1,721,005 research outputs found
One-way dependent clusters and stability of cluster synchronization in directed networks
No full textCluster synchronization in networks of coupled oscillators is the subject of broad interest from the scientific community, with applications ranging from neural to social and animal networks and technological systems. Most of these networks are directed, with flows of information or energy that propagate unidirectionally from given nodes to other nodes. Nevertheless, most of the work on cluster synchronization has focused on undirected networks. Here we characterize cluster synchronization in general directed networks. Our first observation is that, in directed networks, a cluster A of nodes might be one-way dependent on another cluster B: in this case, A may remain synchronized provided that B is stable, but the opposite does not hold. The main contribution of this paper is a method to transform the cluster stability problem in an irreducible form. In this way, we decompose the original problem into subproblems of the lowest dimension, which allows us to immediately detect inter-dependencies among clusters. We apply our analysis to two examples of interest, a human network of violin players executing a musical piece for which directed interactions may be either activated or deactivated by the musicians, and a multilayer neural network with directed layer-to-layer connections
Nonlinear models of power inductors: A survey
No full textSwitch-mode power supplies (SMPSs) are widely exploited to interface electrical energy sources to motors and other electrical loads. Inductors are usually the biggest and heaviest components in SMPSs, limiting their overall power density. Therefore, there is an increasing interest in designing SMPSs with partially saturating inductors, because this significantly reduces their weight and size, thus increasing power density. This paper provides a review of nonlinear behavioral models (based on easy-to-measure quantities) of the inductance, power loss, and temperature rise of inductors working, at least partially, in magnetic saturation. This survey discusses the pros, cons, and ranges of validity of these models, with a glimpse at their application to SMPS simulation, design, monitoring, and control
A parameter-dependent approximation of the Hindmarsh-Rose neuron model suitable for analog circuit implementation
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Effects of parameter variation on the accuracy of a nonlinear inductor model for switch-mode power supplies applications
No full textA nonlinear inductor model has been recently proposed, which takes into account the magnetic saturation and the dependence of the inductance on the temperature. The model depends on seven parameters, which are identified based on experimental measurements of the inductor current in a switch-mode power supply. In this paper we show how the accuracy of each parameter affects the overall modeling accuracy in reproducing the inductor current
Analysis and improvement of an algorithm for the online inertia estimation in power grids with RES
No full textThe increasing presence of renewable energy sources (RES) in a power grid tends to reduce its inertia constant, which quantifies the grid's ability to contrast the frequency changes due to external disturbances. This led to the development of control strategies that interface the RES to the grid providing synthetic inertia, but these strategies cannot avoid oscillations of the overall system inertia, thus requiring algorithms for the online inertia constant estimation under normal operating conditions of the power grid. In this paper, we consider one of these algorithms, which exploits the data measured online through phasor measurement units, and critically analyze it, in order to efficiently apply it to the estimation of the inertia constant in the IEEE-14-bus power system, also with the addition of a PV power plant. The obtained results point out an increased efficiency of the online estimation of the network inertia
On the problem of estimating connectivity from spike recordings in large neuron networks
No full textBAL: A library for the brute-force analysis of dynamical systems
No full textThis paper describes the functionality and usage of bal, a C/C++ library with a Python front-end for the brute-force analysis of continuous-time dynamical systems described by ordinary differential equations (ODEs). bal provides an easy-to-use wrapper for the efficient numerical integration of ODEs and, by detecting intersections of the trajectory with appropriate Poincaré sections, allows to classify the asymptotic trajectory of a dynamical system for bifurcation analysis. Some examples of application are discussed, concerning two-dimensional bifurcation diagrams, Lyapunov exponents and finite-time Lyapunov exponents, basins of attraction, simulation of switching ODE systems, and integration with AUTO, a software package for continuation analysis.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
On the complexity of periodic and non-periodic behaviors of a hysteresis-based electronic oscillator
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