School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau

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https://hdl.handle.net/10292/553
AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries. Current research interests include:
  • Artificial Intelligence; Astronomy and Space Research;
  • Biomedical Technologies;
  • Computer Engineering; Computer Vision; Construction Management;
  • Data Science;
  • Health Informatics and eHealth;
  • Industrial Optimisation, Modelling & Control;
  • Information Security;
  • Mathematical Sciences Research; Materials & Manufacturing Technologies;
  • Networking, Instrumentation and Telecommunications;
  • Parallel and Distributed Systems; Power and Energy Engineering;
  • Software Engineering; Signal Processing; STEM Education;
  • Wireless Engineering;

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Browsing School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau by Subject "0204 Condensed Matter Physics"

Now showing 1 - 5 of 5
  • Item
    A Geometric Approach to Textual Augmented Data Filtering
    (IOP Publishing, 2024-09-09) Feng, SJH; Lai, EMK; Li, W
    Data augmentation is necessary if the amount of training data is insufficient for supervised learning. For natural language processing tasks, obtaining good quality augmented data is not easy. This paper introduces GATFilter, a novel method for filtering out inappropriate augmented textual data for text classification (TC). Utilizing geometric concepts, more specifically the principle component and convex hull analyses, this method adeptly preserves the semantic integrity of words within augmented texts. GATFilter is versatile and applicable across various types of textual augmentation methods. Experiments using several datasets and augmentation strategies showed that classifiers trained with GATFilter-filtered augmented data sets showed improvements in key performance metrics, including accuracy, precision, recall, and F1 score. The method’s efficacy is notably influenced by the quality of the underlying augmentation techniques, indicating its potential to complement and refine various text augmentation strategies. Furthermore, our analysis showed that GATFilter is particularly able to amplify the effectiveness of methods that generate good quality augmented data. GATFilter is openly available online on Github1, and as a Python package2
  • Item
    Additive 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 Alan
    Superconducting 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.
  • Item
    Anomaly Detection in Text Data Sets Using Character-Level Representation
    (Institute of Physics (IoP), 2021-04-28) Mohaghegh, Mahsa; Abdurakhmanov, Amantay
    This 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.
  • Item
    Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks
    (Institute of Physics (IoP), 2021-04-01) Mohaghegh, Mahsa; Ash, Payne
    The 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.
  • Item
    Heat Exchanger Based on Paraffin/Expanded Graphite Composites for Breathing Air Cooling in Fire
    (IOP Publishing, 2021-12-08) Lv, Y; Xiao, J; Huang, Y; Jiang, X; Zhu, Y
    The enormous amount of heat in fires can push inhalation temperature to ~500 K, which is fatal to the civilians. However, conventional rescue respirators are unable to control the breathing air temperature. In this work, we utilized paraffin/expanded graphite (EG) composites to construct a heat exchanger for breathing air cooling. The material itself can be used as the mechanical support, the heat spreader and the heat absorber at the same time. The composites of 0~35 wt% EG were prepared and characterized. The results showed the paraffin was uniformly absorbed in the porous structures of EG. And the paraffin/EG composite with 25 wt% EG has better performance both in simulation and experiment. The heat exchanger constructed by this composite shows good cooling efficiency by cooling the inlet air from 500 K to a breathable 313 K and sustaining for more than 20 minutes.
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