported by the US Appalachian Regional Commission(ARC)under Grant MU-21579-23。
文摘Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60901029, 61172148, and 60925005)the Natural Science Foundation of Shaanxi Province, China (Grant No. 2011JQ8040)
文摘A new technique for designing a varactor-tunable frequency selective surface （FSS） with an embedded bias network is proposed and experimentally verified. The proposed FSS is based on a square-ring slot FSS. The frequency tuning is achieved by inserting varactor diodes between the square mesh and each unattached square patch. The square mesh is divided into two parts for biasing the varactor diodes. Full-wave numerical simulations show that a wide tuning range can be achieved by changing the capacitances of these loaded varactors. Two homo-type samples using fixed lumped capacitors are fabricated and measured using a standard waveguide measurement setup. Excellent agreement between the measured and simulated results is demonstrated.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.
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.
基金National Natural Science Foundation of China under Grant No.10675060
文摘Complex behavior in a selective aging simple neuron model based on small world networks is investigated. The basic elements of the model are endowed with the main features of a neuron function. The structure of the selective aging neuron model is discussed. We also give some properties of the new network and find that the neuron model displays a power-law behavior. If the brain network is small world-like network, the mean avalanche size is almost the same unless the aging parameter is big enough.
基金National Natural Science Foundation of China(No.62071146).
文摘We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the proposed network to improve the average end-to-end throughput performance.More specifically,by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols,we derive the expressions of system instantaneous bit-error-rate(BER),instantaneous end-to-end throughput,average end-to-end throughput,single node detection(SND)occurrence probability and average end-to-end BER over non-identical fading channels.Finally,theoretical analyses and Monte Carlo simulation results are presented.Evaluations show that:1) SPNC protocol outperforms the conventional physical-layer network coding(PNC) protocol in infrequent light shadowing(ILS),average shadowing(AS) and frequent heavy shadowing(FHS) Shadowed-Rician fading channels.2) As the satellite-relay channel fading gets more sewere,SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively.3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio(SNR) regime,while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in highSNR regime.
基金Supported by the National Natural Science Foundation of China (61074153, 61104131)the Fundamental Research Fundsfor Central Universities of China (ZY1111, JD1104)
文摘Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金The National Natural Science Foundation of China(No.60472053),the Natural Science Foundation of Jiangsu Province(No.BK2003055),the Specialized Research Fund for the Doctoral Pro-gram of Higher Education (No.20030286017).
文摘The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.
基金Supported by the Major State Basic Research Development Program of China(973 Program)(No.2013CB329005)the National Natural Science Foundation of China(No.61171094)+1 种基金the National Science & Technology Key Project(No.2011ZX03001-006-02.No.2011ZX03005004-03)the Key Project of Jiangsu Provincial Natural Science Foundation(No.BK2011027)
文摘It is a hot issue in communication research field to select the best network for Heterogeneous Wireless Networks(HWNs),and it is also a difficult problem to reduce the handoff number of vertical handoff.In order to solve this problem,the paper proposes a multiple attribute network selection algorithm based on Analytic Hierarchy Process(AHP)and synergetic theory.The algorithm applies synergetics to network selection,considering the candidate network as a compound system composed of multiple attribute subsystems,and combines the subsystem order degree with AHP weight to obtain entropy of the compound system,which is opposite the synergy degree of a network system.The greater the synergy degree,the better the network performance.The algorithm takes not only the coordination of objective attributes but also Quality of Service(QoS)requirements into consideration,ensuring that users select the network with overall good performance.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide uses with satisfactory QoS according to different services.
基金Sponsored by the National Natural Science Foundation of China for Young Scholar(Grant No.61302080)the National Natural Science Foundation of China(Grant No.61271182)the National High Technology Research and Development Program of China(863 Program)(Grant No.2012AA01A508)
文摘This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme by combining the network properties and the users' requirement accurately and decrease ping-pong effect. The method of entropy and FAHP( Fuzzy Analytic Hierarchy Process) are used to calculate weight value and the sojourn time calculation is used to avoid ping-pang effect. The simulation results show that the improved scheme enhances the more accuracy of network selection than the existing methods and reduces the number of ping-pang effect.
文摘Even though various wireless Net- work Access Technologies （NATs） with dif- ferent specifications and applications have been developed in the recent years, no single wireless technology alone can satisfy the any- time, anywhere, and any service wire- less-access needs of mobile users. A real seamless wireless mobile environment is only realized by considering vertical and horizontal handoffs together. One of the major design issues in heterogeneous wireless networks is the support of Vertical Handoff （VHO）. VHO occurs when a multi-interface enabled mobile terminal changes its Point of Attachment （PoA） from one type of wireless access technology to another, while maintaining an active session. In this paper we present a novel multi-criteria VHO algorithm, which chooses the target NAT based on several factors such as user preferences, system parameters, and traf- tic-types with varying Quality of Service （QoS） requirements. Two modules i.e., VHO Neces- sity Estimation （VHONE） module and target NAT selection module, are designed. Both modules utilize several ＂weighted＂ users＇ and system＇s parameters. To improve the robust- ness of the proposed algorithm, the weighting system is designed based on the concept of fuzzy linguistic variables.
基金This research was supported in part by the National Natural Science Foundation of China under Grant No.62062031 and 61877053in part by Inner Mongolia natural science foundation grant number 2019MS06035,and Inner Mongolia Science and Technology Major Project,China+1 种基金in part by ROIS NII Open Collaborative Research 21S0601in part by JSPS KAKENHI grant numbers 18KK0279,19H04093,20H00592,and 21H03424.
文摘In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.
基金the National Natural Science Foundation of China under Grants 61631005 and U1801261the National Key R&D Program of China under Grant 2018YFB1801105+3 种基金the Central Universities under Grant ZYGX2019Z022the Key Areas of Research and Development Program of Guangdong Province, China, under Grant 2018B010114001the 111 Project under Grant B20064the China Postdoctoral Science Foundation under Grant No. 2018M631075
文摘In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.
基金funded by the University of Malaya, under Grant No.RG208-11AFR
文摘Heterogeneous wireless access technologies will coexist in next generation wireless networks.These technologies form integrated networks,and these networks support multiple services with high quality level.Various access technologies allow users to select the best available access network to meet the requirements of each type of communication service.Being always best connected anytime and anywhere is a major concern in a heterogeneous wireless networks environment.Always best connected enables network selection mechanisms to keep mobile users always connected to the best network.We present an overview of the network selection and prediction problems and challenges.In addition,we discuss a comprehensive classification of related theoretic approaches,and also study the integration between these methods,finding the best solution of network selection and prediction problems.The optimal solution can fulfill the requirements of the next generation wireless networks.
基金supported by National Natural Science Foundation of China under Grant No.60971083National International Science and Technology Cooperation Project of China (No.2010DFA11320)
文摘This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.
基金supported in part by the National Natural Science Foundation of China(Grant No.62001265)the Fundamental Research Funds for the Central Universities(Grant No.buctrc202124)。
文摘The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea.The existing works focused on vessels collaboratively served by UAV-enabled aerial base station(ABSs)and terrestrial base stations(TBSs)deployed along the coast,and proved that data rate could be improved by optimizing transmit power and ABS’s position.In practice,users on a vessel can be collaboratively served by an ABS and a vesselenabled base station(VBS)in different networks.In this case,how to select the network for users on a vessel is still an open issue.In this paper,a TBS and a satellite respectively provide wireless backhaul for the ABS and the VBS.The network selection is jointly optimized with transmit power of ABS and VBS,and ABS’s position for improving data rate of all users.We solve it by finding candidates for network selection and iteratively solving transmit power and ABS’s position for each candidate.Simulation results demonstrate that data rate can be improved by collaborative coverage for users on a vessel.
