Faculty of Design and Creative Technologies (Te Ara Auaha)

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https://hdl.handle.net/10292/10758
The Faculty of Design and Creative Technologies - Te Ara Auaha is comprised of four schools: The School of Future Environments - Huri Te Ao, the School of Art and Design - Te Kura Toi a Hoahoa, the School of Communication Studies - Te Kura Whakapāho and the School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau. 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"

Now showing 1 - 7 of 7
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    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
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    A Highly Stretchable Strain-based Sensing Sheet for the Integrated Structural Health Monitoring
    (IOP Publishing, 2024-06-28) Zhang;, Hui; Beskhyroun, Sherif
    In this study, a flexible strain sensing system that can be applied to full-scale reinforced concrete frame structures is presented. In order to fulfil the criteria for strain detection that are posed by various structural components, the flexible strain gauge is offered in two distinct configurations: one full bridge and one double half bridge. A strain configuration selector is built on the basis of this information. The selector is designed to enable the system to flexibly switch strain modes for measuring axial or bending strain without adjusting the installation location of strain sensors. The first section of this study focuses mostly on elaborating on the methodology behind the development of a flexible strain system. This method was primarily designed with the aim of detecting the abnormalities in the strain field that are brought on by structural damage in order to accomplish the goal of local detection. The creation of a strain configuration selector also enables the conversion between two different strain measures whenever it is necessary without requiring the sensor installation to be moved to a new position, which helps to significantly reduce the amount of cost associated with sensor deployment. The performance of the flexible strain sensing system as well as its sensitivity were evaluated by doing the cyclic load testing on a full-scale RC frame. Both half-bridge and full-bridge strain gauges are installed in the critical components, such as beams and columns. In addition, 14 linear variable displacement transducers (LVDTS) were placed on the RC frame in order to monitor variations in displacement and deformation. The findings of the experiments indicate that the flexible strain sensor exhibits a high degree of sensitivity, and it is therefore suitable for integration into a structural health monitoring (SHM) system for the purpose of tracing the strain caused by localised structural damage. Additionally, it is able to monitor the strain trend on the complete scale of the frame model. In future work, the flexible strain system will be modified and enhanced by using wireless technology for data transmission in order to build a wirelessly integrated structural health monitoring (SHM) system.
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    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.
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    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.
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    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.
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    Data-Driven Prediction of Indoor Airflow Distribution in Naturally Ventilated Residential Buildings Using Combined CFD Simulation and Machine Learning (ML) Approach
    (SAGE Publications, 2024-01-10) Quang, Tran Van; Doan, Dat; Phuong, Nguyen Lu; Yun, Geun Young
    Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.
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    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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