The construction of carbon nanocoil(CNC)-based chiral-dielectric-magnetic trinity composites is considered as a promising approach to achieve excellent low-frequency microwave absorption.However,it is still challengin...The construction of carbon nanocoil(CNC)-based chiral-dielectric-magnetic trinity composites is considered as a promising approach to achieve excellent low-frequency microwave absorption.However,it is still challenging to further enhance the low frequency microwave absorption and elucidate the related loss mechanisms.Herein,the chiral CNCs are first synthesized on a threedimensional(3D)carbon foam and then combined with the FeNi/NiFe_(2)O_(4) nanoparticles to form a novel chiral-dielectric-magnetic trinity foam.The 3D porous CNC-carbon foam network provides excellent impedance matching and strong conduction loss.The formation of the FeNi-carbon interfaces induces interfacial polarization loss,which is confirmed by the density functional theory calculations.Further permeability analysis and the micromagnetic simulation indicate that the nanoscale chiral magnetic heterostructures achieve magnetic pinning and coupling effects,which enhance the magnetic anisotropy and magnetic loss capability.Owing to the synergistic effect between dielectricity,chirality,and magnetism,the trinity composite foam exhibits excellent microwave absorption performance with an ultrabroad effective absorption bandwidth(EAB)of 14 GHz and a minimum reflection of loss less than-50 dB.More importantly,the C-band EAB of the foam is extended to 4 GHz,achieving the full C-band coverage.This study provides further guidelines for the microstructure design of the chiral-dielectric-magnetic trinity composites to achieve broadband microwave absorption.展开更多
The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for per...The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.展开更多
In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general effic...In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general efficiency of a small-world network relative to that of the corresponding regular network is used to measure the small-world effect quantitatively. The more considerable the small-world effect, the higher the general efficiency of a network with a certain cost is. It is shown that the small-world effect increases monotonically with the increase of the vertex number. The optimal rewiring probability to induce the best small-world effect is approximately 0.02 and the optimal average connection probability decreases monotonically with the increase of the vertex number. Therefore, the optimal network structure to induce the maximal small-world effect is the structure with the large vertex number （〉 500）, the small rewiring probability （≈0.02） and the small average connection probability （〈 0.1）. Many previous research results support our results.展开更多
In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is in- vestigated. Both the topological structure and the system parameters can be unknown and need to be i...In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is in- vestigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results.展开更多
By means of the network equation and generalized dimensionless Floquet-Bloch theorem, this paper investigates the properties of the band number and width for quadrangular multiconnected networks （QMNs） with a differ...By means of the network equation and generalized dimensionless Floquet-Bloch theorem, this paper investigates the properties of the band number and width for quadrangular multiconnected networks （QMNs） with a different number of connected waveguide segments （NCWSs） and various matching ratio of waveguide length （MRWL）. It is found that all photonic bands are wide bands when the MRWL is integer. If the integer attribute of MRWL is broken, narrow bands will be created from the wide band near the centre of band structure. For two-segment-connected networks and three-segment-connected networks, it obtains a series of formulae of the band number and width. On the other hand, it proposes a so-called concept of two-segment-connected quantum subsystem and uses it to discuss the complexity of the band structures of QMNs. Based on these formulae, one can dominate the number, width and position of photonic bands within designed frequencies by adjusting the NCWS and MRWL. There would be potential applications for designing optical switches, optical narrow-band filters, dense wavelength-division-multiplexing devices and other correlative waveguide network devices.展开更多
In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of...In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of munitions with an aerial three-dimensional(3D) highly-dynamic topographic structure under a satellite denied environment. As for aerial networked munitions, the measurement of munitions is objectively incomplete due to the degenerated and interrupted link of munitions. For this reason, a cluster-oriented collaborative localization method is put forward in this paper. Multidimensional scaling(MDS) was first integrated with a trilateration localization method(TLM) to construct a relative localization algorithm for determining the relative location of a mobile cluster network. The information related to relative velocity was then combined into a collaborative localization framework to devise a TLM-vMDS algorithm. Finally, an iterative refinement algorithm based on scaling by majorizing a complicated function(SMACOF) was employed to effectively eliminate the influence of incomplete link observation on localization accuracy. Compared with the currently available advanced algorithms, the proposed TLM-vMDS algorithm achieves higher localization accuracy and faster convergence for a cluster of extensively networked munitions, and also offers better numerical stability and robustness for highspeed motion models.展开更多
Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different a...Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different aspects.To explore the impact of location information in depth,this paper proposes an improved global structure model to characterize the influence of nodes.The method considers both the node’s self-information and the role of the location information of neighboring nodes.First,degree centrality of each node is calculated,and then degree value of each node is used to represent self-influence,and degree values of the neighbor layer nodes are divided by the power of the path length,which is path attenuation used to represent global influence.Finally,an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes.In this paper,the propagation process of a real network is obtained by simulation with the SIR model,and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy.The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.展开更多
In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly c...In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly correlated, and hence energy saving using in-network data fusion becomes possible. A traditional data fusion scheme starts with dividing the network into clusters, followed by electing a sensor node as cluster head in each cluster. A cluster head is responsible for collecting data from all its cluster members, performing data fusion on these data and transmitting the fused data to the base station. Assuming that a sensor node is only capable of handling a single node-to-node transmission at a time and each transmission takes T time-slots, a cluster head with n cluster members will take at least nT time-slots to collect data from all its cluster members. In this paper, a tree-based network structure and its formation algorithms are proposed. Simulation results show that the proposed network structure can greatly reduce the delay in data collection.展开更多
Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysi...Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.展开更多
In the functional properties of complex networks, modules play a central role. In this paper, we propose a new method to detect and describe the modular structures of weighted networks. In order to test the proposed m...In the functional properties of complex networks, modules play a central role. In this paper, we propose a new method to detect and describe the modular structures of weighted networks. In order to test the proposed method, as an example, we use our method to analyse the structural properties of the Chinese railway network. Here, the stations are regarded as the nodes and the track sections are regarded as the links. Rigorous analysis of the existing data shows that using the proposed algorithm, the nodes of network can be classified naturally. Moreover, there are several core nodes in each module. Remarkably, we introduce the correlation function Grs, and use it to distinguish the different modules in weighted networks.展开更多
Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified mult...Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified multi-service bearing in the IP network, the largescale access convergence network architecture is proposed. This flat access convergence structure with ultra-small hops, which shortens the service transmission path, reduces the complexity of the edge of the network, and achieves IP strong waist model with the integration of computation, storage and transmission. The key technologies are also introduced in this paper, including endto-end performance guarantee for real time interactive services, fog storing mechanism, and built-in safety transmission with integration of aggregation and control.展开更多
In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by th...In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region （r ≤ 0.001）, the network has even worse synchronizability than the case of isolated communities （r = 0）. Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales： the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network.展开更多
We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature dis...We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.展开更多
In light of the nonlinear approaching capability of artificial neural networks （ ANN）, the term structure of interest rates is predicted using The generalized regression neural network （GRNN） and back propagation ...In light of the nonlinear approaching capability of artificial neural networks （ ANN）, the term structure of interest rates is predicted using The generalized regression neural network （GRNN） and back propagation （BP） neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek＇s model and Cox-Ingersoll-Ross （CIR） model. The comparison reveals that neural network models outperform Vasicek＇s model and CIR model, which are more precise and closer to the real market situation.展开更多
Fe-C-Si-Mn alloy castings used as blades in hydroelectric generators are studied and found to contain network structures after some heat treatments. Castings after annealing and normalizing were analyzed by microscope...Fe-C-Si-Mn alloy castings used as blades in hydroelectric generators are studied and found to contain network structures after some heat treatments. Castings after annealing and normalizing were analyzed by microscope and transmission electron microscopy (TEM). The network formed during annealing was proved by TEM to be pearlite with very fine slices, while that formed during normalizing was proved by TEM and micro-hardness to be martensite or bainite. A theoretical analysis together with experimental studies has proved that the pearlite network is caused by carbon content increase in the interdendritic regions to which carbon atoms transfered from dendritic arms due to lower manganese content there during annealing, while the martensite or bainite network results from the higher hardenability of interdendritic regions where manganese content is higher. Experiments reveal that higher heating temperature or longer heating time enlarges the network size due to manganese homogenization. The network structure has a strengthening function like reinforcing rib, and the smaller the network size, the greater its strengthening capability.展开更多
Abstract： The shortest path problem in a network G is to find shortest paths between some specified source vertices and terminal vertices when the lengths of edges are given. The structure of the optimal solutions se...Abstract： The shortest path problem in a network G is to find shortest paths between some specified source vertices and terminal vertices when the lengths of edges are given. The structure of the optimal solutions set on the shortest paths is studied in this paper. First, the conditions of having unique shortest path between two distinguished vertices 8 and t in a network G are discussed; Second, the structural properties of 2-transformation ^-G graph G on the shortest-paths for G are presented heavily.展开更多
High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated wi...High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.展开更多
RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to de...RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to develop computational methods to predict RNA 3D structures.For these methods,designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges.In this study,we designed and trained a deep learning model to tackle this problem.The model was based on a graph convolutional network(GCN)and named RNAGCN.The model provided a natural way of representing RNA structures,avoided complex algorithms to preserve atomic rotational equivalence,and was capable of extracting features automatically out of structural patterns.Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions.Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions.RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.展开更多
Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.展开更多
A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory.The control problems of c...A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory.The control problems of coupling among the channels and the uncertainty of model parameters are solved by using the method.High precise and robust tracking of required attitude angles can be achieved in complicated air space.A mathematical model of reusable launch vehicle is presented first,and then a controller of flight system is presented.Base on the mathematical model,the controller is divided into two parts:variable-structure controller and neural network module which is used to modify the parameters of controller.This control system decouples the lateraldirectional tunnels well with a neural network sliding mode controller and provides a robust and de-coupled tracking for mission angle profiles.After this a control allocation algorithm is employed to allocate the torque moments to aerodynamic control surfaces and thrusters.The final simulation shows that the control system has a good accurate,robust and de-coupled tracking performance.The stable state error is less than 1°,and the overshoot is less than 5%.展开更多
基金supported by the National Natural Science Foundation of China[Grant Nos.52272288 and 51972039]the China Postdoctoral Science Foundation[No.2021M700658].
文摘The construction of carbon nanocoil(CNC)-based chiral-dielectric-magnetic trinity composites is considered as a promising approach to achieve excellent low-frequency microwave absorption.However,it is still challenging to further enhance the low frequency microwave absorption and elucidate the related loss mechanisms.Herein,the chiral CNCs are first synthesized on a threedimensional(3D)carbon foam and then combined with the FeNi/NiFe_(2)O_(4) nanoparticles to form a novel chiral-dielectric-magnetic trinity foam.The 3D porous CNC-carbon foam network provides excellent impedance matching and strong conduction loss.The formation of the FeNi-carbon interfaces induces interfacial polarization loss,which is confirmed by the density functional theory calculations.Further permeability analysis and the micromagnetic simulation indicate that the nanoscale chiral magnetic heterostructures achieve magnetic pinning and coupling effects,which enhance the magnetic anisotropy and magnetic loss capability.Owing to the synergistic effect between dielectricity,chirality,and magnetism,the trinity composite foam exhibits excellent microwave absorption performance with an ultrabroad effective absorption bandwidth(EAB)of 14 GHz and a minimum reflection of loss less than-50 dB.More importantly,the C-band EAB of the foam is extended to 4 GHz,achieving the full C-band coverage.This study provides further guidelines for the microstructure design of the chiral-dielectric-magnetic trinity composites to achieve broadband microwave absorption.
基金National Natural Science Foundation of China(61772196,61472136)Hunan Provincial Focus Natural Science Fund(2020JJ4249)+4 种基金Key Project of Hunan Provincial Social Science Achievement Review Committee(XSP 19ZD1005)Postgraduate Scientific Research Innovation Project of Hunan Province(CX20201074)Hunan Technology and Business University’s 2019 school-level degree and postgraduate education and teaching reform project(YJG2019YB13)The 2020 school-level teaching reform project of Hunan Technology and Business University(School Teaching Word[2020]No.15)Research Project of Degree and Postgraduate Education Reform in Hunan Province(2020JGYB234).
文摘The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.61101117,61171099,and 61362008)the National Key Scientific and Technological Project of China (Grant No.2012ZX03004005002)+1 种基金the Fundamental Research Funds for the Central Universities,China (Grant No.BUPT2012RC0112)the Natural Science Foundation of Jiangxi Province,China (Grant No.20132BAB201018)
文摘In this paper, the general efficiency, which is the average of the global efficiency and the local efficiency, is defined to measure the communication efficiency of a network. The increasing ratio of the general efficiency of a small-world network relative to that of the corresponding regular network is used to measure the small-world effect quantitatively. The more considerable the small-world effect, the higher the general efficiency of a network with a certain cost is. It is shown that the small-world effect increases monotonically with the increase of the vertex number. The optimal rewiring probability to induce the best small-world effect is approximately 0.02 and the optimal average connection probability decreases monotonically with the increase of the vertex number. Therefore, the optimal network structure to induce the maximal small-world effect is the structure with the large vertex number （〉 500）, the small rewiring probability （≈0.02） and the small average connection probability （〈 0.1）. Many previous research results support our results.
基金Project supported by the Tianyuan Special Funds of the National Natural Science Foundation of China(Grant No.11226242)the Natural Science Foundation of Jiangxi Province of China(Grant No.20122BAB211006)
文摘In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is in- vestigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results.
基金supported by the National Natural Science Foundation of China (Grant No. 10974061)the Program for Innovative Research Team of the Higher Education in Guangdong of China (Grant No. 06CXTD005)
文摘By means of the network equation and generalized dimensionless Floquet-Bloch theorem, this paper investigates the properties of the band number and width for quadrangular multiconnected networks （QMNs） with a different number of connected waveguide segments （NCWSs） and various matching ratio of waveguide length （MRWL）. It is found that all photonic bands are wide bands when the MRWL is integer. If the integer attribute of MRWL is broken, narrow bands will be created from the wide band near the centre of band structure. For two-segment-connected networks and three-segment-connected networks, it obtains a series of formulae of the band number and width. On the other hand, it proposes a so-called concept of two-segment-connected quantum subsystem and uses it to discuss the complexity of the band structures of QMNs. Based on these formulae, one can dominate the number, width and position of photonic bands within designed frequencies by adjusting the NCWS and MRWL. There would be potential applications for designing optical switches, optical narrow-band filters, dense wavelength-division-multiplexing devices and other correlative waveguide network devices.
文摘In the internet of battlefield things, ammunition is becoming networked and intelligent, which depends on location information. Therefore, this paper focuses on the self-organized network collaborative localization of munitions with an aerial three-dimensional(3D) highly-dynamic topographic structure under a satellite denied environment. As for aerial networked munitions, the measurement of munitions is objectively incomplete due to the degenerated and interrupted link of munitions. For this reason, a cluster-oriented collaborative localization method is put forward in this paper. Multidimensional scaling(MDS) was first integrated with a trilateration localization method(TLM) to construct a relative localization algorithm for determining the relative location of a mobile cluster network. The information related to relative velocity was then combined into a collaborative localization framework to devise a TLM-vMDS algorithm. Finally, an iterative refinement algorithm based on scaling by majorizing a complicated function(SMACOF) was employed to effectively eliminate the influence of incomplete link observation on localization accuracy. Compared with the currently available advanced algorithms, the proposed TLM-vMDS algorithm achieves higher localization accuracy and faster convergence for a cluster of extensively networked munitions, and also offers better numerical stability and robustness for highspeed motion models.
基金supported by the National Natural Science Foundation of China(Grant No.11975307).
文摘Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different aspects.To explore the impact of location information in depth,this paper proposes an improved global structure model to characterize the influence of nodes.The method considers both the node’s self-information and the role of the location information of neighboring nodes.First,degree centrality of each node is calculated,and then degree value of each node is used to represent self-influence,and degree values of the neighbor layer nodes are divided by the power of the path length,which is path attenuation used to represent global influence.Finally,an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes.In this paper,the propagation process of a real network is obtained by simulation with the SIR model,and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy.The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.
基金The Hong Kong Polytechnic University under internal Grant No. G-YF51.
文摘In a sensor network with a large number of densely populated sensor nodes, a single target of interest may be detected by multiple sensor nodes simultaneously. Data collected from the sensor nodes are usually highly correlated, and hence energy saving using in-network data fusion becomes possible. A traditional data fusion scheme starts with dividing the network into clusters, followed by electing a sensor node as cluster head in each cluster. A cluster head is responsible for collecting data from all its cluster members, performing data fusion on these data and transmitting the fused data to the base station. Assuming that a sensor node is only capable of handling a single node-to-node transmission at a time and each transmission takes T time-slots, a cluster head with n cluster members will take at least nT time-slots to collect data from all its cluster members. In this paper, a tree-based network structure and its formation algorithms are proposed. Simulation results show that the proposed network structure can greatly reduce the delay in data collection.
基金supported by the National Science Library of Chinese Academy of Sciences
文摘Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.
文摘In the functional properties of complex networks, modules play a central role. In this paper, we propose a new method to detect and describe the modular structures of weighted networks. In order to test the proposed method, as an example, we use our method to analyse the structural properties of the Chinese railway network. Here, the stations are regarded as the nodes and the track sections are regarded as the links. Rigorous analysis of the existing data shows that using the proposed algorithm, the nodes of network can be classified naturally. Moreover, there are several core nodes in each module. Remarkably, we introduce the correlation function Grs, and use it to distinguish the different modules in weighted networks.
基金supported by The National Key Technology R&D Program (Grant No. 2011BAH19B00)The National Basic Research Program of China (973) (Grant No. 2012CB315900)The National High Technology Research and Development Program of China (863) (Grant No. 2015AA016102)
文摘Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified multi-service bearing in the IP network, the largescale access convergence network architecture is proposed. This flat access convergence structure with ultra-small hops, which shortens the service transmission path, reduces the complexity of the edge of the network, and achieves IP strong waist model with the integration of computation, storage and transmission. The key technologies are also introduced in this paper, including endto-end performance guarantee for real time interactive services, fog storing mechanism, and built-in safety transmission with integration of aggregation and control.
基金supported in part by the National Natural Science Foundation of China (Grant Nos. 60673097, 60601029, 60672126and 60702062)the National High-Tech Research and Development Plan of China (Grant Nos. 2009AA12Z210, 2008AA01Z125,2007AA12Z136 and 2007AA12Z223)+1 种基金the National Research Foundation for the Doctoral Program of Higher Education of China (Grant Nos. 20060701007 and 20070701016)Ministry&Commission-Level Research Foundation of China (Grant Nos. XADZ2008159 and 51307040103)
文摘In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region （r ≤ 0.001）, the network has even worse synchronizability than the case of isolated communities （r = 0）. Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales： the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60672095)the Fundamental Research Funds for the Central Universities,China (Grant No. KYZ201300)the Youth Sci-Tech Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024)
文摘We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.
基金National Natural Science Foundation of China (No.70471051 & No.70671074)
文摘In light of the nonlinear approaching capability of artificial neural networks （ ANN）, the term structure of interest rates is predicted using The generalized regression neural network （GRNN） and back propagation （BP） neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek＇s model and Cox-Ingersoll-Ross （CIR） model. The comparison reveals that neural network models outperform Vasicek＇s model and CIR model, which are more precise and closer to the real market situation.
文摘Fe-C-Si-Mn alloy castings used as blades in hydroelectric generators are studied and found to contain network structures after some heat treatments. Castings after annealing and normalizing were analyzed by microscope and transmission electron microscopy (TEM). The network formed during annealing was proved by TEM to be pearlite with very fine slices, while that formed during normalizing was proved by TEM and micro-hardness to be martensite or bainite. A theoretical analysis together with experimental studies has proved that the pearlite network is caused by carbon content increase in the interdendritic regions to which carbon atoms transfered from dendritic arms due to lower manganese content there during annealing, while the martensite or bainite network results from the higher hardenability of interdendritic regions where manganese content is higher. Experiments reveal that higher heating temperature or longer heating time enlarges the network size due to manganese homogenization. The network structure has a strengthening function like reinforcing rib, and the smaller the network size, the greater its strengthening capability.
文摘Abstract： The shortest path problem in a network G is to find shortest paths between some specified source vertices and terminal vertices when the lengths of edges are given. The structure of the optimal solutions set on the shortest paths is studied in this paper. First, the conditions of having unique shortest path between two distinguished vertices 8 and t in a network G are discussed; Second, the structural properties of 2-transformation ^-G graph G on the shortest-paths for G are presented heavily.
基金supported by the Shanghai Rising-Star Program (20QA1406800)the National Natural Science Foundation of China (22072091,91745102,92045301)。
文摘High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.
基金funded by the National Natural Science Foundation of China(Grant Nos.11774158 to JZ,11934008 to WW,and 11974173 to WFL)。
文摘RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to develop computational methods to predict RNA 3D structures.For these methods,designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges.In this study,we designed and trained a deep learning model to tackle this problem.The model was based on a graph convolutional network(GCN)and named RNAGCN.The model provided a natural way of representing RNA structures,avoided complex algorithms to preserve atomic rotational equivalence,and was capable of extracting features automatically out of structural patterns.Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions.Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions.RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
文摘A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory.The control problems of coupling among the channels and the uncertainty of model parameters are solved by using the method.High precise and robust tracking of required attitude angles can be achieved in complicated air space.A mathematical model of reusable launch vehicle is presented first,and then a controller of flight system is presented.Base on the mathematical model,the controller is divided into two parts:variable-structure controller and neural network module which is used to modify the parameters of controller.This control system decouples the lateraldirectional tunnels well with a neural network sliding mode controller and provides a robust and de-coupled tracking for mission angle profiles.After this a control allocation algorithm is employed to allocate the torque moments to aerodynamic control surfaces and thrusters.The final simulation shows that the control system has a good accurate,robust and de-coupled tracking performance.The stable state error is less than 1°,and the overshoot is less than 5%.
