A bayesian artificial neural network method to characterise laminar defects using dynamic measurements

This paper reports on the development of an artificial neural network (ANN) method to detect laminar defects following the pattern matching approach utilizing dynamic measurement. Although structural health monitoring (SHM) using ANN has attracted much attention in the last decade, the problem of how to select the optimal class of ANN models has not been investigated in great depth. It turns out that the lack of a rigorous ANN design methodology is one of the main reasons for the delay in the successful application of the promising technique in SHM. In this paper, a Bayesian method is applied in the selection of the optimal class of ANN models for a given set of input/target training data. The ANN design method is demonstrated for the case of the detection and characterisation of laminar defects in carbon fibre-reinforced beams using flexural vibration data for beams with and without non-symmetric delamination damage.

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood and adult-onset schizophrenia, bipolar disorder, attention-deficit/ hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages. (C) 2004 Published by Elsevier Inc.

An equivalent waveguide approach to designing of reflect arrays with the use of variable-size microstrip patches

An equivalent unit cell waveguide approach (WGA) is described to study the behavior of a multilayer reflect array of variable-size patches/dipoles, The approach considers normal incidence of a plane wave on an infinite periodic array of identical radiating elements and introduces an equivalent unit cell waveguide to obtain the reflection coefficient. A field matching technique and method of moments (MoM) is used to determine fields in different layers of the equivalent waveguide. Good agreements for the phase of the reflection coefficient between the proposed model and those published in selected literatures are obtained. (C) 2002 Wiley Periodicals, Inc.

Designing a 161-element Ku-band microstrip reflectarray of variable size patches using an equivalent unit cell waveguide approach

An equivalent unit cell waveguide approach (WGA) to designing 4 multilayer microstrip reflectarray of variable size patches is presented. In this approach, a normal incidence of a plane wave on an infinite periodic array of radiating elements is considered to obtain reflection coefficient phase curves for the reflectarray's elements. It is shown that this problem is equivalent to the problem of reflection of the dominant TEM mode in a waveguide with patches interleaved by layers of dielectric. This waveguide problem is solved using a field matching technique and a method of moments (MoM). Based on this solution, a fast computer algorithm is developed to generate reflection coefficient phase curves for a multilayer microstrip patch reflectarray. The validity of the developed algorithm is tested against alternative approaches and Agilent High Frequency Structure Simulator (HFSS). Having confirmed the validity of the WGA approach, a small offset feed two-layer microstrip patch array is designed and developed. This reflectarray is tested experimentally and shows good performance.

Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.

A wavelet-based adaptive technique for adsorption problems involving steep gradients

A general, fast wavelet-based adaptive collocation method is formulated for heat and mass transfer problems involving a steep moving profile of the dependent variable. The technique of grid adaptation is based on sparse point representation (SPR). The method is applied and tested for the case of a gas–solid non-catalytic reaction in a porous solid at high Thiele modulus. Accurate and convergent steep profiles are obtained for Thiele modulus as large as 100 for the case of slab and found to match the analytical solution.

A matching pursuit-based signal complexity measure for the analysis of newborn EEG

This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity, which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed relative structural complexity measure is used in the analysis of newborn EEG. To do this, firstly, a time-frequency (TF) decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).

A finite integral transform technique for solving the diffusion-reaction equation with Michaelis-Menten kinetics

An approximate analytical technique employing a finite integral transform is developed to solve the reaction diffusion problem with Michaelis-Menten kinetics in a solid of general shape. A simple infinite series solution for the substrate concentration is obtained as a function of the Thiele modulus, modified Sherwood number, and Michaelis constant. An iteration scheme is developed to bring the approximate solution closer to the exact solution. Comparison with the known exact solutions for slab geometry (quadrature) and numerically exact solutions for spherical geometry (orthogonal collocation) shows excellent agreement for all values of the Thiele modulus and Michaelis constant.

New calibration technique for multiple-component stress wave force balances

Force measurement in hypervelocity expansion tubes is not possible using conventional techniques. The stress wave force balance technique can be applied in expansion tubes to measure forces despite the short test times involved. This paper presents a new calibration technique for multiple-component stress wave force balances where an impulse response created using a load distribution is required and no orthogonal surfaces on the model exist.. This new technique relies on the tensorial superposition of single-component impulse responses analogous to the vectorial superposition of the calibration loads. The example presented here is that of a scale model of the Mars Pathfinder, but the technique is applicable to any geometry and may be useful for cases where orthogonal loads cannot be applied.

Sensitive detection of nitric oxide using seeded parametric four-wave mixing

A sensitive near-resonant four-wave mixing technique based on two-photon parametric four-wave mixing has been developed. Seeded parametric four-wave mixing requires only a single laser as an additional phase matched seeder field is generated via parametric four-wave mixing of the pump beam in a high gain cell. The seeder field travels collinearly with the pump beam providing efficient nondegenerate four-wave mixing in a second medium. This simple arrangement facilitates the detection of complex molecular spectra by simply scanning the pump laser. Seeded parametric four-wave mixing is demonstrated in both a low pressure cell and an air/acetylene flame with detection of the two-photon C (2) Pi(upsilon'=0)<--X (2) Pi(upsilon =0) spectrum of nitric oxide. From the cell data a detection limit of 10(12) molecules/cm(3) is established. A theoretical model of seeded parametric four-wave mixing is developed from existing parametric four-wave mixing theory. The addition of the seeder field significantly modifies the parametric four-wave mixing behaviour such that in the small signal regime, the signal intensity can readily be made to scale as the cube of the laser pump power while the density dependence follows a more familiar square law dependence, In general, we find excellent agreement between theory and experiment. Limitations to the process result from an ac Stark shift of the two-photon resonance in the high pressure seeder cell caused by the generation of a strong seeder field, as well as a reduction in phase matching efficiency due to the presence of certain buffer species. Various optimizations are suggested which should overcome these limitations, providing even greater detection sensitivity. (C) 1998 American Institute of Physics, [S0021-9606(98)01014-9].

CO2 reforming of methane on Ni catalysts: Effects of the support phase and preparation technique

The effects of the support phase and catalyst preparation methods on catalytic activity and carbon deposition were systematically investigated over nickel catalysts supported on Al2O3, SiO2 and MgO for the reforming reaction of methane with carbon dioxide. It is found that the pore structure of the support and metal-support interaction significantly affected the catalytic activity and coking resistance. Catalyst with well-developed porosity exhibited higher catalytic activity. Strong interaction between metal and the support made the catalyst more resistant to sintering and coking, thus resulting in a longer time of catalyst stability. (C) 1998 Elsevier Science B.V.

Effect of load distance on self-selected manual lifting technique

The aim of this research is to determine the effects of constraining the horizontal distance of the feet from the load on the posture adopted at the start of the lift. Kinematic data were collected while each of 24 subjects lifted 3, 6, and 9 kg loads from a starting height 18 cm above the ground. The position of the feet was controlled relative to the load such that the horizontal distance from the hand to the ankle at the start of extension was either 20, 40, or 60 cm. Subjects performed 20 trials in each of six combinations of load and ankle-load distance chosen to provide three sets of equivilent load moment pairs. The initial horizontal distance from the load to the ankle had a large influence on the posture adopted to lift the load. Ankle and knee flexion, in particular, were reduced when the ankle-load distance was smaller, and particularly so when the distance was reduced to 20 cm. Hip flexion was reduced to a smaller extent, while lumbar vertebral flexion remained relatively unchanged. The inclination of the trunk at the start of the lift was unchanged when the ankle-load distance was 60 or 40 cm, but was 10 degrees greater when the load was 20 cm from the ankles, indicating that subjects adopted a posture closer to a stoop when the ankle-load distance was small. Comparison of conditions of equal load moment (but different load mass and ankle-load distance) revealed differences which mirrored the effects of ankle-load distance alone, suggesting that the effects of ankle-load distance on the posture adopted at the start of extension were largely independent of the load moment. While the forces and torques required to lift a load must be to some extent dependent on the load moment, rather than load or ankle-load distance per se, the posture adopted to lift the load is not.