Ferroelectric and Dipolar Glass Phases of Non-Crystalline Systems

In a recent letter [Phys. Rev. Lett. {\bf 75}, 2360 (1996)] we briefly discussed the existence and nature of ferroelectric order in positionally disordered dipolar materials. Here we report further results and give a complete description of our work. Simulations of randomly frozen and dynamically disordered dipolar soft spheres are used to study ferroelectric ordering in non-crystalline systems. We also give a physical interpretation of the simulation results in terms of short- and long-range interactions. Cases where the dipole moment has 1, 2, and 3 components (Ising, XY and XYZ models, respectively) are considered. It is found that the Ising model displays ferroelectric phases in frozen amorphous systems, while the XY and XYZ models form dipolar glass phases at low temperatures. In the dynamically disordered model the equations of motion are decoupled such that particle translation is completely independent of the dipolar forces. These systems spontaneously develop long-range ferroelectric order at nonzero temperature despite the absence of any fined-tuned short-range spatial correlations favoring dipolar order. Furthermore, since this is a nonequilibrium model we find that the paraelectric to ferroelectric transition depends on the particle mass. For the XY and XYZ models, the critical temperatures extrapolate to zero as the mass of the particle becomes infinite, whereas, for the Ising model the critical temperature is almost independent of mass and coincides with the ferroelectric transition found for the randomly frozen system at the same density. Thus in the infinite mass limit the results of the frozen amorphous systems are recovered.Comment: 25 pages (LATEX, no macros). 11 POSTSCRIPT figures enclosed. Submitted to Phisical Review E. Contact: [email protected]

Dynamic phase transitions in a ferromagnetic thin film system: A Monte Carlo simulation study

Dynamic phase transition properties of ferromagnetic thin film system under the influence both bias and time dependent magnetic fields have been elucidated by means of kinetic Monte Carlo simulation with local spin update Metropolis algorithm. The obtained results after a detailed analysis suggest that bias field is the conjugate field to dynamic order parameter, and it also appears to define a phase line between two antiparallel dynamic ordered states depending on the considered system parameters. Moreover, the data presented in this study well qualitatively reproduce the recently published experimental findings where time dependent magnetic behavior of a uniaxial cobalt films is studied in the neighborhood of dynamic phase transition point.Comment: 15 pages, 5 Figure

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS) algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set

Improving games AI performance using grouped hierarchical level of detail

Computer games are increasingly making use of large environments; however, these are often only sparsely populated with autonomous agents. This is, in part, due to the computational cost of implementing behaviour functions for large numbers of agents. In this paper we present an optimisation based on level of detail which reduces the overhead of modelling group behaviours, and facilitates the population of an expansive game world. We consider an environment which is inhabited by many distinct groups of agents. Each group itself comprises individual agents, which are organised using a hierarchical tree structure. Expanding and collapsing nodes within each tree allows the efficient dynamic abstraction of individuals, depending on their proximity to the player. Each branching level represents a different level of detail, and the system is designed to trade off computational performance against behavioural fidelity in a way which is both efficient and seamless to the player. We have developed an implementation of this technique, and used it to evaluate the associated performance benefits. Our experiments indicate a significant potential reduction in processing time, with the update for the entire AI system taking less than 1% of the time required for the same number of agents without optimisation

Distributed LQR design for identical dynamically coupled systems: Application to load frequency control of multi-area power grid

The paper proposes a distributed LQR method for the solution to regulator problems of networks composed of dynamically dependent agents. It is assumed that these dynamical couplings among agents can be expressed in a state-space form of a certain structure. Following a top-down approach we approximate a centralized LQR optimal controller by a distributed scheme the stability of which is guaranteed via a stability test applied to convex combination of Hurwitz matrices. The method is applied to N-identical-area power grid where a distributed state-feedback Load Frequency Controller (LFC) is proposed to achieve frequency regulation under power demand variations. An illustrative numerical example demonstrates the applicability of the method