Distributed classifier migration in XCS for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

A dual-memory permanent magnet brushless machine for automotive integrated starter-generator application

This paper presents a dual-memory permanent magnet brushless machine for automotive integrated starter-generator (ISG) application. The key is that the proposed machine adopts two kinds of PM materials, namely NdFeB and AlNiCo for hybrid excitations. Due to the non-linear characteristic of demagnetization curve, AlNiCo can be regulated to operate at different magnetization levels via a magnetizing winding. With this distinct merit, AlNiCo can provide the assistance for online tuning the air-gap flux density. Firstly, the configuration of proposed machine is presented. Secondly, the finite element method (FEM) is applied for the field calculation and performance verification. Finally, both simulation and experimental results confirm that the proposed machine is very suitable for the ISG application. © 2012 IEEE.published_or_final_versio

A magnetless axial-flux machine for range-extended electric vehicle

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Classification of EEG signals using a genetic-based machine learning classifier

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

An efficient offshore wind-wave hybrid generation system using direct-drive multitoothed rotating and linear machines

This paper presents an offshore wind-wave hybrid generation (WWHG) system, which can efficiently harness the offshore wind and wave energy. The key is to use the multitoothed doubly-salient permanent-magnet (MDSPM) machines for serving the rotating generator and the linear generator. Different from the traditional wind or wave generation system, this WWHG system integrates the wind generation part and wave generation part together to directly harness the wind and wave energy without gear box. The system configuration and machine design are analyzed and discussed in detail. Also, the finite-element method is performed to verify the validity of the proposed two machine design. The results tell that the system has the high reliability and can be upgraded to the suitable size for offshore hybrid-source energy conversion in practical application. © 2014 IEEE.published_or_final_versio

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg

Melt conditioning by advanced shear technology (MCAST) for refining solidification microstructures

MCAST (melt conditioning by advanced shear technology) is a novel processing technology developed recently by BCAST at Brunel University for conditioning liquid metal prior to solidification processing. The MCAST process uses a twin screw mechanism to impose a high shear rate and a high intensity of turbulence to the liquid metal, so that the conditioned liquid metal has uniform temperature, uniform chemical composition and well-dispersed and completely wetted oxide particles with a fine size and a narrow size distribution. The microstructural refinement is achieved through an enhanced heterogeneous nucleation rate and an increased nuclei survival rate during the subsequent solidification processing. In this paper we present the MCAST process and its applications for microstructural refinement in both shape casting and continuous casting of light alloys

A new magnetless flux-reversal HTS machine for direct-drive application

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Excitation Spectra And Hard-core Thermodynamics Of Bosonic Atoms In Optical Superlattices

A generalized double-well-basis coupled representation is proposed to investigate excitation spectra and thermodynamics of bosonic atoms in double-well optical superlattices. In the hard-core limit and with a filling factor of one, excitations describing the creation of pairs of a doubly occupied state and a simultaneous empty state, and those from a symmetric singly occupied state to an antisymmetric state are carefully analyzed and their excitation spectra are calculated within mean-field theory. Based on the hard-core statistics, the equilibrium properties such as heat capacity and particle populations are studied in detail. The cases with other filling factors are also briefly discussed.published_or_final_versio