https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 Application of a 2D segregation-dispersion model to describe binary and multi-component size classification in a reflux classifier https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34792 3 in both cases. For the binary mixture, particle species of size 200 and 300 µm were used, while for the multicomponent mixture eight particle species with sizes between 49 and 421 µm were selected. The partition curves obtained from the model predictions were successfully validated with published experimental results. The separation performance of the RC was characterised by analysing how the imperfection and the d50 values changed with the process variables. Furthermore, the simulation data were used for the first time to demonstrate the concentration distribution of the individual solid particle species in the fluidization and inclined sections of the RC. This study showed that the particle species with sizes closer to the d50 values had a larger presence in both the vertical and inclined sections of the RC. Thus, the total concentration inside the RC mainly consisted of those particle species.]]> Wed 15 Feb 2023 09:24:51 AEDT ]]> Automated identification of coronary artery disease from short-term 12 lead electrocardiogram signals by using wavelet packet decompostion and common spatial pattern techniques https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33891 four levels of wavelet packet decomposition (WPD) to obtain various coefficients. Using the fourth-level coefficients obtained for each lead ECG signal beat, new 2s. ECG signal beats are reconstructed. Later, the reconstructed signals are split into two-fold data sets, in which one set is used for acquiring common spatial pattern (CSP) filter and the other for obtaining features vector (vice versa). The obtained features are one by one fed into k-nearest neighbors (KNN) classifier for automated classification. The proposed system yielded maximum average classification results of 99.65% accuracy, 99.64% sensitivity and 99.7% specificity using 10 features. Our proposed algorithm is highly efficient and can be used by the clinicians as an aiding system in their CAD diagnosis, thus, assisting in faster treatment and avoiding the progression of CAD condition.]]> Tue 22 Jan 2019 14:22:16 AEDT ]]>