Detection and Analysis of Molten Aluminium Cleanliness Using a Pulsed Ultrasound System

This document presents the development of a solution for analysis and detection of molten metal quality deviations. The data is generated by an MV20/20, an ultrasound sensor that detects inclusions - molten metal defects that affect the quality of the product. The data is then labelled by assessing the sample using metallography. The analysis provides the sample outcome and dominant inclusion. The business objectives for the project include the real-time classification of anomalous events by means of a supervised classifier for the metal quality outcome, and a classifier for the inclusion type responsible for low quality. The adopted methodology involves descriptive, diagnostic and predictive analytics. Once the data is statistically profiled, it is standardised and scaled to unit variance in order to compensate for different units in the descriptors. Principal components analysis is applied as a dimensionality reduction technique, and it is found that the first three components account for 99.6% of the variance of the dataset. In order for the system to have predictive ability, two modelling approaches are considered, namely Response Surface Methodology and supervised machine learning. Supervised machine learning is preferred as it offers more flexibility than a polynomial approximator, and it is more accurate. Four classifiers are built, namely logistic regression, support vector machine, multi-layer perceptron and a radial basis function network. The hyperparameters are tuned using 10- fold repeated cross-validation. The multi-layer perceptron offers the best performance in all cases. For determining the quality outcome of a cast (passed or failed), all the models perform according to business targets for accuracy, precision, sensitivity and specificity. For the inclusion type classification, the multi-layer perceptron performs within 5% of the target metrics. In order to optimise the model, a grid search is performed for optimal parameter tuning. The results offer negligible improvement, which indicates that the model has reached a global maximum in the parameter optimisation in the hyperspace. It is noted that the source of variance in the inclusion type data respondent is attributed to operator error during labelling of the dataset, among several other sources of variance. It is therefore recommended that a Gage R&R be performed in order to identify sources of variation, among other improvement recommendations. From a research perspective, a vision system is recommended for assessing metal colour, texture and other visual properties in order to provide more insights. Another possible research extension recommended is the use of Fourier Transform Infrared Spectroscopy in determining signatures of the clean metal and different inclusions for detection. The project is regarded as a success, as the business metrics are met by the solution

Modeling and Forecasting CAT and HDD Indices For Weather Derivative Pricing

In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein–Uhlenbeck temperature process, with seasonality in the level and volatility and time-varying speed of mean reversion. We forecast up to 2 months ahead out of sample daily temperatures, and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in the Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods, proposed in prior studies, in most cases. We find that wavelet networks can model the temperature process very well and consequently they constitute an accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Cooling and Heating Degree Day indices

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

The Shallow and the Deep:A biased introduction to neural networks and old school machine learning

The Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon.Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility