Coupled Network Model Research Articles

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

A Double-Layer Coupled Network Model of Network Density Effects on Multi-Stage Innovation Efficiency Dynamics: Agent-Based Modeling Methods

This paper proposes a double-layer coupled network model to analyze the multi-stage innovation activities of online, and the model consists of two layers: the online layer, which represents the virtual interactions among innovators, and the offline layer, which represents the physical interactions among innovators. The model assumes that the innovation activities are influenced by both the online and offline network structures, as well as the coupling effect between them. And it simulates the entire innovation process including knowledge diffusion and knowledge recombination. The model also incorporates the concept of network density, which measures the degree of network connectivity and cohesion (network structure). Observing the network density influence on innovation efficiency during the innovation process is realized through setting the selection mechanism and the knowledge recombination mechanism. The coupling relationship between the two layers of network density on the three stages of innovation is further discussed under the theoretical framework of the innovation value chain. Simulation and experimental results suggest that when the offline network density is constant, a higher online network density is not always better. When the online network density is low, the sparse structure of the online network reduces innovation efficiency. When the online network density is high, the structural redundancy caused by the tight network structure prevents innovation efficiency from improving. The results of the study help enterprises to adjust and optimize the internal cooperation network structure at different stages of innovation in order to maximize its effectiveness and improve the innovation efficiency of enterprises.

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.

Coupled Airflow-Thermal Network Model for Large-Scale Offshore Wind Generators With Modular Fractional-Slot Concentrated Winding

Coupled Airflow-Thermal Network Model for Large-Scale Offshore Wind Generators With Modular Fractional-Slot Concentrated Winding

Layout-Dominated Dynamic Current Balancing Analysis of Multichip SiC Power Modules Based on Coupled Parasitic Network Model

Multichip silicon carbide (SiC) power modules with Kelvin-source connections are commonly used in applications requiring large capacity. As a result of the parasitic effect induced by the interconnections in module packaging, the dynamic current mismatch among paralleled dies limits the available capacity of power modules. This article presents a general analysis on the mechanism of layout-dominated dynamic current balancing in multichip SiC power modules, utilizing a coupled parasitic network model. Focusing on the interrelation of parasitic parameters in the power module, a coupled parasitic network model is developed specially for switching transients, and the dynamic current balancing equations are derived. For the multichip power modules with two different layouts, the parasitic parameters pertaining to the proposed model are extracted by the finite-element analysis (FEA). The acquired parasitic parameters considering magnetic coupling are utilized to calculate and verify the dynamic current balancing equations. Moreover, based on these parasitic parameters, the electromagnetic coupling simulation is performed to evaluate the dynamic current sharing. Furthermore, for the validation of the proposed model and equations, experiments are conducted with the fabricated power module prototypes.

Suppression of absence seizures by using different stimulations in a reduced corticothalamic-basal ganglion-pedunculopontine nucleus model.

Coupled neural network models are playing an increasingly important part in the modulation of absence seizures today. However, it is currently unclear how basal ganglia, corticothalamic network and pedunculopontine nucleus can coordinate with each other to develop a whole coupling circuit, theoretically. In addition, it is still difficult to select effective parameters of electrical stimulation on the regulation of absence seizures in clinical trials. Therefore, to develop a coupled model and reduce computation cost, a new model constructed by a simplified basal ganglion, two corticothalamic circuits and a pedunculopontine nucleus was proposed. Further, to seek better inhibition therapy, three electrical stimulations, high frequency stimulation (HFS), 1:0 coordinate reset stimulation (CRS) and 3:2 CRS, were applied to the thalamic reticular nucleus (RE) in the first corticothalamic circuit in the coupled model. The simulation results revealed that increasing the frequency and pulse width of an electrical stimulation within a certain range can also suppress seizures. Under the same parameters of electrical stimulation, the inhibitory effect of HFS on seizures was better than that of 1:0 CRS and 3:2 CRS. The research established a reduced corticothalamic-basal ganglion-pedunculopontine nucleus model, which lays a theoretical foundation for future optimal parameters selection of electrical stimulation. We hope that the findings will provide new insights into the role of theoretical models in absence seizures.

Coupled continuum and network model framework to study catalyst layers of polymer electrolyte fuel cells

The nanostructured thin film (NSTF) catalyst layers which have demonstrated high power densities, mass activities, and exceptional metal and support stability can have limited operational robustness due to their thin thickness and the hydrophilicity of the metal-coated nano whiskers. The dispersed nanostructured thin film (dNSTF) catalyst layers have been developed by dispersing the NSTF Pt whiskers with ionomer and carbon support to increase the thickness and hydrophobicity. Continuum and network models (NM) are coupled through boundary conditions to study the polymer electrolyte fuel cell with a dNSTF cathode catalyst layer. The coupled model combines the computational efficiency of the continuum model with the pore-scale information in the dNSTF cathode catalyst layer of the NM. It captures the special morphology of the partially ionomer/water covered cylindrical whiskers, as well as water percolation through the pore structures and their impact on the cell performance. We observe optimal ionomer coverage on whiskers to be 0.5, ionomer to carbon ratio to be 0.9 and higher whisker to carbon ratios to be desired.

Impact of co-evolution of negative vaccine-related information, vaccination behavior and epidemic spreading in multilayer networks

In this paper, we establish a three-layer coupled network model to analyze the co-evolution of negative vaccine-related information, vaccination behavior, and epidemic spread. The three layers are used to represent negative vaccine-related information dissemination, vaccination behavior diffusion and epidemic propagation, respectively. The comprehensive impact of vaccination costs and herd mentality are firstly considered in the diffusion of vaccination behavior. Then, we use the micro-Markov chain (MMC) method to derive the system dynamics equations and the epidemic threshold. The analytical results show that the proportion of efficaciously vaccinated individuals and the topology of the epidemic spread layer both determine the epidemic threshold. Finally, a large number of simulation experiments are conducted to investigate the impact of various factors on the epidemic spread, which include negative vaccine-related information diffusion, rational judgment and herd mentality during the process of vaccination decision, and cost of vaccine. The results demonstrate the role of negative vaccine-related information diffusion and vaccination behavior spread in epidemic control, and may provide some valuable clues for policymakers to formulate appropriate measures to prevent and control epidemics.

A Briefing Survey on Advances of Coupled Networks With Various Patterns

In real-world scenarios, networks do not exist in isolation but coupled together in different ways, including dependent, multi-support, and inter-connected patterns. And, when a coupled network suffers from structural instability or dynamic perturbations, the system with different coupling patterns shows rich phase transition behaviors. In this review, we present coupled network models with different coupling patterns developed from real scenarios in recent years for studying the system robustness. For the coupled networks with different coupling patterns, based on the network percolation theory, this paper mainly describes the influence of coupling patterns on network robustness. Moreover, for different coupling patterns, we here show readers the research background, research context, and the latest research results and applications. Furthermore, different approaches to improve system robustness with various coupling patterns and future possible research directions for coupled networks are explained and considered.

Cascading failures of overload behaviors using a new coupled network model between edges

With the development of network science, the coupling between networks has become the focus of complex network research. However, previous studies mainly focused on the coupling between nodes, while ignored the coupling between edges. We propose a novel cascading failure model of two-layer networks. The model considers the different loads and capacities of edges, as well as the elastic and coupling relationship between edges. In addition, a more flexible load-capacity strategy is adopted to verify the model. The simulation results show that the model is feasible. Different networks have different behaviors for the same parameters. By changing the load parameters, capacity parameters, overload parameters, and distribution parameters reasonably, the robustness of the model can be significantly improved.

Coupled pore network model for the cathode gas diffusion layer in PEM fuel cells

A pore network model (PNM) is developed for gas diffusion layer (GDL) in the cathode side of polymer electrolyte membrane fuel cells (PEMFCs). The model is coupled to network models of reactant oxygen and electron transport inside GDL and also to simple models of catalyst layer and membrane. The coupled model captures the simultaneous effect of reactant and charge access to reaction sites and the resulting water generation, allowing it a transient nature up to reaching the steady state, which is a notable modification to the available PNMs which assume uniform invasion of liquid water from catalyst layer. The results show strongly non-uniform water saturation distributions inside GDL with maxima under the current collector ribs. As an extra feature, the model can predict time evolution of oxygen concentration and water generation rate at catalyst layer as a result of liquid water build-up in GDL. Also included is a dry case coupled model in order to be compared with the main model. The local water blockages in GDL inflict an average of 38.8% loss on the produced limiting current of the fuel cell. Finally, the coupling allows prediction of concentration overvoltages which emerges to be most pronounced in the under-rib region.

English

English

Two-layer coupled network model for topic derivation in public opinion propagation

In view of the fact that news can generate derivative topics when it spreads through micro-blogs, a two-layer coupled SEIR public opinion propagation model is proposed in this paper. The model divides the process of public opinion propagation into two layers: the original topic layer and the derived topic layer. Messages are transmitted separately by the SEIR model in the two topic layers, which are independent and interactive. The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold. Further, we establish the relationship between the original topic and the derived topic by simulation. This paper uses the Baidu index to demonstrate the correctness of the model. The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method. The results show that the proposed model is consistent with the propagation of actual public opinion.

Optimal Coupling Patterns in Interconnected Communication Networks

Traffic dynamics on single or isolated complex networks has been extensively studied in the past decade. Recently, several coupled network models have been developed to describe the interactions between real-world networked systems. In a system of interconnected networks, the coupling links refer to the physical links between networks and provide paths for traffic transmission. Thus, the coupling pattern, i.e., the way coupling links are added, has a profound influence on the overall traffic performance. In this paper, we employ a simulated annealing (SA) algorithm to find a near-optimal configuration of the coupling links, which effectively improves the overall traffic capacity compared with random, assortative and disassortative couplings. Furthermore, we investigate the optimal configuration of coupling links given by the SA algorithm and develop a faster method to select the coupling links.

The pacemaker role of thalamic reticular nucleus in controlling spike-wave discharges and spindles.

Absence epilepsy, characterized by 2-4 Hz spike-wave discharges (SWDs), can be caused by pathological interactions within the thalamocortical system. Cortical spindling oscillations are also demonstrated to involve the oscillatory thalamocortical rhythms generated by the synaptic circuitry of the thalamus and cortex. This implies that SWDs and spindling oscillations can share the common thalamocortical mechanism. Additionally, the thalamic reticular nucleus (RE) is hypothesized to regulate the onsets and propagations of both the epileptic SWDs and sleep spindles. Based on the proposed single-compartment thalamocortical neural field model, we firstly investigate the stimulation effect of RE on the initiations, terminations, and transitions of SWDs. It is shown that the activations and deactivations of RE triggered by single-pulse stimuli can drive the cortical subsystem to behave as the experimentally observed onsets and self-abatements of SWDs, as well as the transitions from 2-spike and wave discharges (2-SWDs) to SWDs. In particular, with increasing inhibition from RE to the specific relay nucleus (TC), rich transition behaviors in cortex can be obtained through the upstream projection path, RE→TC→Cortex. Although some of the complex dynamical patterns can be expected from the earlier single compartment thalamocortical model, the effect of brain network topology on the emergence of SWDs and spindles, as well as the transitions between them, has not been fully investigated. We thereby develop a spatially extended 3-compartment coupled network model with open-/closed-end connective configurations, to investigate the spatiotemporal effect of RE on the SWDs and spindles. Results show that the degrees of activations of RE1 can induce the rich spatiotemporal evolution properties including the propagations from SWDs to spindles within different compartments and the transitions between them, through the RE1→TC1→Cortex1 and Cortex1→Cortex2→Cortex3 projecting paths, respectively. Overall, those results imply that RE possesses the pacemaker function in controlling SWDs and spindling oscillations, which computationally provide causal support for the involvement of RE in absence seizures and sleep spindles.

Coupled flow network model and CFD analysis for a combined impingement and film cooled gas turbine nozzle guide vane

Coupled flow network model and CFD analysis for a combined impingement and film cooled gas turbine nozzle guide vane

Uncertainty Quantification of Oscillation Suppression During DBS in a Coupled Finite Element and Network Model.

Models of the cortico-basal ganglia network and volume conductor models of the brain can provide insight into the mechanisms of action of deep brain stimulation (DBS). In this study, the coupling of a network model, under parkinsonian conditions, to the extracellular field distribution obtained from a three dimensional finite element model of a rodent's brain during DBS is presented. This coupled model is used to investigate the influence of uncertainty in the electrical properties of brain tissue and encapsulation tissue, formed around the electrode after implantation, on the suppression of oscillatory neural activity during DBS. The resulting uncertainty in this effect of DBS on the network activity is quantified using a computationally efficient and non-intrusive stochastic approach based on the generalized Polynomial Chaos. The results suggest that variations in the electrical properties of brain tissue may have a substantial influence on the level of suppression of oscillatory activity during DBS. Applying a global sensitivity analysis on the suppression of the simulated oscillatory activity showed that the influence of uncertainty in the electrical properties of the encapsulation tissue had only a minor influence, in agreement with previous experimental and computational studies investigating the mechanisms of current-controlled DBS in the literature.

An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images

Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods.

Pinning outer synchronization between two delayed complex networks with nonlinear coupling via adaptive periodically intermittent control

This paper is concerned with the problem of pinning outer synchronization between two delayed complex networks with nonlinear coupling. Both the internal delay and coupling delay are included in coupled network model. By designing appropriate adaptive intermittent controllers and using the Lyapunov stability theory, some pinning outer synchronization criteria are derived, which can guarantee the response network asymptotically synchronizes to drive network. Furthermore, the restrictions about delay and control width are removed. Simultaneously, two simple adaptive intermittent pinning outer synchronization conditions are obtained based on the proposed criteria. A numerical example is provided to demonstrate the effectiveness of the theoretical results.

A Fully Coupled Network Model: Comparison With Other Integrated Models on Field Cases

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 173251, “A Fully Coupled Network Model—Practical Issues and Comprehensive Comparison With Other Integrated Models on Field Cases,” by H. Cao, SPE, P. Samier, SPE, H.M. Kalunga, E. Detige, and E. Obi, SPE, Total, prepared for the 2015 SPE Reservoir Simulation Symposium, Houston, 23–25 February. The paper has not been peer reviewed. There has been increasing interest in integrated simulation of reservoirs, wells, and surface facilities, which is particularly important for companies with major assets in deep offshore fields. The basic approach for integration can be split into decoupled or separated (between a reservoir simulator and a facility simulator), iteratively coupled, and fully coupled networks, with increasing stability and efforts in implementation. This paper covers the development of a state-of-the- art fully coupled network inside a next-generation commercial reservoir simulator and compares different coupling approaches on real field cases. Introduction Two basic and diverse strategies exist for integrated simulation. The separated strategy focuses on loosely coupling separate software together, typically between an existing reservoir simulator and an existing facility simulator. The combined strategy focuses on tight integration within a single piece of software, typically a reservoir simulator that also models surface facilities. Both of these strategies have their advantages and disadvantages, and both are actively used in the industry. In the separated strategy, a reservoir simulator is used for reservoir and wells modeling and a facility simulator is used for surface-facility modeling. The information exchange [well inflow performance relationship (IPR) from reservoir to facility and well pressure/rate constraint] happens per timestep or periodically between the two simulators through a custom-made controller. The main advantages of this strategy are its low cost in development (mostly associated with the controller) and its flexibility in allowing users to use the reservoir and surface simulators that fit their needs best. On the other hand, as a loosely coupled (here referred to as decoupled) method, it often suffers from numerical instability because the IPR calculated at the beginning of the coupling timestep is not representative of the IPR at the end of the coupling timestep, which is particularly true for the kind of fine and full-featured reservoir models used in reservoir-simulation studies, where well-perforation cells are much smaller than the well-drainage radius. There are also other issues for the strategy. For instance, the information exchanged is based on IPR of individual (and independent) wells without proper accounting of well interference anywhere in the coupling scheme and in the IPR generation and format. However, well interference can become important when opening and shutting nearby wells and when modeling smart wells with well laterals and segments controlled individually. The separated strategy typically requires a combination of software across multiple platforms. In the combined strategy, a reservoir simulator typically is extended to model the facility, thus everything is contained within a single simulator. Contrary to the facility model in a standalone advanced facility simulator, the facility model in a reservoir simulator is a more simplified one in both topology and modeling. The iteratively coupled methods can differ further according to the frequency of coupling and the definition of timestep convergence. Recently, recognizing the fact that both wells and surface facilities model flow in pipes, researchers started to examine the idea of using a general pipe-network module to model both. This network module can replace the traditional well module inside a reservoir simulator in all aspects and, hence, achieve fully implicit coupling among reservoir, wells, and surface facilities. The combined strategy takes considerably more time and effort to develop, but, in turn, it ensures much-improved stability and speed, yielding generous payouts well worth the initial investment. Because of its ease of use (using only a single piece of software) and much-improved stability and speed, especially for fine and detailed reservoir models, this combined strategy has been recommended to and is often preferred by reservoir engineers for reservoir studies of deep offshore fields. The fully implicitly coupled network is the most advanced next-generation approach available. It is unconditionally stable and offers the best speed and performance possible. This approach has been featured in most next-generation industrial reservoir simulators.