A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

Cost-exergy optimisation of linear Fresnel reflectors

This paper presents a new method for the optimisation of the mirror element spacing arrangement and operating temperature of linear Fresnel reflectors (LFR). The specific objective is to maximise available power output (i.e. exergy) and operational hours whilst minimising cost. The method is described in detail and compared to an existing design method prominent in the literature. Results are given in terms of the exergy per total mirror area (W/m2) and cost per exergy (US $/W). The new method is applied principally to the optimisation of an LFR in Gujarat, India, for which cost data have been gathered. It is recommended to use a spacing arrangement such that the onset of shadowing among mirror elements occurs at a transversal angle of 45°. This results in a cost per exergy of 2.3 $/W. Compared to the existing design approach, the exergy averaged over the year is increased by 9% to 50 W/m2 and an additional 122 h of operation per year are predicted. The ideal operating temperature at the surface of the absorber tubes is found to be 300 °C. It is concluded that the new method is an improvement over existing techniques and a significant tool for any future design work on LFR systems

The tribology of linear tape recording systems

This thesis is devoted to the tribology at the head~to~tape interface of linear tape recording systems, OnStream ADRTM system being used as an experimental platform, Combining experimental characterisation with computer modelling, a comprehensive picture of the mechanisms involved in a tape recording system is drawn. The work is designed to isolate the mechanisms responsible for the physical spacing between head and tape with the aim of minimising spacing losses and errors and optimising signal output. Standard heads-used in ADR current products-and prototype heads- DLC and SPL coated and dummy heads built from a AI203-TiC and alternative single-phase ceramics intended to constitute the head tape-bearing surface-are tested in controlled environment for up to 500 hours (exceptionally 1000 hours), Evidences of wear on the standard head are mainly observable as a preferential wear of the TiC phase of the AI203-TiC ceramic, The TiC grains are believed to delaminate due to a fatigue wear mechanism, a hypothesis further confirmed via modelling, locating the maximum von Mises equivalent stress at a depth equivalent to the TiC recession (20 to 30 nm). Debris of TiC delaminated residues is moreover found trapped within the pole-tip recession, assumed therefore to provide three~body abrasive particles, thus increasing the pole-tip recession. Iron rich stain is found over the cycled standard head surface (preferentially over the pole-tip and to a lesser extent over the TiC grains) at any environment condition except high temperature/humidity, where mainly organic stain was apparent, Temperature (locally or globally) affects staining rate and aspect; stain transfer is generally promoted at high temperature. Humidity affects transfer rate and quantity; low humidity produces, thinner stains at higher rate. Stain generally targets preferentially head materials with high electrical conductivity, i.e. Permalloy and TiC. Stains are found to decrease the friction at the head-to-tape interface, delay the TiC recession hollow-out and act as a protective soft coating reducing the pole-tip recession. This is obviously at the expense of an additional spacing at the head-to-tape interface of the order of 20 nm. Two kinds of wear resistant coating are tested: diamond like carbon (DLC) and superprotective layer (SPL), 10 nm and 20 to 40 nm thick, respectively. DLC coating disappears within 100 hours due possibly to abrasive and fatigue wear. SPL coatings are generally more resistant, particularly at high temperature and low humidity, possibly in relation with stain transfer. 20 nm coatings are found to rely on the substrate wear behaviour whereas 40 nm coatings are found to rely on the adhesive strength at the coating/substrate interface. These observations seem to locate the wear-driving forces 40 nm below the surface, hence indicate that for coatings in the 10 nm thickness range-· i,e. compatible with high-density recording-the substrate resistance must be taken into account. Single-phase ceramic as candidate for wear-resistant tape-bearing surface are tested in form of full-contour dummy-heads. The absence of a second phase eliminates the preferential wear observed at the AI203-TiC surface; very low wear rates and no evidence of brittle fracture are observed.

Assessing interactions of linear and nonlinear neuronal sources using MEG beamformers:a proof of concept

Objective: This study aimed to explore methods of assessing interactions between neuronal sources using MEG beamformers. However, beamformer methodology is based on the assumption of no linear long-term source interdependencies [VanVeen BD, vanDrongelen W, Yuchtman M, Suzuki A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans Biomed Eng 1997;44:867-80; Robinson SE, Vrba J. Functional neuroimaging by synthetic aperture magnetometry (SAM). In: Recent advances in Biomagnetism. Sendai: Tohoku University Press; 1999. p. 302-5]. Although such long-term correlations are not efficient and should not be anticipated in a healthy brain [Friston KJ. The labile brain. I. Neuronal transients and nonlinear coupling. Philos Trans R Soc Lond B Biol Sci 2000;355:215-36], transient correlations seem to underlie functional cortical coordination [Singer W. Neuronal synchrony: a versatile code for the definition of relations? Neuron 1999;49-65; Rodriguez E, George N, Lachaux J, Martinerie J, Renault B, Varela F. Perception's shadow: long-distance synchronization of human brain activity. Nature 1999;397:430-3; Bressler SL, Kelso J. Cortical coordination dynamics and cognition. Trends Cogn Sci 2001;5:26-36]. Methods: Two periodic sources were simulated and the effects of transient source correlation on the spatial and temporal performance of the MEG beamformer were examined. Subsequently, the interdependencies of the reconstructed sources were investigated using coherence and phase synchronization analysis based on Mutual Information. Finally, two interacting nonlinear systems served as neuronal sources and their phase interdependencies were studied under realistic measurement conditions. Results: Both the spatial and the temporal beamformer source reconstructions were accurate as long as the transient source correlation did not exceed 30-40 percent of the duration of beamformer analysis. In addition, the interdependencies of periodic sources were preserved by the beamformer and phase synchronization of interacting nonlinear sources could be detected. Conclusions: MEG beamformer methods in conjunction with analysis of source interdependencies could provide accurate spatial and temporal descriptions of interactions between linear and nonlinear neuronal sources. Significance: The proposed methods can be used for the study of interactions between neuronal sources. © 2005 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Using interior point algorithms for the solution of linear programs with special structural features

Linear Programming (LP) is a powerful decision making tool extensively used in various economic and engineering activities. In the early stages the success of LP was mainly due to the efficiency of the simplex method. After the appearance of Karmarkar's paper, the focus of most research was shifted to the field of interior point methods. The present work is concerned with investigating and efficiently implementing the latest techniques in this field taking sparsity into account. The performance of these implementations on different classes of LP problems is reported here. The preconditional conjugate gradient method is one of the most powerful tools for the solution of the least square problem, present in every iteration of all interior point methods. The effect of using different preconditioners on a range of problems with various condition numbers is presented. Decomposition algorithms has been one of the main fields of research in linear programming over the last few years. After reviewing the latest decomposition techniques, three promising methods were chosen the implemented. Sparsity is again a consideration and suggestions have been included to allow improvements when solving problems with these methods. Finally, experimental results on randomly generated data are reported and compared with an interior point method. The efficient implementation of the decomposition methods considered in this study requires the solution of quadratic subproblems. A review of recent work on algorithms for convex quadratic was performed. The most promising algorithms are discussed and implemented taking sparsity into account. The related performance of these algorithms on randomly generated separable and non-separable problems is also reported.

System identification of linear vibrating structures

Methods of dynamic modelling and analysis of structures, for example the finite element method, are well developed. However, it is generally agreed that accurate modelling of complex structures is difficult and for critical applications it is necessary to validate or update the theoretical models using data measured from actual structures. The techniques of identifying the parameters of linear dynamic models using Vibration test data have attracted considerable interest recently. However, no method has received a general acceptance due to a number of difficulties. These difficulties are mainly due to (i) Incomplete number of Vibration modes that can be excited and measured, (ii) Incomplete number of coordinates that can be measured, (iii) Inaccuracy in the experimental data (iv) Inaccuracy in the model structure. This thesis reports on a new approach to update the parameters of a finite element model as well as a lumped parameter model with a diagonal mass matrix. The structure and its theoretical model are equally perturbed by adding mass or stiffness and the incomplete number of eigen-data is measured. The parameters are then identified by an iterative updating of the initial estimates, by sensitivity analysis, using eigenvalues or both eigenvalues and eigenvectors of the structure before and after perturbation. It is shown that with a suitable choice of the perturbing coordinates exact parameters can be identified if the data and the model structure are exact. The theoretical basis of the technique is presented. To cope with measurement errors and possible inaccuracies in the model structure, a well known Bayesian approach is used to minimize the least squares difference between the updated and the initial parameters. The eigen-data of the structure with added mass or stiffness is also determined using the frequency response data of the unmodified structure by a structural modification technique. Thus, mass or stiffness do not have to be added physically. The mass-stiffness addition technique is demonstrated by simulation examples and Laboratory experiments on beams and an H-frame.

An application of linear programming in the pottery industry

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Dynamics of linear polymers in random media

We study phenomenological scaling theories of the polymer dynamics in random media, employing the existing scaling theories of polymer chains and the percolation statistics. We investigate both the Rouse and the Zimm model for Brownian dynamics and estimate the diffusion constant of the center-of-mass of the chain in such disordered media. For internal dynamics of the chain, we estimate the dynamic exponents. We propose similar scaling theory for the reptation dynamics of the chain in the framework of Flory theory for the disordered medium. The modifications in the case of correlated disorders are also discussed. .

Impact of linear mode coupling on the group delay spread in few-mode fibers

We report an investigation on the group delay spread in few-mode fibers operating in the weak and strong linear coupling regimes, and for the first time, we study the transition region between them. A single expression linking the group delay spread to the fiber correlation length is validated for any coupling regime, considering 3 guided modes.

On the group delay statistics of few-mode fibres with intermediate linear mode coupling

We report an investigation on the statistics of group delay for few-mode fibres operating in the weak and strong linear coupling regimes as well as in the intermediate coupling regime. A single expression linking the standard deviation of the group delay spread to the fibre linear mode coupling is validated for any coupling regime, considering up to six linearly polarized guided modes. Furthermore, the study of the probability density function of the group delays allowed deriving and validating an analytical estimation for the maximum group delay spread as a function of linear mode coupling.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Phakometric measurement of ocular surface radii of curvature, axial separations and alignment in relaxed and accommodated human eyes

Measurements (autokeratometry, A-scan ultrasonography and video ophthalmophakometry) of ocular surface radii, axial separations and alignment were made in the horizontal meridian of nine emmetropes (aged 20-38 years) with relaxed (cycloplegia) and active accommodation (mean ± 95% confidence interval: 3.7 ± 1.1 D). The anterior chamber depth (-1.5 ± 0.3 D) and both crystalline lens surfaces (front 3.1 ± 0.8 D; rear 2.1 ± 0.6 D) contributed to dioptric vergence changes that accompany accommodation. Accommodation did not alter ocular surface alignment. Ocular misalignment in relaxed eyes is mainly because of eye rotation (5.7 ± 1.6° temporally) with small amounts of lens tilt (0.2 ± 0.8° temporally) and decentration (0.1 ± 0.1 mm nasally) but these results must be viewed with caution as we did not account for corneal asymmetry. Comparison of calculated and empirically derived coefficients (upon which ocular surface alignment calculations depend) revealed that negligible inherent errors arose from neglect of ocular surface asphericity, lens gradient refractive index properties, surface astigmatism, effects of pupil size and centration, assumed eye rotation axis position and use of linear equations for analysing Purkinje image shifts. © 2004 The College of Optometrists.

Cascaded Bayesian processes: an account of bias in orientation perception

Following adaptation to an oriented (1-d) signal in central vision, the orientation of subsequently viewed test signals may appear repelled away from or attracted towards the adapting orientation. Small angular differences between the adaptor and test yield 'repulsive' shifts, while large angular differences yield 'attractive' shifts. In peripheral vision, however, both small and large angular differences yield repulsive shifts. To account for these tilt after-effects (TAEs), a cascaded model of orientation estimation that is optimized using hierarchical Bayesian methods is proposed. The model accounts for orientation bias through adaptation-induced losses in information that arise because of signal uncertainties and neural constraints placed upon the propagation of visual information. Repulsive (direct) TAEs arise at early stages of visual processing from adaptation of orientation-selective units with peak sensitivity at the orientation of the adaptor (theta). Attractive (indirect) TAEs result from adaptation of second-stage units with peak sensitivity at theta and theta+90 degrees , which arise from an efficient stage of linear compression that pools across the responses of the first-stage orientation-selective units. A spatial orientation vector is estimated from the transformed oriented unit responses. The change from attractive to repulsive TAEs in peripheral vision can be explained by the differing harmonic biases resulting from constraints on signal power (in central vision) versus signal uncertainties in orientation (in peripheral vision). The proposed model is consistent with recent work by computational neuroscientists in supposing that visual bias reflects the adjustment of a rational system in the light of uncertain signals and system constraints.