Stephen J. Weddell

Bio: Stephen J. Weddell is an academic researcher from University of Canterbury. The author has contributed to research in topics: Wavefront & Adaptive optics. The author has an hindex of 10, co-authored 53 publications receiving 270 citations.

Evolutionary multi-objective fault diagnosis of power transformers

Abstract: This paper introduces a two step algorithm for fault diagnosis of power transformers (2-ADOPT) using a binary version of the multi-objective particle swarm optimization (MOPSO) algorithm. Feature subset selection and ensemble classifier selection are implemented to improve the diagnosing accuracy for dissolved gas analysis (DGA) of power transformers. First, the proposed method selects the most effective features in a multi objective framework and the optimum number of features, simultaneously, which are used as inputs to train classifiers in the next step. The input features are composed of DGA performed on the oil of power transformers along with the various ratios of these gases. In the second step, the most accurate and diverse classifiers are selected to create a classifier ensemble. Finally, the outputs of selected classifiers are combined using the Dempster-Shafer combination rule in order to determine the actual faults of power transformers. In addition, the obtained results of the proposed method are compared to three other scenarios: 1) multi-objective ensemble classifier selection without any feature selection step which takes all the features to train classifiers and then applies MOPSO algorithm to find the best ensemble of classifiers, 2) a well-known classifier ensemble technique called random forests, and 3) another powerful decision tree ensemble which is called oblique random forests. The comparison results were favourable to the proposed method and showed the high reliability of this method for power transformers fault classification.

Atomic Scale Dynamics Drive Brain-like Avalanches in Percolating Nanostructured Networks

Abstract: Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here, we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons, leading to the generation of critical avalanches of signals. These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which self-tune to balanced states consistent with self-organized criticality. Our simulations allow visualization of the network states and detailed mechanisms of signal propagation.

Multi-objective ensemble forecasting with an application to power transformers

Abstract: In this paper we present an ensemble time series forecasting algorithm using evolutionary multi-objective optimization algorithms to predict dissolved gas contents in power transformers. In this method, the correlation between each individual dissolved gas and other transformers’ features such as temperature characteristics and loading history is first determined. Then, a non-linear principal component analysis (NLPCA) technique is applied to extract the most effective time series from the highly correlated features. Afterwards, the forecasting algorithms are trained using a cross validation technique. In addition, evolutionary multi-objective optimization algorithms are used to select the most accurate and diverse group of forecasting algorithms to construct an ensemble. Finally, the selected ensemble is examined to predict the value of the dissolved gases on the testing set. The results of one day, two day, three day, and four day ahead forecasting are presented which show higher accuracy and reliability of the proposed method compared with other statistical methods.

Reservoir Computing for Prediction of the Spatially-Variant Point Spread Function

Abstract: A new method is presented which provides prediction of the spatially variant point spread function for the restoration of astronomical images, distorted by atmospheric turbulence when viewed using ground-based telescopes. Our approach uses reservoir computing to firstly learn the spatio-temporal evolution of aberrations caused by turbulence, and secondly, predicts the space-varying point spread function (PSF) for application of widely-used deconvolution algorithms, resulting in the restoration of astronomical images. In this article, a reservoir-based, recurrent neural network is used to predict modal aberrations that comprise the spatially variant PSF over a wide field-of-view using a time-series ensemble from multiple reference beacons.

Classification of alcoholic EEG signals using wavelet scattering transform-based features.

Abstract: Following the research question and the relevant dataset, feature extraction is the most important component of machine learning and data science pipelines. The wavelet scattering transform (WST) is a recently developed knowledge-based feature extraction technique and is structurally like a convolutional neural network (CNN). It preserves information in high-frequency, is insensitive to signal deformations, and generates low variance features of real-valued signals generally required in classification tasks. With data from a publicly-available UCI database, we investigated the ability of WST-based features extracted from multichannel electroencephalogram (EEG) signals to discriminate 1.0-s EEG records of 20 male subjects with alcoholism and 20 male healthy subjects. Using record-wise 10-fold cross-validation, we found that WST-based features, inputted to a support vector machine (SVM) classifier, were able to correctly classify all alcoholic and normal EEG records. Similar performances were achieved with 1D CNN. In contrast, the highest independent-subject-wise mean 10-fold cross-validation performance was achieved with WST-based features fed to a linear discriminant (LDA) classifier. The results achieved with two 10-fold cross-validation approaches suggest that the WST together with a conventional classifier is an alternative to CNN for classification of alcoholic and normal EEGs. WST-based features from occipital and parietal regions were the most informative at discriminating between alcoholic and normal EEG records.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

The Analysis of Time Series: An Introduction

Abstract: The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

Statistical optics

Abstract: Course Description This is an advanced course in which we explore the field of Statistical Optics. Topics covered include such subjects as the statistical properties of natural (thermal) and laser light, spatial and temporal coherence, effects of partial coherence on optical imaging instruments, effects on imaging due to randomly inhomogeneous media, and a statistical treatment of the detection of light. Development of this more comprehensive model of the behavior of light draws upon the use of tools traditionally available to the electrical engineer, such as linear system theory and the theory of stochastic processes.

Stochastic Processes And Filtering Theory

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Linear Systems, Fourier Transforms and Optics

Abstract: (1979). Linear Systems, Fourier Transforms and Optics. Optica Acta: International Journal of Optics: Vol. 26, No. 7, pp. 836-836.