A Statistical Model for Shadowed Body Centric Communications Channels: Theory and Validation

This paper presents a new statistical signal reception model for shadowed body-centric communications channels. In this model, the potential clustering of multipath components is considered alongside the presence of elective dominant signal components. As typically occurs in body-centric communications channels, the dominant or line-of-sight (LOS) components are shadowed by body matter situated in the path trajectory. This situation may be further exacerbated due to physiological and biomechanical movements of the body. In the proposed model, the resultant dominant component which is formed by the phasor addition of these leading contributions is assumed to follow a lognormal distribution. A wide range of measured and simulated shadowed body-centric channels considering on-body, off-body and body-to-body communications are used to validate the model. During the course of the validation experiments, it was found that, even for environments devoid of multipath or specular reflections generated by the local surroundings, a noticeable resultant dominant component can still exist in body-centric channels where the user's body shadows the direct LOS signal path between the transmitter and the receiver.

Detailed validation of an automotive catalysis model using spatially resolved measurements within the catalyst substrate

Many kinetic models have appeared in literature in past decades using two main approaches: the traditional global kinetics approach, or the more complex micro-kinetics approach. Whether global or micro-kinetics, kinetic models have been based on experimental data obtained at the end of the monolith. The experimental procedure using end pipe analysis may give an accurate overview of the reaction mechanisms that occur; however, the lack of information from within the catalyst can ultimately lead to inaccuracies in the kinetic model and parameters used.

Using SpaciMS, a spatially resolved experimental technique developed at the Queen's University Belfast, information from within the catalyst can be obtained. This minimally invasive technique provides detailed information of the gas concentration and temperature profile from inside the catalytic monolith. This paper presents a kinetic model and simulations validated against experimental data obtained from three positions inside the catalyst monolith at 2, 14, and 26 mm in, using data from the SpaciMS. Also, simulations of end pipe analysis, using a commercial reactor, for the CO oxidation are presented and analyzed. The simulations presented are for varying concentrations of both CO and O2 (0.5 % and 1 % CO, 0.5 % and 2 % O2) for both the global and micro-kinetic approach.

Validation of a 3D damage model for predicting the response of composite structures under crushing loads

A 3D intralaminar continuum damage mechanics based material model, combining damage mode interaction and material nonlinearity, was developed to predict the damage response of composite structures undergoing crush loading. This model captures the structural response without the need for calibration of experimentally determined material parameters. When used in the design of energy absorbing composite structures, it can reduce the dependence on physical testing. This paper validates this model against experimental data obtained from the literature and in-house testing. Results show that the model can predict the force response of the crushed composite structures with good accuracy. The simulated energy absorption in each test case was within 12% of the experimental value. Post-crush deformation and the damage morphologies, such as ply splitting, splaying and breakage, were also accurately reproduced. This study establishes the capability of this damage model for predicting the responses of composite structures under crushing loads.

The Development of a Two-Dimensional Transient Catalyst Model for Direct Injection Two-Stroke Applications.

This paper describes the development of a two-dimensional transient catalyst model. Although designed primarily for two-stroke direct injection engines, the model is also applicable to four-stroke lean burn and diesel applications. The first section describes the geometries, properties and chemical processes simulated by the model and discusses the limitations and assumptions applied. A review of the modeling techniques adopted by other researchers is also included. The mathematical relationships which are used to represent the system are then described, together with the finite volume method used in the computer program. The need for a two-dimensional approach is explained and the methods used to model effects such as flow and temperature distribution are presented. The problems associated with developing surface reaction rates are discussed in detail and compared with published research. Validation and calibration of the model is achieved by comparing predictions with measurements from a flow reactor. While an extensive validation process, involving detailed measurements of gas composition and thermal gradients, has been completed, the analysis is too detailed for publication here and is the subject of a separate technical paper.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Surveying nursing students on their sources of stress: A validation study

This study tested the psychometric properties of a questionnaire that measured sources of distress and eustress, or good stress, in nursing students. The Transactional model of stress construes stress in these different ways and is frequently used to understand sources of stress, coping and stress responses. Limited research has attempted to measure sources of distress and eustress or sources that can potentially enhance performance and well-being. A volunteer sample of final year nursing students (n = 120) was surveyed in the United Kingdom in 2007. The questionnaire measured sources of stress and measures of psychological well-being were taken to test construct validity. This was tested through an exploratory factor analysis. This reduced the questionnaire from 49 to 29 items and suggested three factors: learning and teaching, placement related and course organization; second, it was analysed by testing the assumptions of the Transactional model, the model on which the questionnaire was based. In line with the assumptions of the model, measures of distress related to adverse well-being, and measures of eustress related to healthier well-being responses. The test–retest reliability estimate was 0.8. While certain programme issues were associated with distress, placement-related experiences were the most important source of eustress.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

The Essential Role of Human Databases for Learning in and Validation of Affectively Competent Agents.

This output is an invited and refereed chapter in the second of the two book length outputs resulting from the EU HUMAINE grant and follow-on grants. The book is in the OUP Affective Science Series and is intended to provide a theoretically oriented state of the art model for those working in the area of affective computing. Each chapter provides a synthesis of a specific area and presents new data/findings/approaches developed by the author(s) which take the area further. This chapter is in the section on ‘Approaches to developing expression corpora and databases.’ The chapter provides a critical synthesis of the issues involved in databases for affective computing and introduces the SEMAINE SAL Database, developed as an integral part of the EU SEMAINE Project (The Sensitive Agent Project 2008-2011) which is an interdisciplinary project. The project aimed to develop a computer interface that would allow a human to interact with an artificial agent in an emotional manner.

Cross-validation of geo-electrical and hydrogeological models to evaluate seawater intrusion in coastal aquifers

The validation of variable-density flow models simulating seawater intrusion in coastal aquifers requires information about concentration distribution in groundwater. Electrical resistivity tomography (ERT) provides relevant data for this purpose. However, inverse modeling is not accurate because of the non-uniqueness of solutions. Such difficulties in evaluating seawater intrusion can be overcome by coupling geophysical data and groundwater modeling. First, the resistivity distribution obtained by inverse geo-electrical modeling is established. Second, a 3-D variable-density flow hydrogeological model is developed. Third, using Archie's Law, the electrical resistivity model deduced from salt concentration is compared to the formerly interpreted electrical model. Finally, aside from that usual comparison-validation, the theoretical geophysical response of concentrations simulated with the groundwater model can be compared to field-measured resistivity data. This constitutes a cross-validation of both the inverse geo-electrical model and the groundwater model.
[Comte, J.-C., and O. Banton (2007), Cross-validation of geo-electrical and hydrogeological models to evaluate seawater intrusion in coastal aquifers, Geophys. Res. Lett., 34, L10402, doi:10.1029/2007GL029981.]

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.

Validation of fixed speed induction generator models for inertial response using wind farm measurements

This paper outlines the use of phasor measurement unit (PMU) records to validate models of fixed speed induction generator (FSIG)-based wind farms during frequency transients. Wind turbine manufacturers usually create their own proprietary models which they can supply to power system utilities for stability studies, subject to confidentiality agreements. However, it is desirable to confirm the accuracy of supplied models with measurements from the particular installation, in order to assess their validity under real field conditions. This is prudent due to possible changes in control algorithms and design retrofits, not accurately reflected or omitted in the supplied model. One important aspect of such models, especially for smaller power systems with limited inertia, is their accuracy during system frequency transients. This paper, therefore, assesses the accuracy of FSIG models with regard to frequency stability, and hence validates a subset of the model dynamics. Such models can then be used with confidence to assess wider system stability implications. The measured and simulated response of a wind farm using doubly fed induction generator (DFIG) technology is also assessed.

Implementation of Menter's Transition Model on an Isolated Natural Laminar Flow Nacelle

This study evaluates the implementation of Menter's gamma-Re-theta Transition Model within the CFX12 solver for turbulent transition prediction on a natural laminar flow nacelle. Some challenges associated with this type of modeling have been identified. The computational fluid dynamics transitional flow simulation results are presented for a series of cruise cases with freestream Mach numbers ranging from 0.8 to 0.88, angles of attack from 2 to 0 degrees, and mass flow ratios from 0.60 to 0.75. These were validated with a series of wind-tunnel tests on the nacelle by comparing the predicted and experimental surface pressure distributions and transition locations. A selection of the validation cases are presented in this paper. In all cases, computational fluid dynamics simulations agreed reasonably well with the experiments. The results indicate that Menter's gamma-Re-theta Transition Model is capable of predicting laminar boundary-layer transition to turbulence on a nacelle. Nonetheless, some limitations exist in both the Menter's gamma-Re-theta Transition Model and in the implementation of the computational fluid dynamics model. The implementation of a more comprehensive experimental correlation in Menter's gamma-Re-theta Transition Model, preferably the ones from nacelle experiments, including the effects of compressibility and streamline curvature, is necessary for an accurate transitional flow simulation on a nacelle. In addition, improvements to the computational fluid dynamics model are also suggested, including the consideration of varying distributed surface roughness and an appropriate empirical correction derived from nacelle experimental transition location data.

Development of a risk model for adverse drug events in the elderly

The aim of this study was to develop a predictive model for adverse drug events (ADEs) in elderly patients. Socio-demographic and medical data were collected from chart reviews, computerised information and a patient interview, for a population of 929 elderly patients (aged greater than or equal to 65 years) whose admission to the Waveney/B raid Valley Hospital in Northern Ireland was not scheduled. A further 204 patients formed a validation group. An ADE score was assigned to each patient using a modified Naranjo algorithm scoring system. The ADE scores ranged from 0 to 8. For the purposes of developing a risk model, scores of 4 or more were considered to constitute a high risk of an ADE.