Big hART at John Northcott Estate: Community, Health and the Arts

This paper considers the work of Big hART, a social impact of the Arts company, in their residency entitled Northcott Narratives over three and half years at John Northcott Estate, a public housing estate in Sydney. During this time Big hART used arts practice to engage tenants, strengthen their creative dispositions, and build relationships between tenants and a range of different communities. Northcott Narratives used a variety of multi-modal forms with tenants to inquire into, and then express ideas in relation to issues that confront them. These ideas are presented as social policy recommendations. The power and benefits of this form of arts practice along with the tensions and challenges are revealed. The paper adds to the developing discourse in relation to community cultural development

The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model

Features extraction of geometry defect monitoring in cylindrical piping

This study aims to investigate the features of wave in the single Polyvinyl Chloride (PVC) cylindrical pipeline. This project involved the monitoring process of without defect and defect single pipeline. The experiment process involved the simple geometry defect including the horizontal and vertical cracks. The sensor used in this project is the Piezoelectric Wafer Active Sensor (PWAS) which the sensors use to detect the lamb wave. As a part of non-destructive test process, this crack detection process involving the process of analyzing wave obtained from the sensor. The size of defect is the manipulated variables use in this project which including the size of crack start from 1 cm to 5 cm. However, this project is only concentrate only on the semicircular part of the cross-section of the pipeline. Because of that, all the six sensors used in this project attached only in the distance 36º between each sensor. This project indicates the different pattern of wave for each sensor as each sensor has own angle from the midpoint of the circular cross-section. The different pattern produced by each sensor in the present of crack and for normal pipe was analyzed for the understanding the effect of the defect in the normal single PVC pipeline. The data produced by each sensor then was filter to get the best accurate and consistent data. Filter data next to analyzed followed by the features choice for time domain including the maximum peak, minimum peak, velocity of the wave and maximum amplitude versus angle of the sensors for both horizontal and vertical defect. The results of each features is discussed graphically in results and discussion chapter