Faculty of Design and Creative Technologies (Te Ara Auaha)
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The Faculty of Design and Creative Technologies (Te Ara Auaha) is comprised of four school; Colab, the School of Art and Design, the School of Communication Studies and the School of Engineering, Computer and Mathematical Sciences. It also has Institutes, Centres and Labs across the Arts and Sciences in a mix that blends the traditional and the new, praxis and theory.
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Browsing Faculty of Design and Creative Technologies (Te Ara Auaha) by Subject "0204 Condensed Matter Physics"
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- ItemAdditive Manufacturing Materials for Structural Optimisation and Cooling Enhancement of Superconducting Motors in Cryo-Electric Aircraft(IOP Publishing, 2023-08-18) Lumsden, Grant; Ludbrook, Bart; Rogers Rehn, Nic; Solis Fernandez, Fernando; Davies, Mike; Chamritski, Vadim; Singamneni, Sarat; Badcock, Rodney AlanSuperconducting electric motors offer the potential for low weight and high power in applications such as electric aircraft and high speed marine transport. Combined with renewably-sourced cryogenic fuels and advanced fuel cells they offer a path to zero-carbon mass transport. The proposed architectures of these extreme machines, operating at temperatures around 20 K–50 K and employing very high alternating magnetic fields, require materials for the stator that are not electrically conducting and at the same time have good cryogenic structural performance. Additively manufactured (AM) materials can play a key role in these designs, and a collaboration between the Robinson Research Institute and Auckland University of Technology is studying the performance of a range of composite polymers in superconducting machine applications. There are significant challenges to be met, including understanding the effect of the build process on material properties at low temperatures, and also the effect of formulation changes on thermal properties. AM metals can be employed in the rotor components, where the magnetic field fluctuations are very small for our synchronous designs. In this usage case, we can achieve dramatic reductions in the weight of the rotor assembly by minimising the number of joints and facilitating the design of multi-functional components in our helium cooled, vacuum cryostat architecture. Novel design solutions have been developed for several key components in our prototype machines and these are discussed, along with cryogenic testing results for selected AM polymers and composites.
- ItemAnomaly Detection in Text Data Sets Using Character-Level Representation(Institute of Physics (IoP), 2021-04-28) Mohaghegh, Mahsa; Abdurakhmanov, AmantayThis paper proposes a character-level representation of unsupervised text data sets for anomaly detection problems. An empirical examination of the character-level text representation was conducted to demonstrate the ability to separate outlying and normal records using an ensemble of multiple classic numerical anomaly classifiers. Experimental results obtained on two different data sets confirmed the applicability of the developed unsupervised model to detect outlying instances in various real-world scenarios, providing the opportunity to quickly assess a large amount of textual data in terms of information consistency and conformity without knowledge of the data content itself.
- ItemAutomated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks(Institute of Physics (IoP), 2021-04-01) Mohaghegh, Mahsa; Ash, PayneThe identification of perpetrators, present in Child Sexual Abuse Imagery (CSAI), is a significant challenge due to the use of anonymisation techniques that mask their identities. Consequently, researchers have investigated the use of uncommon biometric identifiers such as knuckle patterns, palmprints and the dorsal side of the hand. This research proposes a Convolutional Neural Network (CNN) based, fully automated approach to biometric identification using dorsal hand images. The identification performance of three different CNN architectures, AlexNet, ResNet50 and ResNet152, is experimentally determined against two similar datasets, the 11k Hands and IITD dorsal hand databases. A transfer learning approach is used and the final output layers of the CNNs are modified to match the number of classes present in the datasets. The results showed that ResNet CNNs achieved identification accuracies greater than 99.9% on both datasets, whereas the AlexNet CNN achieved between 80.1% and 93.7%. These results demonstrate that it is feasible to use deep, off-the-shelf CNNs, such as ResNets, for automated biometric identification using dorsal hand images. This highlights the potential of using dorsal hand images to identify perpetrators of child sexual abuse from CSAI.