Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

On the investigation of fast electron beam filamentation in laser-irradiated solid targets using multi-MeV proton emission

The transverse filamentation of beams of fast electrons transported in solid targets irradiated by ultraintense (5 x 10(20) W cm(-2)), picosecond laser pulses is investigated experimentally. Filamentation is diagnosed by measuring the uniformity of a beam of multi-MeV protons accelerated by the sheath field formed by the arrival of the fast electrons at the rear of the target, and is investigated for metallic and insulator targets ranging in thickness from 50 to 1200 mu m. By developing an analytical model, the effects of lateral expansion of electron beam filaments in the sheath during the proton acceleration process is shown to account for measured increases in proton beam nonuniformity with target thickness for the insulating targets.

Observation of annular electron beam transport in multi-TeraWatt laser-solid interactions

Electron energy transport experiments conducted on the Vulcan 100 TW laser facility with large area foil targets are described. For plastic targets it is shown, by the plasma expansion observed in shadowgrams taken after the interaction, that there is a transition between the collimated electron flow previously reported at the 10 TW power level to an annular electron flow pattern with a 20 degrees divergence angle for peak powers of 68 TW. Intermediate powers show that both the central collimated flow pattern and the surrounding annular-shaped heated region can co-exist. The measurements are consistent with the Davies rigid beam model for fast electron flow (Davies 2003 Phys. Rev. E 68 056404) and LSP modelling provides additional insight into the observed results.

Gaussian Approximation for Tracking Occluding and Interacting Targets

In this paper, we show how interacting and occluding targets can be tackled successfully within a Gaussian approximation. For that purpose, we develop a general expansion of the mean and covariance of the posterior and we consider a first order approximation of it. The proposed method differs from EKF in that neither a non-linear dynamical model nor a non-linear measurement vector to state relation have to be defined, so it works with any kind of interaction potential and likelihood. The approach has been tested on three sequences (10400, 2500, and 400 frames each one). The results show that our approach helps to reduce the number of failures without increasing too much the computation time with respect to methods that do not take into account target interactions.

A Physically-Constrained Source Model For FDTD Acoustic Simulation

The Finite Difference Time Domain (FDTD) method is becoming increasingly popular for room acoustics simulation. Yet, the literature on grid excitation methods is relatively sparse, and source functions are traditionally implemented in a hard or additive form
using arbitrarily-shaped functions which do not necessarily obey the physical laws of sound generation. In this paper we formulate
a source function based on a small pulsating sphere model. A physically plausible method to inject a source signal into the grid
is derived from first principles, resulting in a source with a near-flat spectrum that does not scatter incoming waves. In the final
discrete-time formulation, the source signal is the result of passing a Gaussian pulse through a digital filter simulating the dynamics of the pulsating sphere, hence facilitating a physically correct means to design source functions that generate a prescribed sound field.

Gaussian mixture background modelling optimisation for micro-controllers

This paper proposes an optimisation of the adaptive Gaussian mixture background model that allows the deployment of the method on processors with low memory capacity. The effect of the granularity of the Gaussian mean-value and variance in an integer-based implementation is investigated and novel updating rules of the mixture weights are described. Based on the proposed framework, an implementation for a very low power consumption micro-controller is presented. Results show that the proposed method operates in real time on the micro-controller and has similar performance to the original model. © 2012 Springer-Verlag.

Shear Strength Model for FRP-Strengthened RC Beams with Adverse FRP-Steel Interaction

RC beams shear strengthened with externally bonded fiber-reinforced polymer (FRP) U strips or side strips usually fail owing to debonding of the bonded FRP shear reinforcement. Because such debonding usually occurs in a brittle manner at relatively small shear crack widths, some of the internal steel stirrups intersected by the critical shear crack may not have reached yielding at beam shear failure. Consequently, the yield stress of internal steel stirrups in such a strengthened RC beam cannot be fully utilized. This adverse shear interaction between the internal steel shear reinforcement and the external FRP shear reinforcement may significantly reduce the benefit of the shear strengthening FRP but has not been considered explicitly by any of the shear strength models in the existing design guidelines. This paper presents a new shear strength model considering this adverse shear interaction through the introduction of a shear interaction factor. A comprehensive evaluation of the proposed model, as well as three other shear strength models, is conducted using a large test database. It is shown that the proposed shear strength model performs the best among the models compared, and the performance of the other shear strength models can be significantly improved by including the proposed shear interaction factor. Finally, a design recommendation is presented.

An investigation into the acceleration response of a damaged beam-type structure to a moving force

In recent years there have been a growing number of publications on procedures for damage detection in beams from analysing their dynamic response to the passage of a moving force. Most of this research demonstrates their effectiveness by showing that a singularity that did not appear in the healthy structure is present in the response of the damaged structure. This paper elucidates from first principles how the acceleration response can be assumed to consist of ‘static’ and ‘dynamic’ components, and where the beam has experienced a localised loss in stiffness, an additional ‘damage’ component. The combination of these components establishes how the damage singularity will appear in the total response. For a given damage severity, the amplitude of the ‘damage’ component will depend on how close the damage location is to the sensor, and its frequency content will increase with higher velocities of the moving force. The latter has implications for damage detection because if the frequency content of the ‘damage’ component includes bridge and/or vehicle frequencies, it becomes more difficult to identify damage. The paper illustrates how a thorough understanding of the relationship between the ‘static‘ and ‘damage’ components contributes to establish if damage has occurred and to provide an estimation of its location and severity. The findings are corroborated using accelerations from a planar finite element simulation model where the effects of force velocity and bridge span are examined.

Inverse Gaussian Modeling of Turbulence-Induced Fading in Free-Space Optical Systems

We propose the inverse Gaussian distribution, as a less complex alternative to the classical log-normal model, to describe turbulence-induced fading in free-space optical (FSO) systems operating in weak turbulence conditions and/or in the presence of aperture averaging effects. By conducting goodness of fit tests, we define the range of values of the scintillation index for various multiple-input multiple-output (MIMO) FSO configurations, where the two distributions approximate each other with a certain significance level. Furthermore, the bit error rate performance of two typical MIMO FSO systems is investigated over the new turbulence model; an intensity-modulation/direct detection MIMO FSO system with Q-ary pulse position modulation that employs repetition coding at the transmitter and equal gain combining at the receiver, and a heterodyne MIMO FSO system with differential phase-shift keying and maximal ratio combining at the receiver. Finally, numerical results are presented that validate the theoretical analysis and provide useful insights into the implications of the model parameters on the overall system performance. © 2011 IEEE.

Dynamics of dark hollow Gaussian laser pulses in relativistic plasma

Optical beams with null central intensity have potential applications in the field of atom optics. The spatial and temporal evolution of a central shadow dark hollow Gaussian (DHG) relativistic laser pulse propagating in a plasma is studied in this article for first principles. A nonlinear Schrodinger-type equation is obtained for the beam spot profile and then solved numerically to investigate the pulse propagation characteristics. As series of numerical simulations are employed to trace the profile of the focused and compressed DHG laser pulse as it propagates through the plasma. The theoretical and simulation results predict that higher-order DHG pulses show smaller divergence as they propagate and, thus, lead to enhanced energy transport. © 2013 American Physical Society.

Flexural behaviour of external R/C steel fibre reinforced beam-column joints

A softened strut-and-tie macro model able to reproduce the flexural behaviour of
external beam-column joint is presented. The model is specific for concrete with hooked steel fibres (FRC) and it is designed to calculate the flexural response, as load-deflection curve, of a beam-column sub-assemblages. The model considers the presence of a constant vertical load acting on the column and of a monotonically increasing lateral force applied at the tip of the beam.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

Relative Biological Effectiveness Variation Along Monoenergetic and Modulated Bragg Peaks of a 62-MeV Therapeutic Proton Beam: A Preclinical Assessment

iological optimization of proton therapy critically depends on detailed evaluation of relative biological effectiveness (RBE) variations along the Bragg curve. The clinically accepted RBE value of 1.1 is an oversimplification, which disregards the steep rise of linear energy transfer (LET) at the distal end of the spread-out Bragg peak. We observed significant cell killing RBE variations dependent on beam modulation, intrinsic radiosensitivity, and LET in agreement with the LEM predicted values, indicating dose-averaged LET as a suitable parameter for biological effectiveness. Data have also been used to validate a RBE parameterized model.

Kinetostatic-model-based Stiffness Analysis of Exechon PKM

As a comparative newly-invented PKM with over-constraints in kinematic chains, the Exechon has attracted extensive attention from the research society. Different from the well-recognized kinematics analysis, the research on the stiffness characteristics of the Exechon still remains as a challenge due to the structural complexity. In order to achieve a thorough understanding of the stiffness characteristics of the Exechon PKM, this paper proposed an analytical kinetostatic model by using the substructure synthesis technique. The whole PKM system is decomposed into a moving platform subsystem, three limb subsystems and a fixed base subsystem, which are connected to each other sequentially through corresponding joints. Each limb body is modeled as a spatial beam with a uniform cross-section constrained by two sets of lumped springs. The equilibrium equation of each individual limb assemblage is derived through finite element formulation and combined with that of the moving platform derived with Newtonian method to construct the governing kinetostatic equations of the system after introducing the deformation compatibility conditions between the moving platform and the limbs. By extracting the 6 x 6 block matrix from the inversion of the governing compliance matrix, the stiffness of the moving platform is formulated. The computation for the stiffness of the Exechon PKM at a typical configuration as well as throughout the workspace is carried out in a quick manner with a piece-by-piece partition algorithm. The numerical simulations reveal a strong position-dependency of the PKM's stiffness in that it is symmetric relative to a work plane due to structural features. At the last stage, the effects of some design variables such as structural, dimensional and stiffness parameters on system rigidity are investigated with the purpose of providing useful information for the structural optimization and performance enhancement of the Exechon PKM. It is worthy mentioning that the proposed methodology of stiffness modeling in this paper can also be applied to other overconstrained PKMs and can evaluate the global rigidity over workplace efficiently with minor revisions.