Department of Electrical Engineering Research Articles

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https://hdl.handle.net/20.500.14519/84

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Towards model-free pressure control in water distribution networks.
(MDPI, 2020-09-26) Mosetlhe, Thapelo C.; Hamam, Yskandar; Du, Shengzhi; Monacelli, Eric; Yusuff, Adedayo A.
Pressure control in water distribution networks (WDNs) is one of the interventions commonly employed to improve the reliability and sustainability of water supply. Various approaches have been proposed to solve the problem of pressure control. However, most schemes that have been proposed rely on the accuracy of a model in order to precisely control a real WDN. Therefore, any deviation between a model and real WDN parameters could render the results of control schemes useless. As a result, this work proposes the utilisation of the reinforcement learning (RL) technique to control nodes pressure in WDNs without solving the model. Quadratic approximation emulators of WDNs and RL agents are used in the proposed scheme. The effectiveness of the proposed scheme is tested on two WDNs networks and the results are compared with the conventional optimisation scheme that is commonly used for simulation cases. The results show that the proposed scheme is able to achieve the desired results when compared to the benchmark optimisation procedure. However, unlike the optimisation procedure, the proposed scheme achieved the results without the numerical solution of the WDNs. Therefore, this scheme could be used in situations where the model of a network is not well defined.
OMNIVIL—An autonomous mobile manipulator for flexible production.
(MDPI, 2020-12-17) Engemann, Heiko; Du, Shengzhi; Kallweit, Stephan; Cönen, Patrick; Dawar, Harshal
Flexible production is a key element in modern industrial manufacturing. Autonomous mobile manipulators can be used to execute various tasks: from logistics, to pick and place, or handling. Therefore, autonomous robotic systems can even increase the flexibility of existing production environments. However, the application of robotic systems is challenging due to their complexity and safety concerns. This paper addresses the design and implementation of the autonomous mobile manipulator OMNIVIL. A holonomic kinematic design provides high maneuverability and the implemented sensor setup with the underlying localization strategies are robust against typical static and dynamic uncertainties in industrial environments. For a safe and efficient human–robot collaboration (HRC), a novel workspace monitoring system (WMS) is developed to detect human co-workers and other objects in the workspace. The multilayer sensor setup and the parallel data analyzing capability provide superior accuracy and reliability. An intuitive zone-based navigation concept is implemented, based on the workspace monitoring system. Preventive behaviors are predefined for a conflict-free interaction with human co-workers. A workspace analyzing tool is implemented for adaptive manipulation, which significantly simplifies the determination of suitable platform positions for a manipulation task.
Autosynpose: Automatic generation of synthetic datasets for 6D object pose estimation.
(IOP Press, 2020-06-07) Engemann, Heiko; Du, Shengzhi; Kallweit, Stephan; Ning, Chuanfang; Anwar, Saqib
We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale- CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.
Severity classification of parkinson’s disease based on permutation-variable importance and persistent entropy.
(MDPI, 2021-02-19) Tong, Jigang; Zhang, Jiachen; Dong, Enzeng; Du, Shengzhi
Parkinson’s disease (PD) is a neurodegenerative disease that causes chronic and progressive motor dysfunction. As PD progresses, patients show different symptoms at different stages of the disease. The severity assessment is inefficient and subjective when it comes to artificial diagnosis. However, abnormal gait was contingent and the subject selection was limited. Therefore, few-shot learning based on small sample sets is critical to solving the problem of insufficient sample data in PD patients. Using datasets from PhysioNet, this paper presents a method based on permutation-variable importance (PVI) and persistent entropy of topological imprints and uses support vector machine (SVM) as a classifier to achieve the severity classification of PD patients. The method includes the following steps: (1) Take the data as gait cycles and calculate the gait characteristics of each cycle. (2) Use the random forest (RF) method to obtain the leading factors differentiating the gait of patients at different severity levels. (3) Use time-delay embedding to map the data into a topological space, and use the topological data analysis based on permutation homology to obtain the persistent entropy. (4) Use the Borderline-SMOTE (BSM) method to balance the sample data. (5) Use the SVM to classify the samples for the severity levels of PD. An accuracy of 98.08% was achieved by 10-fold cross-validation, so our method can be used as an effective means of computer-aided diagnosis of PD and has important practical value.
A study of cutaneous perception parameters for designing haptic symbols towards information transfer.
(MDPI, 2021-09-03) Nyasulu, Tawanda Denzel; Du, Shengzhi; Steyn, Nico; Dong, Enzeng
Vibrotactile displays can substitute for sensory channels of individuals experiencing temporary or permanent impairments in balance, vision, or hearing, and can enhance the user experience in professional or entertainment situations. This massive range of potential uses necessitates primary research on human vibrotactile perception. One leading aspect to consider when developing such displays is how to develop haptic patterns or symbols to represent a concept. In most settings, individual patterns are sorted as alphabets of haptic symbols which formulate tactons. Tactons are structured and perceivable tactile patterns (i.e., messages) that transfer information to users by employing the sense of touch. Hence, haptic patterns are critical when designing vibrotactile displays, as they not only affect the rate of information transfer but also determine the design of the displays (e.g., the number and the placement of tactors engaged) and how the information is encoded to achieve separability. Due to this significance, this paper presents an overview study on the cutaneous perception parameters (i.e., intensity, loci, frequency, duration, illusions, and combinations of these) for designing haptic symbols to identify mutual best-practices and knowledge gaps for future work. The study also provides developers from different scientific backgrounds with access to complex notions when engaging this specialized topic (i.e., the use of cutaneous perception parameters towards information transfer). Finally, it offers recommendations on defining which parameters to engage for a specific task or pattern.
Joint queue-perturbed and weakly coupled power control for wireless backbone networks.
(Sciendo, 2012-01-01) Olwal, Thomas Otieno; Djouani, Karim; Kogeda, Okuthe P.; Van Wyk, Barend Jacobus
Wireless Backbone Networks (WBNs) equipped with Multi-Radio Multi-Channel (MRMC) configurations do experience power control problems such as the inter-channel and co-channel interference, high energy consumption at multiple queues and unscalable network connectivity. Such network problems can be conveniently modelled using the theory of queue perturbation in the multiple queue systems and also as a weak coupling in a multiple channel wireless network. Consequently, this paper proposes a queue perturbation and weakly coupled based power control approach for WBNs. The ultimate objectives are to increase energy efficiency and the overall network capacity. In order to achieve this objective, a Markov chain model is first presented to describe the behavior of the steady state probability distribution of the queue energy and buffer states. The singular perturbation parameter is approximated from the coefficients of the Taylor series expansion of the probability distribution. The impact of such queue perturbations on the transmission probability, given some transmission power values, is also analysed. Secondly, the inter-channel interference is modelled as a weakly coupled wireless system. Thirdly, Nash differential games are applied to derive optimal power control signals for each user subject to power constraints at each node. Finally, analytical models and numerical examples show the efficacy of the proposed model in solving power control problems in WBNs.
Measuring straight line segments using HT butterflies.
(Public Library of Science, 2012-02-17) Du, Shengzhi; Tu, Chunling; Van Wyk, Barend J.; Ochola, Elisha Oketch; Chen, Zengqiang
This paper addresses the features of Hough Transform (HT) butterflies suitable for image-based segment detection and measurement. The full segment parameters such as the position, slope, width, length, continuity, and uniformity are related to the features of the HT butterflies. Mathematical analysis and experimental data are presented in order to demonstrate and build the relationship between the measurements of segments and the features of HT butterflies. An effective method is subsequently proposed to employ these relationships in order to discover the parameters of segments. Power line inspection is considered as an application of the proposed method. The application demonstrates that the proposed method is effective for power line inspection, especially for corner detection when they cross poles.
Appraising the impact of pressure control on leakage flow in water distribution networks.
(MDPI, 2021-09-23) Mosetlhe, Thapelo C.; Hamam, Yskandar; Du, Shengzhi; Monacelli, Eric
Water losses in Water Distribution Networks (WDNs) are inevitable. This is due to joints interconnections, ageing infrastructure and excessive pressure at lower demand. Pressure control has been showing promising results as a means of minimising water loss. Furthermore, it has been shown that pressure information at critical nodes is often adequate to ensure effective control in the system. In this work, a greedy algorithm for the identification of critical nodes is presented. An emulator for the WDN solution is put forward and used to simulate the dynamics of the WDN. A model-free control scheme based on reinforcement learning is used to interact with the proposed emulator to determine optimal pressure reducing valve settings based on the pressure information from the critical node. Results show that flows through the pipes and nodal pressure heads can be reduced using this scheme. The reduction in flows and nodal pressure leads to reduced leakage flows from the system. Moreover, the control scheme used in this work relies on the current operation of the system, unlike traditional machine learning methods that require prior knowledge about the system.
A robot-assisted large-scale inspection of wind turbine blades in manufacturing using an autonomous mobile manipulator.
(MDPI, 2021-10-06) Engemann, Heiko; Cönen, Patrick; Dawar, Harshal; Du, Shengzhi; Kallweit, Stephan
Wind energy represents the dominant share of renewable energies. The rotor blades of a wind turbine are typically made from composite material, which withstands high forces during rotation. The huge dimensions of the rotor blades complicate the inspection processes in manufacturing. The automation of inspection processes has a great potential to increase the overall productivity and to create a consistent reliable database for each individual rotor blade. The focus of this paper is set on the process of rotor blade inspection automation by utilizing an autonomous mobile manipulator. The main innovations include a novel path planning strategy for zone-based navigation, which enables an intuitive right-hand or left-hand driving behavior in a shared human–robot workspace. In addition, we introduce a new method for surface orthogonal motion planning in connection with large-scale structures. An overall execution strategy controls the navigation and manipulation processes of the long-running inspection task. The implemented concepts are evaluated in simulation and applied in a real-use case including the tip of a rotor blade form.
Chaotic hopfield neural network swarm optimization and Its application.
(Wiley, 2013-01-01) Sun, Yanxia; Wang, Zenghui; van Wyk, Jacobus
A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
Review on aptic assistive driving Systems based on drivers’ steering-wheel operating behaviour.
(Electronics, 2022-07-05) Tientcheu, Simplice Igor Noubissie; Du, Shengzhi; Djouani, Karim
With the availability of modern assistive techniques, ambient intelligence, and the Internet of Things (IoT), various innovative assistive environments have been developed, such as driving assistance systems (DAS), where the human driver can be provided with physical and emotional assistance. In this human–machine collaboration system, haptic interaction interfaces are commonly employed because they provide drivers with a more manageable way to interact with other components. From the view of control system theory, this is a typical closed-loop feedback control system with a human in the loop. To make such a system work effectively, both the driving behaviour factors, and the electrical–mechanical components should be considered. However, the main challenge is how to deal with the high degree of uncertainties in human behaviour. This paper aims to provide an insightful overview of the relevant work. The impact of various types of haptic assistive driving systems (haptic guidance and warning systems) on driving behaviour performance is discussed and evaluated. In addition, various driving behaviour modelling systems are extensively investigated. Furthermore, the state-of-the-art driving behaviour controllers are analysed and discussed in driver–vehicle–road systems, with potential improvements and drawbacks addressed. Finally, a prospective approach is recommended to design a robust model-free controller that accounts for uncertainties and individual differences in driving styles in a haptic assistive driving system. The outcome indicated that the haptic feedback system applied to the drivers enhanced their driving performance, lowered their response time, and reduced their mental workload compared to a system with auditory or visual signals or without any haptic system, despite some annoyances and system conflicts. The driving behaviour modelling techniques and the driving behaviour control with a haptic feedback system have shown good matching and improved the steering wheel’s base operation performance. However, mathematical principles, a statistical approach, and the lack of consideration of the individual differences in the driver–vehicle–road system make the modelling and the controller less robust and inefficient for different driving styles.
A multi-modal brain–computer interface based on threshold discrimination and its application in wheelchair control.
(Springer, 2021-12-23) Dong, Enzeng; Zhang, Haoran; Zhu, Lin; Du, Shengzhi; Tong, Jigang
In this study, we propose a novel multi-modal brain–computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks. .
An n-sigmoid activation function to improve the squeeze-and-excitation for 2D and 3D deep networks.
(MDPI, 2023-02-10) Mulindwa, Desire Burume; Du, Shengzhi
The Squeeze-and-Excitation (SE) structure has been designed to enhance the neural network performance by allowing it to execute positive channel-wise feature recalibration and suppress less useful features. SE structures are generally adopted in a plethora of tasks directly in existing models and have shown actual performance enhancements. However, the various sigmoid functions used in artificial neural networks are intrinsically restricted by vanishing gradients. The purpose of this paper is to further improve the network by introducing a new SE block with a custom activation function resulting from the integration of a piecewise shifted sigmoid function. The proposed activation function aims to improve the learning and generalization capacity of 2D and 3D neural networks for classification and segmentation, by reducing the vanishing gradient problem. Comparisons were made between the networks with the original design, the addition of the SE block, and the proposed n-sigmoid SE block. To evaluate the performance of this new method, commonly used datasets, CIFAR-10 and Carvana for 2D data and Sandstone Dataset for 3D data, were considered. Experiments conducted using SE showed that the new n-sigmoid function results in performance improvements in the training accuracy score for UNet (up 0.25% to 99.67%), ResNet (up 0.9% to 95.1%), and DenseNet (up 1.1% to 98.87%) for the 2D cases, and the 3D UNet (up 0.2% to 99.67%) for the 3D cases. The n-sigmoid SE block not only reduces the vanishing gradient problem but also develops valuable Nfeatures by combining channel-wise and spatial information
A deep learning method for the detection and compensation of outlier events in stock data.
(MDPI, 2022-10-26) Naidoo, Vashalen; Du, Shengzhi
The stock price is a culmination of numerous factors that are not necessarily quantifiable and significantly affected by anomalies. The forecasting accuracy of stock prices is negatively affected by these anomalies. However, very few methods are available for detecting, modelling, and compensating for anomalies in financial time series given the critical roles of better management of funds and accurate forecasting of anomalies. Time series data are a data type that changes over a defined time interval. Each value in the data set has some relation to the previous values in the series. This attribute of time series data allows us to predict the values that will follow in the series. Typical prediction models are limited to following the patterns in the data set without being able to compensate for anomalous periods. This research will attempt to find a machine learning method to detect outliers and then compensate for these detections in the prediction made. This concept was previously unimplemented, and therefore, it will make use of theoretical work on market forecasting, outliers and their effects, and machine learning methods. The ideas implemented in the paper are based upon the efficient market hypothesis (EMH), in which the stock price reflects knowledge about the market. The EMH hypothesis cannot account for consumer sentiment towards a stock. This sentiment could produce anomalies in stock data that have a significant influence on the movement of the stock market. Therefore, the detection and compensation of outliers may improve the predictions made on stock movements. This paper proposes a deep learning method that consists of two sequential stages. The first stage is an outlier detection model based on a long short-term memory (LSTM) network auto-encoder that can determine if an outlier event has occurred and then create an associated value of this occurrence for the next stage. The second stage of the proposed method uses a higher-order neural network (HONN) model to make a prediction based on the output of the first stage and the stock time series data. Real stock data and standalone prediction models are used to validate this method. This method is superior at predicting stock time series data by compensating for outlier events. The improvement is quantifiable if the data set contains an adequate amount of anomalous periods. We may further apply the proposed method of compensating for outliers in combination with other financial time series prediction methods to offer further improvements and stability.
Time-delay estimation improves active disturbance rejection control for time-delay nonlinear systems.
(MDPI, 2024-08-13) Nahri, Syeda Nadiah Fatima; Du, Shengzhi; Van Wyk, Barend Jacobus; Nyasulu, Tawanda Denzel
Lately, active disturbance rejection control (ADRC), a model-independent controller, has become popular for combating various forms of uncertain disturbances incurred in industrial applications. ADRC was validated for external disturbances, internal disturbances, and nonlinearities incurred under realistic scenarios. Time delay challenges all controllers, especially when it coexists with nonlinearities. This paper investigates the impacts of time delay and backlash-like hysteresis nonlinearity in ADRC-controlled systems. These impacts are analyzed, as in the case study, in two ADRC-based methods, namely the ADRC with delayed input method and the predictive extended state observer (PESO)-based ADRC (PESO-ADRC) method. To improve the system response and to attain a decent attenuation of uncertainties, the time-delay estimation (TDE) mechanism is introduced to the concerned ADRC-based methods. Experimental studies are conducted to verify the effectiveness and stability of the proposed TDE-ADRC methods. The results demonstrate the robustness and decent recovery of the transient response after the adverse impact of the backlash-like hysteresis on both concerned ADRC-controlled systems.
Investigating the relevance of graph cut parameter on interactive and automatic cell segmentation.
(Wiley, 2018-09-13) Oyebode, Kazeem Oyeyemi; Du, Shengzhi; Van Wyk, Barend Jacobus; Djouani, Karim
Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation ﬁelds of application, such as the medical ﬁeld. ) e graph cut energy function has a parameter that is tuned to ensure that the output is neither over segmented (shrink bias) nor under-segmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation ﬁelds of application, such as the medical ﬁeld. ) e graph cut energy function has a parameter that is tuned to ensure that the output is neither over segmented (shrink bias) nor under-segmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored.) therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a signiﬁcant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut been ignored.) therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a signiﬁcant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.
A sample-free Bayesian-like model for indoor environment recognition.
(Institute of Electrical and Electronics Engineers (IEEE), 2019-07-01) Oyebode, Kazeem; Du, Shengzhi; Van Wyk, Barend Jacobus; Djouani, Karim
Visual localization of indoor environments enables an autonomous system to recognize its current location and environment using sensors such as a camera. This paper proposes a method for visual recognition of indoor environments leveraging on existing object detection, ontology, Bayesian-like framework, and speeded-up robust features (SURF) algorithms. Objects detected in such an environment are fed into a Bayesian-like framework for domain recognition. Finally, the SURF localizes the predicted environment. One of the objectives of the proposed model is to eliminate the image-based training phase encountered in traditional place recognition algorithms. The proposed model does not rely on any visual information on the environment for training. Experiments are carried out on two publicly available datasets with promising results.
Rope-weaver’s principles: Towards more effective learning.
(Institute of Electrical and Electronics Engineers (IEEE), 2019-12-10) Aylward, R.C.; Van Wyk, B. J.; Hamam, Y.
This paper introduces a new methodology to assist teaching and learning in a time-constrained environment at the hand of two time-on-task examples. These examples are from the field of Electrical Engineering studies with a focus on first-year studies and an advanced software design course taught at the Tshwane University of Technology in South Africa. In an endeavour to understand the timing model of the human brain to master and assimilate new information, a study was conducted to determine some of the parameters that could possibly have an influence on the timing model and how the brain perceives new information. From this study the Rope-Weaver’s Principles were derived and are built on three well-known theories, comparative judgment, the Guttman scale and the learning curve, integrated into the new methodology. The Rope-Weaver’s Principles are presented as an abstraction of the mathematical principles and the measures that underpin this study. The research was done from a participant-observer perspective with design research as central methodology. The research methodology involved a longitudinal study employing mixed-methods research. The results led to the observation of a toe or plateau in the infancy of the learning curve. The observed plateau has a direct influence on the order and time frame of the introduction of new study material in a formal educational programme. The results were found to adhere to the Weber-Fechner Law. This relates to other studies on animals, suggesting that the way the brain perceives stimuli or assimilate knowledge is hard-wired throughout the animal kingdom although the brain structures vary widely. It is proposed that Rope-Weaver’s Principles, complementary to the current pool of teaching and learning theories, lead to a better mastery of the learning material or skills, moving persons under instruction from rule-based training – behaviourism, to maxim integration – constructivism.
Characterization of background temperature dynamics of a multitemporal satellite scene through data assimilation for wildfire detection
(MDPI, 2020-05-21) Udahemuka, Gustave; Van Wyk, Barend J.; Hamam, Yskandar
Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a pixel is commonly derived based on spatial-contextual information that can overestimate the background temperature of a fire pixel and therefore results in the omission of a fire event. This paper proposes a method that assimilates brightness temperatures acquired from the Geostationary Earth Orbit (GEO) sensor MSG-SEVIRI into a Diurnal Temperature Cycle (DTC) model. The expected brightness temperatures are observational forecasts derived using the ensemble forecasting approach. The threshold on the difference between the observed and expected temperatures is derived under a Constant False Alarm Rate (CFAR) framework. The detection results are assessed against a reference dataset comprised of MODIS MOD14/MYD14 and EUMETSAT FIR products, and the performance is presented in terms of user’s and producer’s accuracies, and Precision-Recall and Receiver Operating Characteristic (ROC) graphs. The method has a high detection rate when the data assimilation is implemented with an Ensemble Kalman Filter (EnKF) and a Sampling Importance Resampling (SIR) particle filter, while the weak-constraint Four-Dimensional Variational Assimilation (4D-Var) has comparatively lower detection and false alarm rates according to the reference dataset. Consideration of the diurnal variation in the background temperature enables the proposed method to detect even low-power fires.
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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