Half-quantum vortex state in a spin-orbit coupled Bose-Einstein condensate

We investigate theoretically the condensate state and collective excitations of a two-component Bose gas in two-dimensional harmonic traps subject to isotropic Rashba spin-orbit coupling. In the weakly interacting regime when the inter-species interaction is larger than the intra-species interaction ($g_{\uparrow\downarrow}>g$), we find that the condensate ground state has a half-quantum-angular-momentum vortex configuration with spatial rotational symmetry and skyrmion-type spin texture. Upon increasing the interatomic interaction beyond a threshold $g_{c}$, the ground state starts to involve higher-order angular momentum components and thus breaks the rotational symmetry. In the case of $g_{\uparrow\downarrow}<g$, the condensate becomes unstable towards the superposition of two degenerate half-quantum vortex states. Both instabilities (at $g>g_{c}$ and $g_{\uparrow\downarrow}<g$) can be determined by solving the Bogoliubov equations for collective density oscillations of the half-quantum vortex state, and by analyzing the softening of mode frequencies. We present the phase diagram as functions of the interatomic interactions and the spin-orbit coupling. In addition, we directly simulate the time-dependent Gross-Pitaevskii equation to examine the dynamical properties of the system. Finally, we investigate the stability of the half-quantum vortex state against both the trap anisotropy and anisotropy in the spin-orbit coupling term.Comment: 13 pages, 18 figure

Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea

Numerical dynamic analysis of reciprocating compressor mechanism. Parametric studies for optimization purposes

© 2016. This version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A complete numerical dynamic analysis of reciprocating compressor mechanism is presented, coupling the instantaneous pressure in the compression chamber, the electric motor torque and the hydrodynamic reactions, which arise from the piston and crankshaft secondary movements. Additionally, non-constant crankshaft angular velocity and the piston and crankshaft misalignment torques have also been considered. Two sensitivity analyses have been carried out to prove that neither the inertial forces in the directions of the secondary movements, nor the oscillations of the angular velocity produce significant differences in the compressor behaviour. Finally, a set of parametric studies has been developed to evaluate the influence of geometrical parameters in the stability of the secondary movements, the friction power losses and the compressor consumptionPeer ReviewedPostprint (author's final draft

Application of artificial intelligence techniques for rolling dynamic compaction

Rolling dynamic compaction (RDC), involving non-circular modules towed behind a tractor, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method to reliably predict the increase in soil strength after the application of a given number of passes of RDC. This paper presents the application of artificial intelligence (AI) techniques in the form of artificial neural networks (ANNs) and genetic programming (GP) for a priori prediction of the density improvement by means of RDC in a range of ground conditions. These AI-based models are developed by using in situ soil test data, specifically cone penetration test (CPT) and dynamic cone penetration (DCP) test data obtained from several ground improvement projects that employed the 4- sided, 8-tonne ‘impact roller’. The predictions of ANN- and GP-based models are compared with the corresponding actual values and they show strong correlations (r > 0.8). Additionally, the robustness of the optimal models is investigated in a parametric study and it is observed that the model predictions are in a good agreement with the expected behaviour of RDC.R. A. T. M. Ranasinghe and M. B. Jaks

Continuous correlated beta processes

In this paper we consider a (possibly continuous) space of Bernoulli experiments. We assume that the Bernoulli distributions are correlated. All evidence data comes in the form of successful or failed experiments at different points. Current state-ofthe-art methods for expressing a distribution over a continuum of Bernoulli distributions use logistic Gaussian processes or Gaussian copula processes. However, both of these require computationally expensive matrix operations (cubic in the general case). We introduce a more intuitive approach, directly correlating beta distributions by sharing evidence between them according to a kernel function, an approach which has linear time complexity. The approach can easily be extended to multiple outcomes, giving a continuous correlated Dirichlet process, and can be used for both classification and learning the actual probabilities of the Bernoulli distributions. We show results for a number of data sets, as well as a case-study where a mixture of continuous beta processes is used as part of an automated stroke rehabilitation system.

Energy-based numerical models for assessment of soil liquefaction

AbstractThis study presents promising variants of genetic programming (GP), namely linear genetic programming (LGP) and multi expression programming (MEP) to evaluate the liquefaction resistance of sandy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction (capacity energy) and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction