Dynamic Behavior Of Network Research Articles

Dynamic Equivalents of Nonlinear Active Distribution Networks Based on Hammerstein-Wiener Models: An Application for Long-Term Power System Phenomena

In traditional power systems, the load consumption inside the distribution networks has a relatively passive behavior. Consequently, transmission system operators have commonly aggregated the load centers using static loads such as exponential load models. This is a practical approach for bulk power system stability studies. In modern systems, the impact of distributed generation units on the system dynamics can no longer be neglected. Static load models fall short to represent the highly dynamic behavior of active distribution networks. This paper proposes a methodology for equivalencing such networks while conserving the dynamics of interest for long-term stability studies. The methodology is based on Hammerstein-Wiener models that use only boundary variables measured at the distribution network point of common coupling. Therefore, unlike gray-box models, no previous knowledge about the system is required. Furthermore, the proposed methodology has a transparent relationship to linear systems and a convenient block representation that can be implemented in commercial power system analysis software using only standard elements. This allows for easy implementation, overcoming a barrier for a rather conservative sector such as power systems planning and operation. The conclusions are derived from time-domain simulations on the IEEE test system for voltage stability assessment.

Dynamic urban evaluation routing protocol for enhanced vehicle ad hoc networks

Recently, a crucial need has grown for improving data communication for the application of roads and ad hoc networks. That is, to provide reliable and operational efficiency in data delivery and throughput. Due to the fast fragmentation and dynamic network behavior, there is an increasing demand to reach reliability in data transmission. Furthermore, the various features and the manifold of dynamic topologies in the vehicular ad hoc network raise the need to redesign the routing strategy. Thus, ensuring efficient and reliable data delivery. This paper aims to introduce a Software architecture for Road Network. The architecture is based on fog computing and aims to improve the overall performance in vehicular networks. The proposed architecture is a new routing design for the urban system to accomplish low energy consumption and operational efficiency in data delivery. The integration between the software-defined networks and fog computing platform in the proposed architecture aids to address the high rate of data transmission. Historically, this high rate negatively affected network capacity and power consumption. To prove the effectiveness of the proposed architecture, it is compared with five state-of-art algorithms published in high impact journals. The proposed architecture performance is tested based on four metrics namely packet delivery ratio, network throughput, power consumption, and routing overhead. The experimental results indicate that a 50–60% improvement in both power consumption and packet delivery ratio, while a 60–65% enhancement in network throughput and routing overhead, respectively.

Localizing targets for neuromodulation in drug-resistant epilepsy using intracranial EEG and computational model.

Neuromodulation has emerged as a promising technique for the treatment of epilepsy. The target for neuromodulation is critical for the effectiveness of seizure control. About 30% of patients with drug-resistant epilepsy (DRE) fail to achieve seizure freedom after surgical intervention. It is difficult to find effective brain targets for neuromodulation in these patients because brain regions are damaged during surgery. In this study, we propose a novel approach for localizing neuromodulatory targets, which uses intracranial EEG and multi-unit computational models to simulate the dynamic behavior of epileptic networks through external stimulation. First, we validate our method on a multivariate autoregressive model and compare nine different methods of constructing brain networks. Our results show that the directed transfer function with surrogate analysis achieves the best performance. Intracranial EEGs of 11 DRE patients are further analyzed. These patients all underwent surgery. In three seizure-free patients, the localized targets are concordant with the resected regions. For the eight patients without seizure-free outcome, the localized targets in three of them are outside the resected regions. Finally, we provide candidate targets for neuromodulation in these patients without seizure-free outcome based on virtual resected epileptic network. We demonstrate the ability of our approach to locate optimal targets for neuromodulation. We hope that our approach can provide a new tool for localizing patient-specific targets for neuromodulation therapy in DRE.

Route to chaos in a branching model of neural network dynamics

Simplified models are a necessary steppingstone for understanding collective neural network dynamics, in particular the transitions between different kinds of behavior, whose universality can be captured by such models, without prejudice. One such model, the cortical branching model (CBM), has previously been used to characterize part of the universal behavior of neural network dynamics and also led to the discovery of a second, chaotic transition which has not yet been fully characterized. Here, we study the properties of this chaotic transition, that occurs in the mean-field approximation to the kin=1 CBM by focusing on the constraints the model imposes on initial conditions, parameters, and the imprint thereof on the Lyapunov spectrum. Although the model seems similar to the Hénon map, we find that the Hénon map cannot be recovered using orthogonal transformations to decouple the dynamics. Fundamental differences between the two, namely that the CBM is defined on a compact space and features a non-constant Jacobian, indicate that the CBM maps, more generally, represent a class of generalized Hénon maps which has yet to be fully understood.

Development of a probabilistic short-term voltage quality assessment method with the weak point detection capability through the dynamic analyses

The proliferation of power electronics-based sources and equipment in low voltage to high voltage applications reshapes the dynamic behavior of power system networks, leading to certain voltage quality vulnerabilities for industrial customers and independent power producers. The root causes of most voltage quality problems are related to power system faults which may trigger voltage dips, voltage swells, and interruptions. In-depth stochastic modeling combined with advanced power system simulations and statistical validation is required to foresee the effects of these events on the network users. Therefore, a holistic method was proposed in this paper that utilizes reliability parameters in the context of the novel Monte Carlo-based fault creation approach. This method incorporates the input data from an extensive literature survey and real-life measurements. The probabilistic fault events are simulated in an environment where the protection system – dynamics interactions are considered. A modified IEEE test system was developed to introduce unconventional sources into the conventional test grid. A python script was developed to implement the algorithms into the power system analysis tool. Also, several voltage quality indices were introduced which are based on international standards. 30 Monte Carlo repetitions of a 5-year simulation period were performed (150 years Monte Carlo simulations), leading to a total number of 4410 fault cases for each network scenario. The results of the Monte-Carlo simulations were used to find the weak points of the network under five different operation and design scenarios with several statistical significance tests.

Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect.

The dynamic behavior of memristive neural networks (MNNs), including synchronization, effectively keeps the robotic stability against numerous uncertainties from the mimic of the human brain. However, it is challenging to perform projective quasi-synchronization of coupled MNNs with low-consumer control devices. This is partly because complete synchronization is difficult to realize under various projective factors and parameter mismatch. This article aims to investigate projective quasi-synchronization from the perspective of the controller. Here, two approaches are considered to find the event-triggered scheme for lag synchronization of coupled MNNs. In the first approach, the projective quasi-synchronization issue is formulated for coupled MNNs for the first time, where the networks are combined with time-varying delays and uncertainties under the constraints imposed by the frequency of controller updates within limited system communication resources. It is shown that our methods can avoid the Zeno-behavior under the newly determined triggered functions. In the second approach, following classical methods, a novel projective quasi-synchronization criterion that combines the nonlinear property of the memristor and the framework of Lyapunov-Krasovskii functional (LKF) is proposed. Simulation results indicate that the proposed two approaches are useful for coupled MNNs, and they have less control cost for different types of quasi-synchronization.

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

Combined effect of cable sag and bending stiffness on in-plane modal behavior of two horizontal shallow cables connected by an elastic cross-tie

Cross-tie solution is commonly employed to mitigate stay cable vibrations, especially the long ones. Therefore, cable sag could have a sizable impact on the dynamic response of the formed cable network and should not be neglected. Besides, the reduced cable effective length due to the installation of cross-ties and the larger diameter of longer cables would render the cable bending stiffness to have a more pronounced effect on the dynamic behavior of cable networks consisting long cables. Though the mechanism and effectiveness of cable networks have been studied by numerous researchers, majority of these work employed the taut cable assumption in modeling cable behavior. Only a few recent studies investigated the influence of either cable bending stiffness or cable sag separately on the modal response of cable networks, whereas the evaluation on their combined effect has yet to be fulfilled. In the current study, an analytical model of a flexible two-cable network proposed earlier by the authors is refined by including the sag effect. The combined effect of cable sag and bending stiffness on the dynamic behavior of cable networks with different configurations are examined, using the linear theory of shallow cables. Besides, the effect of cross-tie axial stiffness and installation location, as well as the length ratio and the bending stiffness ratio between the consisting cables, on the occurrence and characteristics of modal cross-over are discussed. Results show that while cable bending stiffness would influence all network modes, sag would only influence the symmetric modes. Both of them have a stiffening effect on the affected modes with that of the latter being more sizable, in particular, on the high order modes. Besides, modal cross-over in cable networks is observed to occur not only between two global modes, but also a global mode and a local mode. Enhance the stiffness of a cable network is found to advance the occurrence of modal cross-over at a smaller sag.

In-plane modal behavior of a single shallow flexible cable with an intermediate transverse elastic support

Cross-tie solution has been successfully applied in field to mitigate undesirable bridge stay cable vibrations, especially for cables on modern long-span cable-stayed bridges which typically have long length and relative thick cross section. In evaluating the effectiveness of the cross-tie solution and analyzing dynamic response of the formed cable network, the taut cable assumption has been used in most existing studies. Nevertheless, cable sag and bending stiffness could have non-negligible influence in networks consisting of long cables with relatively large diameter. Since a transverse cross-tie provides an elastic constraint to a vulnerable cable by connecting it with its neighbors, to fully comprehend the impact of cable sag and bending stiffness on the dynamic behavior of shallow-cable networks, it is imperative to scrutinize their effect on the behavior of a shallow cable with a transverse elastic constraint within the span. In the current study, by using the linear theory of shallow cables, an analytical model for the in-plane dynamics of a single flexible low-sag cable with an intermediate transverse elastic support is proposed. The impact of the intermediate support stiffness, location, cable bending stiffness, and sag on the in-plane modal response of the studied cable system are examined. Results show that the presence of an intermediate transverse elastic support breaks the symmetry and anti-symmetry of the original cable mode shapes, which renders the influence of sag extends to all cable modes. Further, the level of such a mode shape distortion would determine whether or not modal cross-over would occur. For any given intermediate support location, it is found that there always exists a specific level of intermediate support stiffness of which the cable sag corresponding to the occurrence of modal cross-over would be independent of cable bending stiffness. By reducing or increasing the intermediate support stiffness to be lower or higher than this specific level, the increase of cable bending stiffness would either delay modal cross-over to occur at a larger cable sag or advance its occurrence at a smaller cable sag. Reducing the intermediate support stiffness would render a local mode to evolve into a global mode, and the presence of cable bending stiffness and sag is found to allow this mode evolution to occur when a more flexible intermediate support is used.

A generalized Flory-Stockmayer kinetic theory of connectivity percolation and rigidity percolation of cytoskeletal networks.

Actin networks are essential for living cells to move, reproduce, and sense their environments. The dynamic and rheological behavior of actin networks is modulated by actin-binding proteins such as α-actinin, Arp2/3, and myosin. There is experimental evidence that actin-binding proteins modulate the cooperation of myosin motors by connecting the actin network. In this work, we present an analytical mean field model, using the Flory-Stockmayer theory of gelation, to understand how different actin-binding proteins change the connectivity of the actin filaments as the networks are formed. We follow the kinetics of the networks and estimate the concentrations of actin-binding proteins that are needed to reach connectivity percolation as well as to reach rigidity percolation. We find that Arp2/3 increases the actomyosin connectivity in the network in a non-monotonic way. We also describe how changing the connectivity of actomyosin networks modulates the ability of motors to exert forces, leading to three possible phases of the networks with distinctive dynamical characteristics: a sol phase, a gel phase, and an active phase. Thus, changes in the concentration and activity of actin-binding proteins in cells lead to a phase transition of the actin network, allowing the cells to perform active contraction and change their rheological properties.

A Dynamic System Approach to Spiking Second Order Memristor Networks

Second order memristors are two terminal devices that present a conductance depending on two orders of variables, namely the geometric parameters and the internal temperature. They have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromorphic computing. In particular, memristor crossbar structures appear to be suitable for implementing locally competitive algorithms and for tackling classification problems by exploiting temporal learning techniques. On the other hand, neuromorphic studies and experiments have revealed the existence of different kinds of plasticity and have shown the effect of calcium concentration on synaptic changes. Computational studies have investigated the behavior of spiking networks in the context of supervised, unsupervised, and reinforcement learning. In this paper, we first derive a simplified, almost analytical, model of a second-order memristor, only involving two variables, the mem-conductance, and the temperature, directly attributable to the synaptic efficacy and to the calcium concentration. Then we study in detail the response of a single memristive synapse to the most relevant plasticity models, including cycles of spike pairs, triplets, and quadruplets at different frequencies. Finally, we accurately characterize memristor spiking networks as discrete nonlinear dynamic systems, with mem-conductances as state variables and pre and postsynaptic spikes as inputs and outputs, respectively. The result shows that the model developed in this manuscript can explain and accurately reproduce a significant portion of observed synaptic behaviors, including those not captured by classical spike pair-based STDP models. Furthermore, under such an approach, the global dynamic behavior of memristor networks and the related learning mechanisms can be deeply analyzed by employing advanced nonlinear dynamic techniques.

Robustness analysis of cyber-coupled power systems with considerations of interdependence of structures, operations and dynamic behaviors

The failure propagation of a networked system is profoundly affected by the operation and dynamic behavior of its constituent systems. In this paper, we propose a novel network-based model to investigate the cascading failure of cyber-coupled power systems. Unlike existing models that assume oversimplified network functions, operational characteristics and failure evolution, our proposed model considers the practical differences between a communication network and a power network in terms of network structure, physical operation and dynamic behavior. A practical trip mechanism is derived according to the instantaneous data transmission overload and the stochastic power flow overload. The effects of communication network connection strength and coupling patterns on the robustness of interdependent systems are also studied. Our study shows that the thermal memory effect of power lines aggravates the failure propagation, and that the robustness of the interdependent system is strongly related to the coupled communication network’s tolerance. Furthermore, coupling patterns based on network similarity have better inhibition effects on cascading failure under random attacks, and node destructiveness is a suitable coupling similarity index that can be used to improve the robustness of the system. However, the strategy is inapplicable when the system is under intentional attacks.

Handling dynamic network behavior and unbalanced datasets for WSN anomaly detection

Handling dynamic network behavior and unbalanced datasets for WSN anomaly detection

Effects of double delays on bifurcation for a fractional-order neural network.

Neural network bifurcation is an important nonlinear dynamic behavior of neural network, which plays an important role in cognitive calculation. The effects of leakage delay or communication delay on the stability and bifurcation of a fractional-order neural network (FONN) are researched. By viewing leakage delay or communication delay as the bifurcation parameters to detect the bifurcations conditions of the developed FONN, respectively, we capture the bifurcation points with regard to leakage delay or communication delay. It alleges that FONN exhibits excellent stability performance with choosing smaller values of them, and Hopf bifurcations emerge of FONN and induce poor performance if selecting a larger ones. In the end, numerical examples are employed to evaluate the feasibleness of the analytical discoveries.

Synchronous Behavior of Coupled Oscillatory Network with Different Interconnection Patterns

This paper concerns with the synchronous behavior of coupled oscillatory networks with positive and negative interconnections. Furthermore, the coupling strength is related to the frequencies of oscillators, and we study the dynamical behavior of power networks with weight coefficient between oscillators. Most importantly, time delay is inevitable in the propagation power through the transmission line. Thus, the impact of time delay on synchronous state of oscillatory network is discussed. Simulation results show that the impact of positive interconnection is not obvious on the synchronous state of power network. Nevertheless, for negative coupling, large coupling strength between oscillators decreases the synchronous performance. In addition, we find that when the weight coefficient is even, the synchronous ability of network increases with the increase in weight coefficient whatever the coupling strategy and topological structure be. However, when weight parameter is odd, the entire network with negative coupling cannot achieve synchronization. Finally, our results show that time delay has a critical value τ = 1, and the evolutions of order parameters are different under different values of time delay.

Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction

Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure networks, such as the power systems, is a challenging but important task. In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures. Existing approaches for prediction of dynamic trajectories either rely on the availability of accurate physical models of the system, use computationally expensive time-domain simulations, or are applicable only at local prediction problems (e.g., a single generator). In this paper, we report the application of two broad classes of data-driven learning models &#x2013; along with their algorithmic implementation and performance evaluation &#x2013; in predicting transient trajectories in power networks using only streaming measurements and the network topology as input. One class of models is based on the <i>Koopman operator theory</i> which allows for capturing the nonlinear dynamic behavior via an infinite-dimensional linear operator. The other class of models is based on the <i>graph convolutional neural networks</i> which are adept at capturing the inherent spatio-temporal correlations within the power network. Transient dynamic datasets for training and testing the models are synthesized by simulating a wide variety of load change events in the IEEE 68-bus system, categorized by the load change magnitudes, as well as by the degree of connectivity and the distance to nearest generator nodes. The results confirm that the proposed predictive models can successfully predict the post-disturbance transient evolution of the system with a high level of accuracy.

Mitochondrial Adaptive Responses to Hypertriglyceridemia and Bioactive Lipids.

Significance: Altered plasma triglyceride metabolism and changes in dietary fatty acid types and levels are major contributors to the development of metabolic and cardiovascular diseases such as fatty liver disease, obesity, diabetes, and atherosclerosis. Lipid accumulation in visceral adipose tissue and ectopically in other organs, as well as lipid-induced redox imbalance, is connected to mitochondrial dysfunction in a range of oxidative stress-associated metabolic and degenerative disorders. Recent Advances: Successful mitochondrial adaptive responses in the context of hypertriglyceridemia and dietary bioactive polyunsaturated fatty acids contribute to increase body energy expenditure and reduce oxidative stress, thus allowing several cell types to cope with metabolic challenges and stresses. These responses include mitochondrial redox signaling, mild uncoupling, and changes in network dynamic behavior. Critical Issues: Mitochondrial bioenergetics and redox changes in a lipid overload context are relatively well characterized. However, the turning point between adaptive and maladaptive mitochondrial responses remains a critical issue to be elucidated. In addition, the relationship between changes in fusion/fission machinery and mitochondrial function is less well understood. Future Directions: The effective mitochondrial responses described here support the research for new drug design and diet or nutraceutical formulations targeting mitochondrial mild uncoupling and effective quality control as putative strategies for cardiometabolic diseases. Antioxid. Redox Signal. 36, 953-968.

Dynamical behavior of recurrent neural networks with different external inputs

The main aim of this paper is to study the dynamics of a recurrent neural networks with different input currents in terms of asymptotic point. Under certain circumstances, we studied the existence, the uniqueness of bounded solutions and their homoclinic and heteroclinic motions of the considered system with rectangular currents input. Moreover, we studied the unpredictable behavior of the continuous high-order recurrent neural networks and the discrete high-order recurrent neural networks. Our method was primarily based on Banach’s fixed-point theorem, topology of uniform convergence on compact sets and Gronwall inequality. For the demonstration of theoretical results, we give examples and their numerical simulations.

In-Network Neural Networks: Challenges and Opportunities for Innovation

The quest for self-driving networks poses growing pressure to manage network events at a nano-second scale. In this article, we make a case for leveraging programmable forwarding planes to achieve self-driving networks and respond to their dynamism in real time by in-network intelligence and without performing traffic steering/mirroring to centralized management solutions (intelligent or not). We briefly cover throughout the article preliminary ideas in the in-network neural networks field and discuss the technical challenges of running machine learning techniques entirely in the forwarding plane. We also highlight potential use cases of having an autonomous intelligent network capable of self-adapting to dynamic network behavior changes with minimal to no human intervention, including smart network telemetry, smart traffic engineering, real-time flow classification, and network tomography. We close with a roadmap of research opportunities enabled by distributed in-network intelligence in programma-ble forwarding planes.

Securing BGP by Handling Dynamic Network Behavior and Unbalanced Datasets

The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.