Finite-Element Time-Step Coupled Generator, Load, AVR and Brushless Exciter Modelling

This paper presented results from a details and comprehensive simulation using finite element method of the practical operation of an electrical machine. The results it displayed have been used in practice to design more efficient equipment.

Power dissipation in nanoscale conductors: classical, semi-classical and quantum dynamics

Modelling Joule heating is a difficult problem because of the need to introduce correct correlations between the motions of the ions and the electrons. In this paper we analyse three different models of current induced heating (a purely classical model, a fully quantum model and a hybrid model in which the electrons are treated quantum mechanically and the atoms are treated classically). We find that all three models allow for both heating and cooling processes in the presence of a current, and furthermore the purely classical and purely quantum models show remarkable agreement in the limit of high biases. However, the hybrid model in the Ehrenfest approximation tends to suppress heating. Analysis of the equations of motion reveals that this is a consequence of two things: the electrons are being treated as a continuous fluid and the atoms cannot undergo quantum fluctuations. A means for correcting this is suggested.

Single-ionization of helium at Ti:sapphire wavelengths: rates and scaling laws

We present a numerical and theoretical study of intense-field single-electron ionization of helium at 390 nm and 780 nm. Accurate ionization rates (over an intensity range of (0.175-34) X10^14 W/ cm^2 at 390 nm, and (0.275 - 14.4) X 10^14 W /cm^2 at 780 nm) are obtained from full-dimensionality integrations of the time-dependent helium-laser Schroedinger equation. We show that the power law of lowest order perturbation theory, modified with a ponderomotive-shifted ionization potential, is capable of modelling the ionization rates over an intensity range that extends up to two orders of magnitude higher than that applicable to perturbation theory alone. Writing the modified perturbation theory in terms of scaled wavelength and intensity variables, we obtain to first approximation a single ionization law for both the 390 nm and 780 nm cases. To model the data in the high intensity limit as well as in the low, a new function is introduced for the rate. This function has, in part, a resemblance to that derived from tunnelling theory but, importantly, retains the correct frequency-dependence and scaling behaviour derived from the perturbative-like models at lower intensities. Comparison with the predictions of classical ADK tunnelling theory confirms that ADK performs poorly in the frequency and intensity domain treated here.

Improved power-saving medium access protocol for IEEE 802.11e QoS-enabled wireless networks

The performance of a new pointer-based medium-access control protocol that was designed to significantly improve the energy efficiency of user terminals in quality-of-service-enabled wireless local area networks was analysed. The new protocol, pointer-controlled slot allocation and resynchronisation protocol (PCSARe), is based on the hybrid coordination function-controlled channel access mode of the IEEE 802.11e standard. PCSARe reduces energy consumption by removing the need for power-saving stations to remain awake for channel listening. Discrete event network simulations were performed to compare the performance of PCSARe with the non-automatic power save delivery (APSD) and scheduled-APSD power-saving modes of IEEE 802.11e. The simulation results show a demonstrable improvement in energy efficiency without significant reduction in performance when using PCSARe. For a wireless network consisting of an access point and eight stations in power-saving mode, the energy saving was up to 39% when using PCSARe instead of IEEE 802.11e non-APSD. The results also show that PCSARe offers significantly reduced uplink access delay over IEEE 802.11e non-APSD, while modestly improving the uplink throughput. Furthermore, although both had the same energy consumption, PCSARe gave a 25% reduction in downlink access delay compared with IEEE 802.11e S-APSD.

Plasma power measurement and hysteresis in the E-H transition of a rf inductively coupled plasma system

An experimental investigation of the argon plasma behavior near the E-H transition in an inductively coupled Gaseous Electronics Conference reference cell is reported. Electron density and temperature, ion density, argon metastable density, and optical emission measurements have been made as function of input power and gas pressure. When plotted versus plasma power, applied power corrected for coil and hardware losses, no hysteresis is observed in the measured plasma parameter dependence at the E-H mode transition. This suggests that hysteresis in the E-H mode transition is due to ignoring inherent power loss, primarily in the matching system.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Neural modelling, control and optimisation of an industrial grinding process

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. (c) 2004 Elsevier Ltd. All rights reserved.

Analysis of multiple aspects of a repeated signal: power and rate of rapping during shell fights in hermit crabs

Repeated activities used by animals during contests are assumed to act as signals advertising the quality of the sender. However, their exact functions are not well understood and observations fit only a limited set of the predictions made by models of signaling systems. Experimental studies of contest behavior tend to focus on analysis of the rate of signaling, but individual performances may also vary in magnitude. Both of these features can vary between outcomes and within contests. We examined changes in the rate and power of shell rapping during shell fights in hermit crabs. We show that both rate and power decline during the course of the encounter and that the duration of pauses between bouts of shell rapping increases with an index of the total effort put into each bout. This supports the idea that the vigor of shell rapping is regulated by fatigue and could therefore act as a signal of stamina. By examining different interacting components of this complex activity, we gain greater insight into its function than would be achieved by investigating a single aspect in isolation.

TDM modelling and evaluation of different domain transforms for LSI

Latent semantic indexing (LSI) is a popular technique used in information retrieval (IR) applications. This paper presents a novel evaluation strategy based on the use of image processing tools. The authors evaluate the use of the discrete cosine transform (DCT) and Cohen Daubechies Feauveau 9/7 (CDF 9/7) wavelet transform as a pre-processing step for the singular value decomposition (SVD) step of the LSI system. In addition, the effect of different threshold types on the search results is examined. The results show that accuracy can be increased by applying both transforms as a pre-processing step, with better performance for the hard-threshold function. The choice of the best threshold value is a key factor in the transform process. This paper also describes the most effective structure for the database to facilitate efficient searching in the LSI system.

PLS modelling and fault detection on the Tennessee Eastman benchmark

This paper describes the application of multivariate regression techniques to the Tennessee Eastman benchmark process for modelling and fault detection. Two methods are applied : linear partial least squares, and a nonlinear variant of this procedure using a radial basis function inner relation. The performance of the RBF networks is enhanced through the use of a recently developed training algorithm which uses quasi-Newton optimization to ensure an efficient and parsimonious network; details of this algorithm can be found in this paper. The PLS and PLS/RBF methods are then used to create on-line inferential models of delayed process measurements. As these measurements relate to the final product composition, these models suggest that on-line statistical quality control analysis should be possible for this plant. The generation of `soft sensors' for these measurements has the further effect of introducing a redundant element into the system, redundancy which can then be used to generate a fault detection and isolation scheme for these sensors. This is achieved by arranging the sensors and models in a manner comparable to the dedicated estimator scheme of Clarke et al. 1975, IEEE Trans. Pero. Elect. Sys., AES-14R, 465-473. The effectiveness of this scheme is demonstrated on a series of simulated sensor and process faults, with full detection and isolation shown to be possible for sensor malfunctions, and detection feasible in the case of process faults. Suggestions for enhancing the diagnostic capacity in the latter case are covered towards the end of the paper.

Adaptive data echo cancellation using cost function adaptation

For a digital echo canceller it is desirable to reduce the adaptation time, during which the transmission of useful data is not possible. LMS is a non-optimal algorithm in this case as the signals involved are statistically non-Gaussian. Walach and Widrow (IEEE Trans. Inform. Theory 30 (2) (March 1984) 275-283) investigated the use of a power of 4, while other research established algorithms with arbitrary integer (Pei and Tseng, IEEE J. Selected Areas Commun. 12(9)(December 1994) 1540-1547) or non-quadratic power (Shah and Cowan, IEE.Proc.-Vis. Image Signal Process. 142 (3) (June 1995) 187-191). This paper suggests that continuous and automatic, adaptation of the error exponent gives a more satisfactory result. The family of cost function adaptation (CFA) stochastic gradient algorithm proposed allows an increase in convergence rate and, an improvement of residual error. As special case the staircase CFA algorithm is first presented, then the smooth CFA is developed. Details of implementations are also discussed. Results of simulation are provided to show the properties of the proposed family of algorithms. (C) 2000 Elsevier Science B.V. All rights reserved.

The Effects of Anti-Hypertensive Drugs Evaluated Using Markov Modelling for Northern Ireland Chronic Kidney Disease Patients

The aim of this paper is to use Markov modelling to
investigate survival for particular types of kidney patients
in relation to their exposure to anti-hypertensive treatment
drugs. In order to monitor kidney function an intuitive three
point assessment is proposed through the collection of blood
samples in relation to Chronic Kidney Disease for Northern
Ireland patients. A five state Markov Model was devised
using specific transition probabilities for males and
females over all age groups. These transition probabilities
were then adjusted appropriately using relative risk scores
for the event death for different subgroups of patients. The
model was built using TreeAge software package in order to
explore the effects of anti-hypertensive drugs on patients.

Physical modelling of millimetre wave signal reflection from forward biased PIN diodes

This paper examines the DC power requirements of PIN diodes which, with suitable applied DC bias, have the potential to reflect or to permit transmission of millimetre wave energy through them by the process of inducing a semiconductor plasma layer in the i-region. The study is conducted using device level simulation of SOI and bulk PIN diodes and reflection modelling based on the Drude conduction model. We examined five diode lengths (60–140 µm) and seven diode thicknesses (4–100 µm). Simulation output for the diodes of varying thicknesses was subsequently used in reflection modelling to assess their performance for 100 GHz operation. It is shown that substantially high DC input power is required in order to induce near total reflection in SOI PIN diodes at 100 GHz. Thinner devices consume less DC power, but reflect less incident radiation for given input power. SOI diodes are shown to have improved carrier confinement compared with bulk diodes.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.