TUT DIGITAL OPEN REPOSITORY

Recent Submissions

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    The influence of learning and study strategies inventory on the success of engineering students at a South African University of Technology.
    (Taylor and Francis, 2021-05-03) Van Wyk, Barend J.; Mason, Henry D.
    This article reports on a study investigating the relationship between university students’ self-reported use and application of learning and study strategies and student success indicators (timeframe and student type, namely low performing, average-performing, or high-performing). Participants were 1 439 engineering students enrolled for academic studies at a South African University of Technology (UoT). Data were collected using the Learning and Study Strategies Inventory (LASSI) and academic performance data, obtained via the university’s student management information system. The LASSI provides diagnostic information about students’ self-perception regarding their study skills and learning orientations, assists educators in designing interventions for students to improve their skills, and aids in predicting academic achievement. Researchers have questioned the long-term correlation between LASSI scores and academic performance. Our results confirm that time spent in the tertiary environment and student type (low-, average- or high-performing) should be considered when using LASSI as a diagnostic tool. The usefulness of using the Credit Accumulation Rate (CAR), a measure that combines time spent in the tertiary environment and academic performance, is introduced and explored. Some novel trends emerged by investigating the relationships between CAR and LASSI scores. Based on our results, we make recommendations for identifying students at risk for academic failure and propose avenues for further research.
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    Active disturbance rejection control design for a haptic machine interface platform.
    (Advances in Science, Technology and Engineering Systems, 2021-01-24) Nahri, Syeda Nadiah Fatima; Du, Shengzhi; Van Wyk, Barend J.
    This paper proposes an active disturbance rejection control (ADRC) design for a haptic display platform structure. The motivation for the following scheme originates from the shortcomings faced by classical proportional integral derivative (PID) controllers in control theory. The ADRC is an unconventional model-independent approach, acknowledged as an effective controller in the existence of total plant uncertainties, and these uncertainties are inclusive of the total disturbances and unknown dynamics of the plant. The design and simulation for ADRC are established in MATLAB/ Simulink. The concerned electro-mechanical platform consists of dual ball screw driving system and DC motors. This overall physical system constitutes the haptic interface. Modelling of the two- dimensional physical platform is also explained in this article. Designing of ADRC controller and the human-machine interface (HMI) is followed by their integration, in order to obtain simulation results, thus proving the practicality and validity of the overall system. The results of the proposed controller are compared with the Proportional Integral (PI) controller, which suggests that the ADRC controller performs better as compared to the conventional PI controller.
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    Leveraging graph cut’s energy function for context aware facial recognition in indoor environments.
    (Tech Science Press, 2021-02-14) Oyebode, Kazeem; Du, Shengzhi; Van Wyk, Barend J.
    Context-aware facial recognition regards the recognition of faces in association with their respective environments. This concept is useful for the domestic robot which interacts with humans when performing specific functions in indoor environments. Deep learning models have been relevant in solving facial and place recognition challenges; however, they require the procurement of training images for optimal performance. Pre-trained models have also been offered to reduce training time significantly. Regardless, for classification tasks, custom data must be acquired to ensure that learning models are developed from other pre-trained models. This paper proposes a place recognition model that is inspired by the graph cut energy function, which is specifically designed for image segmentation. Common objects in the considered environment are identified and thereafter they are passed over to a graph cut inspired model for indoor environment classification. Additionally, faces in the considered environment are extracted and recognised. Finally, the developed model can recognise a face together with its environment. The strength of the proposed model lies in its ability to classify indoor environments without the usual training process(es). This approach differs from what is obtained in traditional deep learning models. The classification capability of the developed model was compared to state-of-the-art models and exhibited promising outcomes
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    Sustainable electric vehicle transportation.
    (MDPI, 2021-11-09) Kene, Raymond; Olwal, Thomas; Van Wyk, Barend J.
    The future direction of electric vehicle (EV) transportation in relation to the energy demand for charging EVs needs a more sustainable roadmap, compared to the current reliance on the centralised electricity grid system. It is common knowledge that the current state of electricity grids in the biggest economies of the world today suffer a perennial problem of power losses; and were not designed for the uptake and integration of the growing number of large-scale EV charging power demands from the grids. To promote sustainable EV transportation, this study aims to review the current state of research and development around this field. This study is significant to the effect that it accomplishes four major objectives. (1) First, the implication of large-scale EV integration to the electricity grid is assessed by looking at the impact on the distribution network. (2) Secondly, it provides energy management strategies for optimizing plug-in EVs load demand on the electricity distribution network. (3) It provides a clear direction and an overview on sustainable EV charging infrastructure, which is highlighted as one of the key factors that enables the promotion and sustainability of the EV market and transportation sector, re-engineered to support the United Nations Climate Change Agenda. Finally, a conclusion is made with some policy recommendations provided for the promotion of the electric vehicle market and widespread adoption in any economy of the world.
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    The relationship between resilience and student success among a sample of South African engineering students.
    (Taylor and Francis, 2022-03-21) Van Wyk, Mariza; Mason, Henry D.; Van Wyk, Barend J.; Phillips, Tyler K.; Van der Walt, Etienne
    There is a growing need to understand the role of non-cognitive factors in relation to university students’ academic performance and successful adaptation to university life. This study investigated the relationship between the non-cognitive factor “resilience” and student success (academic performance, turnover intentions, brain-body optimisation) among South African university students. This cross-sectional correlational study analysed data from 360 first-year students. Self-report data were collected using the Neurozone Assessment, comprising two subscales: the Brain Performance Diagnostic and the Resilience Index. Turnover intentions were assessed using the Neurozone Assessment, and students’ academic marks were obtained via the university’s management information system. Correlational analyses revealed significant positive relationships between the Stress Mastery and Positive Affect components of resilience and academic performance, a significant negative relationship between the Positive Affect component of resilience and turnover intentions, as well as significant positive relationships between brain-body optimisation and all three components of resilience (Stress Mastery, Positive Affect, and Early-Life Stability). Through regression analyses, we identified the behavioural predictors that underlie resilience and outline a framework for implementing behavioural interventions to enhance resilience and increase student success. Resilience is an important non-cognitive determinant of student success in first- year students.