Signal Analysis Using Born–Jordan-Type Distributions

In this chapter, we exhibit recent advances in signal analysis via time–frequency distributions. New members of the Cohen class, generalizing the Wigner distribution, reveal to be effective in damping artefacts of some signals. We will survey their main properties and drawbacks and present open problems related to such phenomena

A Lyapunov Approach to Control of Microgrids with a Network-Preserved Differential-Algebraic Model

We provide sufficient conditions for asymptotic stability and optimal resource allocation for a networkpreserved microgrid model with active and reactive power loads. The model considers explicitly the presence of constantpower loads as well as the coupling between the phase angle and voltage dynamics. The analysis of the resulting nonlinear differential algebraic equation (DAE) system is conducted by leveraging incremental Lyapunov functions, definiteness of the load flow Jacobian and the implicit function theorem

N-2(+)((2)Sigma(g)) and Rb(S-2) in a hybrid trap: modeling ion losses from radiative association paths

By employing ab initio computed intermolecular potential energy surfaces we calculate the radiative association probabilities and rates for two different associative mechanisms involving trapped molecular ions N-2(+)((2)sigma(g)) interacting either directly with ultracold Rb atoms or undergoing charge-exchange (CE) processes leading to the formation of complexes of the strongly exothermic products N-2(X-1 sigma(g)) plus Rb+(S-1(0)). The two processes are expected to provide possible paths to ion losses in the trap within the timescale of experiments. The present calculations suggest that the associative rates for the vibrational' direct process are too small to be of any significant importance at the millikelvin temperatures considered in the experiments, while the vibronic' path into radiatively associating the CE products has a probability of occurring which is several orders of magnitude larger. However the reaction rate constants attributed to non-adiabatic CE [F. H. J. Hall and S. Willist, Phys. Rev. Lett., 2012, 109, 233202] are in turn several orders of magnitude larger than the radiative ones calculated here, thereby making the primary experimental process substantially unaffected by the radiative losses channel

Synchronization in Complex Oscillator Networks and Smart Grids

The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A coupled oscillator network is characterized by a population of heterogeneous oscillators and a graph describing the interaction among them. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here we present a novel, concise, and closed-form condition for synchronization of the fully nonlinear, non-equilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters, or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters, they are statistically correct for almost all networks, and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks such as electric power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex networks scenarios and in smart grid applications

Synchronization of coupled limit cycles

A unified approach for analyzing synchronization in coupled systems of autonomous differential equations is presented in this work. Through a careful analysis of the variational equation of the coupled system we establish a sufficient condition for synchronization in terms of the geometric properties of the local limit cycles and the coupling operator. This result applies to a large class of differential equation models in physics and biology. The stability analysis is complemented with a discussion of numerical simulations of a compartmental model of a neuron.Comment: Journal of Nonlinear Science, accepte

Subject specific demands of teaching: Implications for out-of-field teachers

This chapter provides a framework for thinking about the subject-specific nature of teaching in terms of the knowledge, modes of inquiry and discursive practices that delineate one subject from another in the traditional school curriculum. The chapter will explore how these disciplinary traits are translated into teaching as curriculum, knowledge and pedagogy, and how this subject-specificity of teaching is juxtaposed against the more generic aspects of teaching. The chapter explores the idea that if a teacher’s expertise can be situated within a field, then they can also be positioned out-of-field. Implications for teaching out-of-field are discussed in terms of the subject-specific knowledge, processes and skills, and the difficulties associated with teacher practice. English and Australian illustrations of teacher practices from in-field and out-of-field situations are provided, in particular highlighting the demands of moving across subject boundaries. Cross-fertilisation is especially evident when subjects are integrated, therefore, the issues associated with integrated curriculum are discussed where the traditional subject boundaries are being challenged as schools are reorganised to integrate subjects through, for example, STEM teaching, or holistic curriculum designs

Data-driven predictive current control for synchronous motor drives

Data-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of data-driven methods to the control of electric motor drives, and specifically to the design of current controllers for three-phase synchronous permanent magnet motor drives. Two of the most promising data-driven algorithms are presented, namely the Subspace Predictive Control algorithm and the Data-Enabled Predictive Control algorithm. The theory behind these techniques is first reviewed in the optimization-based control framework. Standard algorithms are slightly modified to fulfill the requirements of the specific application, and then simulated in the MATLAB Simulink environment. Some key aspects of real-time implementation are studied, providing a proof-of-concept demonstration of the applicability of these algorithms. The data-driven design is proposed for three different topologies of synchronous motors, proving the flexibility of the approach