The network diameter is an important characteristic parameter of a complex network. Calculation for a large-scale complex network’s diameter has been an important subject in the study of complex networks. If the netw...The network diameter is an important characteristic parameter of a complex network. Calculation for a large-scale complex network’s diameter has been an important subject in the study of complex networks. If the network diameter is calculated directly, the problem mainly exists in efficiency for searching and counting the shortest paths. If the network diameter is calculated indirectly by studying the statistical function about the relationship between the network diameter and parameters affecting the diameter, the problems not only exist in the efficiency of statistic, but also exist in the function which may be not applicable to all kinds of networks. An algorithm for the complex network diameter based on the k order distance matrix is proposed with a matrix multiplication approach, and a mathematical proof for the algorithm correctness is given as well. Furthermore, some relevant propositions and deductions for reducing the complexity of this algorithm are put forward. With a good theoretical basis and a simple calculation process, this algorithm can be used to calculate the diameter of a large-scale complex network with small-world effect more accurately and efficiently. Two cases about the advanced research projects agency(ARPA) network model and the Chinese airline network model are adopted to verify the effect of this algorithm.展开更多
Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, fu...Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, furthermore the simulated results are given.展开更多
The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory...The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat＇s lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure.展开更多
In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect ...In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.展开更多
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield n...In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.展开更多
The interactions between the moving dislocation within matrix channel and the interfacial misfit dislocation networks on the two-phase interfaces in Ni-based single crystal superalloys are studied carefully via atomic...The interactions between the moving dislocation within matrix channel and the interfacial misfit dislocation networks on the two-phase interfaces in Ni-based single crystal superalloys are studied carefully via atomic modeling, with special focus on the factors influ- encing the critical bowing stress of moving dislocations in the matrix channel. The results show that the moving matrix dislocation type and its position with respect to the interfacial misfit dislocation segments have considerable influences on the interactions. If the moving matrix dislocation is pure screw, it reacts with the interracial misfit dislocation segments toward dislocation linear energy reduction, which decreases the critical bowing stress of screw dislocation due to dislocation linear energy release during the dislocation reactions. If the moving matrix dislocation is of 60^-mixed type, it is obstructed by the interaction between the mixed matrix dislocations and the misfit interfacial dislocation segments. As a result, the critical bowing stress increases significantly because extra interactive energy needs to be overcome. These two different effects on the critical bowing stress become in- creasingly significant when the moving matrix dislocation is very close to the interracial misfit dislocation segments. In addition, the matrix channel width also has a significant influence on the critical bowing stress, i.e. the narrower the matrix channel is, the higher the critical bowing stress is. The classical Orowan formula is modified to predict these effects on the critical bowing stress of moving matrix dislocation, which is in good agreement with the computational results.展开更多
Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The ...Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.展开更多
A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomne...A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.展开更多
Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-por...Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.展开更多
The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guar...The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler＇s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.展开更多
The 42.1 vol. pct TiC/AZ91D magnesium-matrix composites with interpenetrating networks were fabricated by in-situ reactive infiltration process. The compressive creep behavior of as-synthesized composites was investig...The 42.1 vol. pct TiC/AZ91D magnesium-matrix composites with interpenetrating networks were fabricated by in-situ reactive infiltration process. The compressive creep behavior of as-synthesized composites was investigated at temperature ranging from 673 to 723 K under loads of 95-108 MPa. For a comparative purpose,the creep behavior of the monolithic matrix alloy AZ91D was also conducted under loads of 15-55 MPa at 548-598 K. The creep mechanisms were theoretically analyzed based on the power-law relation. The results showed that the creep rates of both TiC/AZ91D composites and AZ91D alloy increase with increasing the temperature and load. The TiC/AZ91D composites possess superior creep resistance as compared with the AZ91D alloy. At deformation temperature below 573 K, the stress exponent n of AZ91D alloy approaches theoretical value of 5, which suggests that the creep process is controlled by dislocation climb. At 598 K, the stress exponentof AZ91D is close to 3, in which viscous non-basal slip deformation plays a key role in the process of creep deformation. However, the case differs from that of AZ91D alloy when the stress exponent n of TiC/AZ91D composites exceeds 9, which shows that there exists threshold stress in the creep process of the composites, similar to other types of composites. The average activation energies for the creep of the AZ91D alloy and TiC/AZ91D composites were calculated to be 144 and 152 k J/mol, respectively. The existence of threshold stress in the creep process of the composites leads to an increase in activation energy for creep.展开更多
In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More...In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More and more researchers are attracted to be engaged in multiplex network research. A novel framework of community detection of multiplex network based on consensus matrix was presented. Firstly, this framework merges the structure of multiplex network and the information of link between each node into monoplex network. Then, the community structure information of each layer network was obtained through consensus matrix, and the traditional community division algorithm was utilized to carry out community detection of combine networks. The experimental results show that the proposed algorithm can get better performance of community partition in the real network datasets.展开更多
This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopte...This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.展开更多
Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been propose...Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.展开更多
This paper investigates the synchronization of directed networks whose coupling matrices are reducible and asymmetrical by pinning-controlled schemes. A strong sufficient condition is obtained to guarantee that the sy...This paper investigates the synchronization of directed networks whose coupling matrices are reducible and asymmetrical by pinning-controlled schemes. A strong sufficient condition is obtained to guarantee that the synchronization of the kind of networks can be achieved. For the weakly connected network, a method is presented in detail to solve two challenging fundamental problems arising in pinning control of complex networks: (1) How many nodes should be pinned? (2) How large should the coupling strength be used in a fixed complex network to realize synchronization? Then, we show the answer to the question that why all the diagonal block matrices of Perron-Frobenius normal matrices should be pinned? Besides, we find out the relation between the Perron-Frobenius normal form of coupling matrix and the differences of two synchronization conditions for strongly connected networks and weakly connected ones with linear coupling configuration. Moreover, we propose adaptive feedback algorithms to make the coupling strength as small as possible. Finally, numerical simulations are given to verify our theoretical analysis.展开更多
This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the info...This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.展开更多
This paper concerned with the quantized synchronization analysis problem. The scope of state vectors of dynamic systems, based on the matrix measure, is estimated. By using the general intermittent control, some simpl...This paper concerned with the quantized synchronization analysis problem. The scope of state vectors of dynamic systems, based on the matrix measure, is estimated. By using the general intermittent control, some simple yet generic criteria are derived ensuring the exponential stability of dynamic systems. Then, both the general intermittent networked controller and the quantized parameters can be designed, which guarantee that the nodes of the complex network are synchronized. Finally, simulation examples are given to illustrate the effectiveness and feasibility of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China(61273210)
文摘The network diameter is an important characteristic parameter of a complex network. Calculation for a large-scale complex network’s diameter has been an important subject in the study of complex networks. If the network diameter is calculated directly, the problem mainly exists in efficiency for searching and counting the shortest paths. If the network diameter is calculated indirectly by studying the statistical function about the relationship between the network diameter and parameters affecting the diameter, the problems not only exist in the efficiency of statistic, but also exist in the function which may be not applicable to all kinds of networks. An algorithm for the complex network diameter based on the k order distance matrix is proposed with a matrix multiplication approach, and a mathematical proof for the algorithm correctness is given as well. Furthermore, some relevant propositions and deductions for reducing the complexity of this algorithm are put forward. With a good theoretical basis and a simple calculation process, this algorithm can be used to calculate the diameter of a large-scale complex network with small-world effect more accurately and efficiently. Two cases about the advanced research projects agency(ARPA) network model and the Chinese airline network model are adopted to verify the effect of this algorithm.
基金Support by Science Foundation of the Ministry of Posts and Telecommunications
文摘Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, furthermore the simulated results are given.
文摘The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat＇s lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure.
基金supported by the National Natural Science Foundation of China (No.40574059)the Ministry of Education (No.NCET-04-0904)
文摘In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.
基金Project supported by the National Natural Science Foundation of China （Grant No 60674026）, the Science Foundation of Southern Yangtze University, China.
文摘In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
基金supported by the financial support from NSFC (Grant 11472113 and Grant 11272130)
文摘The interactions between the moving dislocation within matrix channel and the interfacial misfit dislocation networks on the two-phase interfaces in Ni-based single crystal superalloys are studied carefully via atomic modeling, with special focus on the factors influ- encing the critical bowing stress of moving dislocations in the matrix channel. The results show that the moving matrix dislocation type and its position with respect to the interfacial misfit dislocation segments have considerable influences on the interactions. If the moving matrix dislocation is pure screw, it reacts with the interracial misfit dislocation segments toward dislocation linear energy reduction, which decreases the critical bowing stress of screw dislocation due to dislocation linear energy release during the dislocation reactions. If the moving matrix dislocation is of 60^-mixed type, it is obstructed by the interaction between the mixed matrix dislocations and the misfit interfacial dislocation segments. As a result, the critical bowing stress increases significantly because extra interactive energy needs to be overcome. These two different effects on the critical bowing stress become in- creasingly significant when the moving matrix dislocation is very close to the interracial misfit dislocation segments. In addition, the matrix channel width also has a significant influence on the critical bowing stress, i.e. the narrower the matrix channel is, the higher the critical bowing stress is. The classical Orowan formula is modified to predict these effects on the critical bowing stress of moving matrix dislocation, which is in good agreement with the computational results.
基金supported by No. DST/INSPIRE Fellowship/2010/[293]/dt. 18/03/2011
文摘Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(61307121)ABRP of Datong(2017127)the Ph.D.’s Initiated Research Projects of Datong University(2013-B-17,2015-B-05)
文摘A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.
基金This work was financially supported by the Natural Science Foundation of Shandong Province, China (Y2006F03).
文摘Reticulated polyurethane was chosen as the preceramic material for preparing the porous preform using the replication process. The immersing and sintering processes were each performed twice for fabricating a high-porosity and super-strong skeleton. The aluminum magnesium matrix composites reinforced with three-dimensional network structure were prepared using the infiltration technique by pressure assisting and vacuum driving. Light interfacial reactions have played a profitable role in most of the ceramic-metal systems. The metal matrix composites interpenetrated with the ceramic phase have a higher wear resistance than the metal matrix phase. The volume fraction of ceramic reinforcement has a significant effect on the abrasive wear, and the wear rate can be decreased with the increase of the volume fraction of reinforcement.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60974004)the Natural Science Foundation of Jilin Province,China (Grant No. 201115222)
文摘The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler＇s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.
基金Financial supports from Natural Science Foundation （Grant No. 20032012）；Liaoning Province, China； the Scientific Research Foundation for the Returned 0verseas Chinese Scholars；State Education Ministry, China and from the Starting for New Scientific Researchers of Institute of Metal Research （IMR）；Chinese Academy of Sciences （CAS）, are gratefully acknowledged.
文摘The 42.1 vol. pct TiC/AZ91D magnesium-matrix composites with interpenetrating networks were fabricated by in-situ reactive infiltration process. The compressive creep behavior of as-synthesized composites was investigated at temperature ranging from 673 to 723 K under loads of 95-108 MPa. For a comparative purpose,the creep behavior of the monolithic matrix alloy AZ91D was also conducted under loads of 15-55 MPa at 548-598 K. The creep mechanisms were theoretically analyzed based on the power-law relation. The results showed that the creep rates of both TiC/AZ91D composites and AZ91D alloy increase with increasing the temperature and load. The TiC/AZ91D composites possess superior creep resistance as compared with the AZ91D alloy. At deformation temperature below 573 K, the stress exponent n of AZ91D alloy approaches theoretical value of 5, which suggests that the creep process is controlled by dislocation climb. At 598 K, the stress exponentof AZ91D is close to 3, in which viscous non-basal slip deformation plays a key role in the process of creep deformation. However, the case differs from that of AZ91D alloy when the stress exponent n of TiC/AZ91D composites exceeds 9, which shows that there exists threshold stress in the creep process of the composites, similar to other types of composites. The average activation energies for the creep of the AZ91D alloy and TiC/AZ91D composites were calculated to be 144 and 152 k J/mol, respectively. The existence of threshold stress in the creep process of the composites leads to an increase in activation energy for creep.
基金The National Key Basic Research and Department Program of China(No.2013CB329606)
文摘In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More and more researchers are attracted to be engaged in multiplex network research. A novel framework of community detection of multiplex network based on consensus matrix was presented. Firstly, this framework merges the structure of multiplex network and the information of link between each node into monoplex network. Then, the community structure information of each layer network was obtained through consensus matrix, and the traditional community division algorithm was utilized to carry out community detection of combine networks. The experimental results show that the proposed algorithm can get better performance of community partition in the real network datasets.
基金Project supported by the National Natural Science Foundation of China(Grant No.61304064)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant Nos.15B067 and 16C0475)a Discovering Grant from Australian Research Council
文摘This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.
文摘Functional brain networks (FBNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Due to its importance, many FBN construction methods have been proposed currently, including the low-order Pearson’s correlation (PC) and sparse representation (SR), as well as the high-order functional connection (HoFC). However, most existing methods usually ignore the information of topological structures of FBN, such as low-rank structure which can reduce the noise and improve modularity to enhance the stability of networks. In this paper, we propose a novel method for improving the estimated FBNs utilizing matrix factorization (MF). More specifically, we firstly construct FBNs based on three traditional methods, including PC, SR, and HoFC. Then, we reduce the rank of these FBNs via MF model for estimating FBN with low-rank structure. Finally, to evaluate the effectiveness of the proposed method, experiments have been conducted to identify the subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) from norm controls (NCs) using the estimated FBNs. The results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate that the classification performances achieved by our proposed method are better than the selected baseline methods.
文摘This paper investigates the synchronization of directed networks whose coupling matrices are reducible and asymmetrical by pinning-controlled schemes. A strong sufficient condition is obtained to guarantee that the synchronization of the kind of networks can be achieved. For the weakly connected network, a method is presented in detail to solve two challenging fundamental problems arising in pinning control of complex networks: (1) How many nodes should be pinned? (2) How large should the coupling strength be used in a fixed complex network to realize synchronization? Then, we show the answer to the question that why all the diagonal block matrices of Perron-Frobenius normal matrices should be pinned? Besides, we find out the relation between the Perron-Frobenius normal form of coupling matrix and the differences of two synchronization conditions for strongly connected networks and weakly connected ones with linear coupling configuration. Moreover, we propose adaptive feedback algorithms to make the coupling strength as small as possible. Finally, numerical simulations are given to verify our theoretical analysis.
基金supported by the Science Foundation of the Department of Science and Technology,New Delhi,India (Grant No.SR/S4/MS:485/07)
文摘This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.
文摘This paper concerned with the quantized synchronization analysis problem. The scope of state vectors of dynamic systems, based on the matrix measure, is estimated. By using the general intermittent control, some simple yet generic criteria are derived ensuring the exponential stability of dynamic systems. Then, both the general intermittent networked controller and the quantized parameters can be designed, which guarantee that the nodes of the complex network are synchronized. Finally, simulation examples are given to illustrate the effectiveness and feasibility of the proposed method.
