Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from ...Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.展开更多
A novel approach to widening the active reflection coefficient(ARC)bandwidth of an antenna array,employing a parasitic coupling network(PCN),is investigated in this article.Different from traditional tightly coupled a...A novel approach to widening the active reflection coefficient(ARC)bandwidth of an antenna array,employing a parasitic coupling network(PCN),is investigated in this article.Different from traditional tightly coupled arrays adopting space structures for enhancing the coupling in balanced-excitation antennas,a PCN derived from rigorous formulas is employed in the feeding lines of unbalanced-excitation ones.Based on network analysis,the mutual coupling utilization condition for an(M×N)-element antenna array is initially deduced,and the PCN is implemented.Then,the PCNs are realized by introducing a parasitic element and a coupling network between the two-element H-plane and E-plane dual-layer coupled microstrip antenna arrays,resulting in 10.9%and 30.8%bandwidth enhancements compared with the original arrays,respectively.Moreover,the PCNs are further expanded to multielement antenna arrays,including three-and five-element one-dimensional and 8×2 twodimensional arrays,exhibiting approximately 40%overlapped ARC bandwidths with normal radiation patterns,steady gains,and applicable scanning characteristics.The results indicate its potential application in large-scale wideband arrays.展开更多
This study explores whether manager mobility can influence syndications between private equity(PE)firms by constructing coupling network models.Using data from China’s private equity market from 1993 to 2017,we found...This study explores whether manager mobility can influence syndications between private equity(PE)firms by constructing coupling network models.Using data from China’s private equity market from 1993 to 2017,we found that driving forces,resistant forces,and network structure play significant roles in determining resource flows between PE firms.Specifically,driving forces indicate that managers moving from domestic and foreign PE firms to state-owned PE firms are more likely to induce syndications.Furthermore,if the manager is promoted when changing jobs,mobility is likely to enhance the flow of resources.Resistant forces indicate that increased geographical distance reduces syndications.As for the influence of structure,if managers leave PE firms with higher status,they are more likely to induce syndications.This study contributes to the coupling network literature by providing a clarified three-factor framework.By exploring the characteristic of managers in state-owned private equity firms,we specified the syndication theory in China.This study can help private equity firms hire valuable managers and expand syndication networks in practice.展开更多
The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activa...The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-com- puting is a practical and advanced tool for solving large-scale underground rock engineering problems.展开更多
In recent years,most studies of complex networks have focused on a single network and ignored the interaction of multiple networks,much less the coupling mechanisms between multiplex networks.In this paper we investig...In recent years,most studies of complex networks have focused on a single network and ignored the interaction of multiple networks,much less the coupling mechanisms between multiplex networks.In this paper we investigate synchronization phenomena in multilayer networks with nonidentical topological structures based on three specific coupling mechanisms:assortative,disassortative,and anti-assortative couplings.We find rich and complex synchronous dynamic phenomena in coupled networks.We also study the behavior of effective frequencies for layers I and II to understand the underlying microscopic dynamics occurring under the three different coupling mechanisms.In particular,the coupling mechanisms proposed here have strong robustness and effectiveness and can produce abundant synchronization phenomena in coupled networks.展开更多
This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks,in which the Markov process is used to model the mixed cyberattacks.To optimize the utiliz...This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks,in which the Markov process is used to model the mixed cyberattacks.To optimize the utilization of channel resources,a decentralized event-triggered mechanism is adopted during the information transmission.By establishing the augmentation system and constructing the Lyapunov function,sufficient conditions are obtained for the system to be finite-time bounded and satisfy the H_(∞ )performance index.Then,under these conditions,a suitable state estimator gain is obtained.Finally,the feasibility of the method is verified by a given illustrative example.展开更多
In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the...In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.展开更多
With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study c...With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.展开更多
In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also...In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.展开更多
In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,r...In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.展开更多
Inhibitory coupled bursting Hindmarsh-Rose neurons are considered as constitutive units of the Macaque corti- cal network. In the absence of information transmission delay the bursting activity is desynchronized, givi...Inhibitory coupled bursting Hindmarsh-Rose neurons are considered as constitutive units of the Macaque corti- cal network. In the absence of information transmission delay the bursting activity is desynchronized, giving rise to spatiotemporally disordered dynamics. This paper shows that the introduction of finite delays can lead to the synchro- nization of bursting and thus to the emergence of coherent propagating fronts of excitation in the space-time domain. Moreover, it shows that the type of synchronous bursting is uniquely determined by the delay length, with the transi- tions from one type to the other occurring in a step-like manner depending on the delay. Interestingly, as the delay is tuned close to the transition points, the synchronization deteriorates, which implies the coexistence of different bursting attractors. These phenomena can be observed by different but fixed coupling strengths, thus indicating a new role for information transmission delays in realistic neuronal networks.展开更多
The economic and financial systems consist of many nonlinear factors that make them behave as the complex systems.Recently many chaotic finance systems have been proposed to study the complex dynamics of finance as a ...The economic and financial systems consist of many nonlinear factors that make them behave as the complex systems.Recently many chaotic finance systems have been proposed to study the complex dynamics of finance as a noticeable problem in economics.In fact,the intricate structure between financial institutions can be obtained by using a network of financial systems.Therefore,in this paper,we consider a ring network of coupled symmetric chaotic finance systems,and investigate its behavior by varying the coupling parameters.The results show that the coupling strength and range have significant effects on the behavior of the coupled systems,and various patterns such as the chimera and multi-chimera states are observed.Furthermore,changing the parameters'values,remarkably influences on the oscillators attractors.When several synchronous clusters are formed,the attractors of the synchronized oscillators are symmetric,but different from the single oscillator attractor.展开更多
This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated l...This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.展开更多
Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain ...Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain vibration test data in 36damage cases. A coupling neural network (NN) based on multi-sensor information fusion is proposed to achieve identification of damage occurrence, damage localization and damage quantification, respectively. First, wavelet packet transform (WPT) is used to extract features of vibration test data from structure with different damage extent. Then, data fusion is conducted by assembling feature vectors of different type sensors. Finally, three sets of coupling NN are constructed to implement decision fusion and damage identification. The results of experimental study proved the validity and feasibility of the proposed methodology.展开更多
Chimera state is a peculiar spatiotemporal pattern,wherein the coherence and incoherence coexist in the network of coupled identical oscillators.In this paper,we study the chimera states in a network of impact oscilla...Chimera state is a peculiar spatiotemporal pattern,wherein the coherence and incoherence coexist in the network of coupled identical oscillators.In this paper,we study the chimera states in a network of impact oscillators with nonlocal coupling.We investigate the effects of the coupling strength and the coupling range on the network behavior.The results reveal the emergence of the chimera state for significantly small values of coupling strength,and higher coupling strength values lead to unbounded motions in the oscillators.We also study the network in the case of excitation failure.We observe that the coupling helps in the maintenance of an oscillatory motion with a lower amplitude in the failed oscillator.展开更多
We investigate the synchronization ability of four types of regular coupled networks. By introducing the proper error variables and Lyapunov functions, we turn the stability of synchronization manifold into that of nu...We investigate the synchronization ability of four types of regular coupled networks. By introducing the proper error variables and Lyapunov functions, we turn the stability of synchronization manifold into that of null solution of error equations, further, into the negative definiteness of some symmetric matrices, thus we get the sufficient synchronization stability conditions. To test the valid of the results, we take the Chua's circuit as an example. Although the theoretical synchronization thresholds appear to be very conservative, they provide new insights about the influence of topology and scale of networks on synchronization, and that the theoretical results and our numerical simulations are consistent.展开更多
This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behavior...This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.展开更多
An irregular segmented region coding algorithm based on pulse coupled neural network(PCNN) is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approx...An irregular segmented region coding algorithm based on pulse coupled neural network(PCNN) is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approximate gray values can be activated simultaneously. One can draw a conclusion that PCNN has the advantage of realizing the regional segmentation, and the details of original image can be achieved by the parameter adjustment of segmented images, and at the same time, the trivial segmented regions can be avoided. For the better approximation of irregular segmented regions, the Gram-Schmidt method, by which a group of orthonormal basis functions is constructed from a group of linear independent initial base functions, is adopted. Because of the orthonormal reconstructing method, the quality of reconstructed image can be greatly improved and the progressive image transmission will also be possible.展开更多
Large-scale development of electric vehicles(EVs)exposes power grids and transportation networks with limited capacity to increased fault risks.In this paper,a method to prevent fault risks in advance by using the fle...Large-scale development of electric vehicles(EVs)exposes power grids and transportation networks with limited capacity to increased fault risks.In this paper,a method to prevent fault risks in advance by using the flexibility of EV travel to coordinate the operation of dis-tribution and transportation networks is proposed.Since EV travel decisions are influenced by the charging and travel time costs,adjusting charging price and travel time price can help guide behavior and enable coordinated operation of power and transportation networks.First,risk-based distribution locational marginal prices(RDLMPs)are established to restrain the distribution network risks.Second,traffic risks are formulated using origin-destination(OD)risk marginal prices(ODRMPs)considering the degree of traffic congestion fault risks.Under the guidance of the RDLMPs and ODRMPs,EV fleets optimize their travel plans to minimize overall costs,including charging and time costs.Finally,case studies verify that the proposed method can reduce the opera-tional risks of both distribution and transportation net-works.展开更多
Ti_(3)C_(2)Tx MXene is widely regarded as a potential micro-wave absorber due to its dielectric multi-layered structure.However,missing magnetic loss capability of pure MXene leads to the unmatched electromagnetic par...Ti_(3)C_(2)Tx MXene is widely regarded as a potential micro-wave absorber due to its dielectric multi-layered structure.However,missing magnetic loss capability of pure MXene leads to the unmatched electromagnetic parameters and unsatisfied impedance matching condi-tion.Herein,with the inspiration from dielectric-magnetic synergy,this obstruction is solved by fabricating magnetic CNTs/Ni hetero-structure decorated MXene substrate via a facile in situ induced growth method.Ni2+ions are successfully attached on the surface and interlamination of each MXene unit by intensive electrostatic adsorption.Benefiting from the possible“seed-germination”effect,the“seeds”Ni^(2+)grow into“buds”Ni nanoparticles and“stem”carbon nanotubes(CNTs)from the enlarged“soil”of MXene skeleton.Due to the improved impedance matching con-dition,the MXene-CNTs/Ni hybrid holds a superior microwave absorp-tion performance of−56.4 dB at only 2.4 mm thickness.Such a distinctive 3D architecture endows the hybrids:(i)a large-scale 3D magnetic coupling network in each dielectric unit that leading to the enhanced magnetic loss capability,(ii)a massive multi-heterojunction interface structure that resulting in the reinforced polarization loss capability,confirmed by the off-axis electron holography.These outstanding results provide novel ideas for developing magnetic MXene-based absorbers.展开更多
基金the National Natural Science Foundation of China(Grant No.62071248)。
文摘Information diffusion in complex networks has become quite an active research topic.As an important part of this field,intervention against information diffusion processes is attracting ever-increasing attention from network and control engineers.In particular,it is urgent to design intervention schemes for the coevolutionary dynamics between information diffusion processes and coupled networks.For this purpose,we comprehensively study the problem of information diffusion intervention over static and temporal coupling networks.First,individual interactions are described by a modified activitydriven network(ADN)model.Then,we establish a novel node-based susceptible-infected-recovered-susceptible(SIRS)model to characterize the information diffusion dynamics.On these bases,three synergetic intervention strategies are formulated.Second,we derive the critical threshold of the controlled-SIRS system via stability analysis.Accordingly,we exploit a spectral optimization scheme to minimize the outbreak risk or the required budget.Third,we develop an optimal control scheme of dynamically allocating resources to minimize both system loss and intervention expense,in which the optimal intervention inputs are obtained through optimal control theory and a forward-backward sweep algorithm.Finally,extensive simulation results validate the accuracy of theoretical derivation and the performance of our proposed intervention schemes.
基金supported by the National Natural Science Foundation of China(Nos.62201579 and 62201580)the Youth Independent Innovation Science Foundation of National University of Defense Technology(No.ZK24-39)。
文摘A novel approach to widening the active reflection coefficient(ARC)bandwidth of an antenna array,employing a parasitic coupling network(PCN),is investigated in this article.Different from traditional tightly coupled arrays adopting space structures for enhancing the coupling in balanced-excitation antennas,a PCN derived from rigorous formulas is employed in the feeding lines of unbalanced-excitation ones.Based on network analysis,the mutual coupling utilization condition for an(M×N)-element antenna array is initially deduced,and the PCN is implemented.Then,the PCNs are realized by introducing a parasitic element and a coupling network between the two-element H-plane and E-plane dual-layer coupled microstrip antenna arrays,resulting in 10.9%and 30.8%bandwidth enhancements compared with the original arrays,respectively.Moreover,the PCNs are further expanded to multielement antenna arrays,including three-and five-element one-dimensional and 8×2 twodimensional arrays,exhibiting approximately 40%overlapped ARC bandwidths with normal radiation patterns,steady gains,and applicable scanning characteristics.The results indicate its potential application in large-scale wideband arrays.
基金supported by the Fundamental Research Funds for the Central Universities(No.FRF-TP-22-063A1)the General Program of the National Natural Science Foundation of China(No.71874099).
文摘This study explores whether manager mobility can influence syndications between private equity(PE)firms by constructing coupling network models.Using data from China’s private equity market from 1993 to 2017,we found that driving forces,resistant forces,and network structure play significant roles in determining resource flows between PE firms.Specifically,driving forces indicate that managers moving from domestic and foreign PE firms to state-owned PE firms are more likely to induce syndications.Furthermore,if the manager is promoted when changing jobs,mobility is likely to enhance the flow of resources.Resistant forces indicate that increased geographical distance reduces syndications.As for the influence of structure,if managers leave PE firms with higher status,they are more likely to induce syndications.This study contributes to the coupling network literature by providing a clarified three-factor framework.By exploring the characteristic of managers in state-owned private equity firms,we specified the syndication theory in China.This study can help private equity firms hire valuable managers and expand syndication networks in practice.
基金This work was financially supported by the Key Project for National Science of "9.5" (Reward Ⅱ for National Science and Technol
文摘The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-com- puting is a practical and advanced tool for solving large-scale underground rock engineering problems.
基金Project supported by the National Natural Science Foundation of China(Grants Nos.71801066 and 71704046)the Natural Science Foundation of Anhui Province,China(Grant Nos.1808085QG225 and 1908085MA22)+1 种基金the FundamentalResearch Funds for the Central Universities,China(Grant Nos.JZ2020HGTB0021 and JZ2021HGTB0065)the Outstanding Young Talent Support Program in Universities of Anhui Province in 2020 year。
文摘In recent years,most studies of complex networks have focused on a single network and ignored the interaction of multiple networks,much less the coupling mechanisms between multiplex networks.In this paper we investigate synchronization phenomena in multilayer networks with nonidentical topological structures based on three specific coupling mechanisms:assortative,disassortative,and anti-assortative couplings.We find rich and complex synchronous dynamic phenomena in coupled networks.We also study the behavior of effective frequencies for layers I and II to understand the underlying microscopic dynamics occurring under the three different coupling mechanisms.In particular,the coupling mechanisms proposed here have strong robustness and effectiveness and can produce abundant synchronization phenomena in coupled networks.
基金Project supported by the National Natural Science Foundation of China(Grant No.62303016)the Research and Development Project of Engineering Research Center of Biofilm Water Purification and Utilization Technology of the Ministry of Education of China(Grant No.BWPU2023ZY02)+1 种基金the University Synergy Innovation Program of Anhui Province,China(Grant No.GXXT-2023-020)the Key Project of Natural Science Research in Universities of Anhui Province,China(Grant No.2024AH050171).
文摘This article investigates the issue of finite-time state estimation in coupled neural networks under random mixed cyberattacks,in which the Markov process is used to model the mixed cyberattacks.To optimize the utilization of channel resources,a decentralized event-triggered mechanism is adopted during the information transmission.By establishing the augmentation system and constructing the Lyapunov function,sufficient conditions are obtained for the system to be finite-time bounded and satisfy the H_(∞ )performance index.Then,under these conditions,a suitable state estimator gain is obtained.Finally,the feasibility of the method is verified by a given illustrative example.
基金supported in part by the National Key Research and Development Program of China(Grant No.2019YFA0706200).
文摘In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data traffic.Meanwhile,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication paradigm.However,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic information.With the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to increase.In contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image transmission.However,the parameters of PCNN are determined by trial and error,which limits its application.To overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this work.The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images.Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets.The IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.
基金supported by National Natural Science Foundation of China(Grant No.71673256).
文摘With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.
基金supported by the National Natural Science Foundation of China under Grant No. 60874088 and No. 11072059the Scientific Research Fund of Yunnan Province under Grant No. 2010ZC150the Scientific Research Fund of Yunnan Provincial Education Department under Grant No. 07Y10085
文摘In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.
基金supported by the National Natural Science Foundation of China(Nos.4210040255,U19A2086)the Sichuan Science and Technology Program(No.2021JDRC0108)。
文摘In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.
基金supported by the National Natural Science Foundation of China (Grant Nos. 10972001,10702023 and 10832006)Matjaz Perc individually acknowledges support from the Slovenian Research Agency (Grant No. Z1-2032)
文摘Inhibitory coupled bursting Hindmarsh-Rose neurons are considered as constitutive units of the Macaque corti- cal network. In the absence of information transmission delay the bursting activity is desynchronized, giving rise to spatiotemporally disordered dynamics. This paper shows that the introduction of finite delays can lead to the synchro- nization of bursting and thus to the emergence of coherent propagating fronts of excitation in the space-time domain. Moreover, it shows that the type of synchronous bursting is uniquely determined by the delay length, with the transi- tions from one type to the other occurring in a step-like manner depending on the delay. Interestingly, as the delay is tuned close to the transition points, the synchronization deteriorates, which implies the coexistence of different bursting attractors. These phenomena can be observed by different but fixed coupling strengths, thus indicating a new role for information transmission delays in realistic neuronal networks.
文摘The economic and financial systems consist of many nonlinear factors that make them behave as the complex systems.Recently many chaotic finance systems have been proposed to study the complex dynamics of finance as a noticeable problem in economics.In fact,the intricate structure between financial institutions can be obtained by using a network of financial systems.Therefore,in this paper,we consider a ring network of coupled symmetric chaotic finance systems,and investigate its behavior by varying the coupling parameters.The results show that the coupling strength and range have significant effects on the behavior of the coupled systems,and various patterns such as the chimera and multi-chimera states are observed.Furthermore,changing the parameters'values,remarkably influences on the oscillators attractors.When several synchronous clusters are formed,the attractors of the synchronized oscillators are symmetric,but different from the single oscillator attractor.
基金Project supported by National Natural Science Foundation of China (Grant No 60674026)the Jiangsu Provincial Natural Science Foundation of China (Grant No BK2007016)Program for Innovative Research Team of Jiangnan University,China
文摘This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
文摘Too many sensors and data information in structural health monitoring system raise the problem of how to realize multi-sensor information fusion. An experiment on a three-story frame structure was conducted to obtain vibration test data in 36damage cases. A coupling neural network (NN) based on multi-sensor information fusion is proposed to achieve identification of damage occurrence, damage localization and damage quantification, respectively. First, wavelet packet transform (WPT) is used to extract features of vibration test data from structure with different damage extent. Then, data fusion is conducted by assembling feature vectors of different type sensors. Finally, three sets of coupling NN are constructed to implement decision fusion and damage identification. The results of experimental study proved the validity and feasibility of the proposed methodology.
基金Project supported by the Polish National Science Centre,MAESTRO Programme(No.2013/08/A/ST8/00780)the OPUS Programme(No.2018/29/B/ST8/00457)。
文摘Chimera state is a peculiar spatiotemporal pattern,wherein the coherence and incoherence coexist in the network of coupled identical oscillators.In this paper,we study the chimera states in a network of impact oscillators with nonlocal coupling.We investigate the effects of the coupling strength and the coupling range on the network behavior.The results reveal the emergence of the chimera state for significantly small values of coupling strength,and higher coupling strength values lead to unbounded motions in the oscillators.We also study the network in the case of excitation failure.We observe that the coupling helps in the maintenance of an oscillatory motion with a lower amplitude in the failed oscillator.
基金Supported by National Natural Science Foundation under Grant No.11002073the Fundamental Research Funds for the Central Universities under Grant No.2011RC0702
文摘We investigate the synchronization ability of four types of regular coupled networks. By introducing the proper error variables and Lyapunov functions, we turn the stability of synchronization manifold into that of null solution of error equations, further, into the negative definiteness of some symmetric matrices, thus we get the sufficient synchronization stability conditions. To test the valid of the results, we take the Chua's circuit as an example. Although the theoretical synchronization thresholds appear to be very conservative, they provide new insights about the influence of topology and scale of networks on synchronization, and that the theoretical results and our numerical simulations are consistent.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61134012 and 11271146)
文摘This paper is concerned with the exponential synchronization problem of coupled memristive neural networks. In contrast to general neural networks, memristive neural networks exhibit state-dependent switching behaviors due to the physical properties of memristors. Under a mild topology condition, it is proved that a small fraction of controlled sub- systems can efficiently synchronize the coupled systems. The pinned subsystems are identified via a search algorithm. Moreover, the information exchange network needs not to be undirected or strongly connected. Finally, two numerical simulations are performed to verify the usefulness and effectiveness of our results.
基金National Natural Science Foundation of China(60572011) 985 Special Study Project(LZ85 -231 -582627)
文摘An irregular segmented region coding algorithm based on pulse coupled neural network(PCNN) is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approximate gray values can be activated simultaneously. One can draw a conclusion that PCNN has the advantage of realizing the regional segmentation, and the details of original image can be achieved by the parameter adjustment of segmented images, and at the same time, the trivial segmented regions can be avoided. For the better approximation of irregular segmented regions, the Gram-Schmidt method, by which a group of orthonormal basis functions is constructed from a group of linear independent initial base functions, is adopted. Because of the orthonormal reconstructing method, the quality of reconstructed image can be greatly improved and the progressive image transmission will also be possible.
基金supported by the Project of State Grid Corporation of China(No.5108-202299265A-1-0-ZB).
文摘Large-scale development of electric vehicles(EVs)exposes power grids and transportation networks with limited capacity to increased fault risks.In this paper,a method to prevent fault risks in advance by using the flexibility of EV travel to coordinate the operation of dis-tribution and transportation networks is proposed.Since EV travel decisions are influenced by the charging and travel time costs,adjusting charging price and travel time price can help guide behavior and enable coordinated operation of power and transportation networks.First,risk-based distribution locational marginal prices(RDLMPs)are established to restrain the distribution network risks.Second,traffic risks are formulated using origin-destination(OD)risk marginal prices(ODRMPs)considering the degree of traffic congestion fault risks.Under the guidance of the RDLMPs and ODRMPs,EV fleets optimize their travel plans to minimize overall costs,including charging and time costs.Finally,case studies verify that the proposed method can reduce the opera-tional risks of both distribution and transportation net-works.
基金supported by the National Natural Science Foundation of China(51725101,11727807,51672050,61790581)the Ministry of Science and Technology of China(2018YFA0209102)。
文摘Ti_(3)C_(2)Tx MXene is widely regarded as a potential micro-wave absorber due to its dielectric multi-layered structure.However,missing magnetic loss capability of pure MXene leads to the unmatched electromagnetic parameters and unsatisfied impedance matching condi-tion.Herein,with the inspiration from dielectric-magnetic synergy,this obstruction is solved by fabricating magnetic CNTs/Ni hetero-structure decorated MXene substrate via a facile in situ induced growth method.Ni2+ions are successfully attached on the surface and interlamination of each MXene unit by intensive electrostatic adsorption.Benefiting from the possible“seed-germination”effect,the“seeds”Ni^(2+)grow into“buds”Ni nanoparticles and“stem”carbon nanotubes(CNTs)from the enlarged“soil”of MXene skeleton.Due to the improved impedance matching con-dition,the MXene-CNTs/Ni hybrid holds a superior microwave absorp-tion performance of−56.4 dB at only 2.4 mm thickness.Such a distinctive 3D architecture endows the hybrids:(i)a large-scale 3D magnetic coupling network in each dielectric unit that leading to the enhanced magnetic loss capability,(ii)a massive multi-heterojunction interface structure that resulting in the reinforced polarization loss capability,confirmed by the off-axis electron holography.These outstanding results provide novel ideas for developing magnetic MXene-based absorbers.
