Intervention against information diffusion in static and temporal coupling networks

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.

Manager Mobility and Private Equity Syndications from the Perspective of Coupling Networks:Evidence from China’s Private Equity Industry

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.

An improved pulse coupled neural networks model for semantic IoT

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.

Synchronization in multilayer networks through different coupling mechanisms

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.

ADAPTIVE PINNING SYNCHRONIZATION OF COUPLED NEURAL NETWORKS WITH MIXED DELAYS AND VECTOR-FORM STOCHASTIC PERTURBATIONS

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.

Evaluation on Stability of Stope Structure Based on Nonlinear Dynamics of Coupling Artificial Neural Network

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.

Adaptive synchronization in an array of asymmetric coupled neural networks

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.

Experimental study of structural damage identification based on WPT and coupling NN

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.

Exponential synchronization of coupled memristive neural networks via pinning control

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.

Epidemic Spreading–Information Dissemination Coupling Mechanism in Heterogeneous Areas

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.

3D Seed-Germination-Like MXene with In Situ Growing CNTs/Ni Heterojunction for Enhanced Microwave Absorption via Polarization and Magnetization

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.

Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm

According to the characteristics of dynamic firing in pulse coupled neural network (PCNN) and regional configuration in retinal blood vessel network, a new method combined with simplified PCNN and fast 2D-Otsu algorithm was proposed for automated retinal blood vessels segmentation. Firstly, 2D Gaussian matched filter was used to enhance the retinal images and simplified PCNN was employed to segment the blood vessels by firing neighborhood neurons. Then, fast 2D-Otsu algorithm was introduced to search the best segmentation results and iteration times with less computation time. Finally, the whole vessel network was obtained via analyzing the regional connectivity. Experiments implemented on the public Hoover database indicate that this new method gets a 0.803 5 true positive rate and a 0.028 0 false positive rate on an average. According to the test results, compared with Hoover algorithm and method of PCNN and 1D-Otsu, the proposed method shows much better performance.

Numerical simulation of direct shear tests on mechanical properties of talus deposits based on self-adaptive PCNN digital image processing

The macro mechanical properties of materials with characteristics of large scale and complicated structural composition can be analyzed through its reconstructed meso-structures.In this work,the meso-structures of talus deposits that widely exist in the hydro-power engineering in the southwest of China were first reconstructed by small particles according to the in-situ photographs based on the self-adaptive PCNN digital image processing,and then numerical direct shear tests were carried out for studying the mechanical properties of talus deposits.Results indicate that the reconstructed meso-structures of talus deposits are more consistent with the actual situation because the self-adaptive PCNN digital image processing has a higher discrimination in the details of soil-rock segmentation.The existence and random distribution of rock blocks make the initial shear stiffness,the peak strength and the residual strength higher than those of the "pure soil" with particle size less than 1.25 cm apparently,but reduce the displacements required for the talus deposits reaching its peak shear strength.The increase of rock proportion causes a significant improvement in the internal friction angle of talus deposit,which to a certain degree leads to the characteristics of shear stress-displacement curves having a changing trend from the plastic strain softening deformation to the nonlinear strain hardening deformation,while an unconspicuous increase in cohesion.The uncertainty and heterogeneity of rock distributions cause the differences of rock proportion within shear zone,leading to a relatively strong fluctuation in peak strengths during the shear process,while movement features of rock blocks,such as translation,rotation and crossing,expand the scope of shear zone,increase the required shear force,and also directly lead to the misjudgment that the lower shear strength is obtained from the samples with high rock proportion.That,however,just explains the reason why the shear strength gained from a small amount of indoor test data is not consistent with engineering practice.

Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

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.

Taming desynchronized bursting with delays in the Macaque cortical network

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.

PCNN based image processing and feature extraction of dual-bypass gas metal arc weld pool

In manual welding process, skilled welders can adjust the welding parameters to ensure the weld quality through their observation of the weld pool surface. In order to acquire useful information of the weld pool for control of the welding process and realizing the automatic welding, the measurement system of DB-GMA W process was established and the weld pool image was obtained by passive vision. Then, three image processing algorithms, Sobel, Canny, and pulse coupled neural network （PCNN） were detailed and applied to extracting the edge of the DB-GMA weld pool. In addition, a scheme was proposed for calculating the length, maximum width and superficial area of the weld pool under different welding conditions. The compared results show that the PCNN algorithm can be used for extracting the edge of the weld pool and the obtained information is more useful and accurate. The calculated results coincide with the actual measurement well, which demonstrates that the proposed algorithm is effective, its imaging processing time is required only 20 ms, which can completely meet the requirement of real-time control.

Shadow detection combining characters of human vision

A shadow detection method using pulse couple neural network inspired by the characters of human visual system is proposed.More precisely,lateral inhibition of human vision and coefficient of variation are combined together to improve the pulse couple neural network.Shadow detection is considered to be a shadow region segmentation problem.Experiment shows that the presented method is consistent with human vision compared to shadow detection methods based on HSV and pulse couple neural network(PCNN) by both subjective and objective assessments.

Irregular Segmented Region Compression Coding Based on Pulse Coupled Neural Network

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.

Distribution System Restoration with Cyber Failures Based on Co-dispatching of Multiple Recovery Resources

Improving the restoration efficiency of a distribution system is essential to enhance the ability of power systems to deal with extreme events.The distribution system restoration(DSR)depends on the interaction among the electric network(EN),cyber network(CN),and traffic network(TN).However,the coordination of these three networks and codispatching of multiple recovery resources have been mostly neglected.This paper proposes a novel DSR framework,which is formulated as a mixed-integer linear programming(MILP)problem.The failures in cyber lines result in cyber blind areas,which restrict the normal operation of remote-controlled switches.To accelerate the recovery process,multiple recovery resources are utilized including electric maintenance crews(EMCs),cyber maintenance crews(CMCs),and emergency communication vehicles(ECVs).Specifically,CMCs and ECVs restore the cyber function of switches in cooperation,and EMCs repair damaged electric lines.The travel time of these three dispatchable resources is determined by TN.The effectiveness and superiority of the proposed framework are verified on the modified IEEE 33-node and 123-node test systems.

Proactive Resilience Enhancement of Power Systems with Link Transmission Model-based Dynamic Traffic Assignment Among Electric Vehicles

The rapid development of electric vehicles(EVs)is strengthening the bi-directional interactions between electric power networks(EPNs)and transportation networks(TNs)while providing opportunities to enhance the resilience of power systems towards extreme events.To quantify the temporal and spatial flexibility of EVs for charging and discharging,a novel dynamic traffic assignment(DTA)problem is proposed.The DTA problem is based on a link transmission model(LTM)with extended charging links,depicting the interaction between EVs and power systems.It models the charging rates as continuous variables by an energy boundary model.To consider the evacuation requirements of TNs and the uncertainties of traffic conditions,the DTA problem is extended to a two-stage distributionally robust version.It is further incorporated into a two-stage distributionally robust unit commitment problem to balance the enhancement of EPNs and the performance of TNs.The problem is reformulated into a mixed-integer linear programming problem and solved by off-the-shelf commercial solvers.Case studies are performed on two test networks.The effectiveness is verified by the numerical results,e.g.,reducing the load shedding amount without increasing the unmet traffic demand.