Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo

Assessment and Prediction of Sea Level Trend in the South Pacific Region

Sea level rise is an important and topical issue in the South Pacific region and needs an urgent assessment of trends for informed decision making. This paper presents mean sea level trend assessment using harmonic analysis and a hybrid deep learning (DL) model based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Neighbourhood Component Analysis (NCA) to build a highly accurate sea level forecasting model for three small islands (Fiji, Marshall Island and Papua New Guinea (PNG)) in the South Pacific. For a 20-year period, the estimated mean sea level rise per year from the harmonic computation is obtained: 112 mm for PNG, 98 mm for Marshall Island and 52 mm for Fiji. The DL procedure uses climate and environment-based remote sensing satellite (MODIS, GLDAS-2.0, MODIS TERRA, MERRA-2) predictor variables with tide gauge base mean-sea level (MSL) data for model training and development for forecasting. The developed CEEMDAN-CNN-GRU as the objective model is benchmarked by comparison to the hybrid model without data decomposition, CNN-GRU and standalone models, Decision Trees (DT) and Support Vector Regression (SVR). All model performances are evaluated using reliable statistical metrics. The CEEMDAN-CNN-GRU shows superior accuracy when compared with other standalone and hybrid models. It shows an accuracy of >96% for the correlation coefficient and an error of < 1% for all study sites

Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model

Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, the prediction of students’ final course grades with continuous assessment marks is a useful toolkit for degree program educators. Predictive models are practical tools used to evaluate the effectiveness of teaching as well as assessing the students’ progression and implementing interventions for the best learning outcomes. This study develops a novel multivariate adaptive regression spline (MARS) model to predict the weighted score WS (i.e., the course grade). To construct the proposed MARS model, Introductory Engineering Mathematics performance data over five years from the University of Southern Queensland, Australia, were used to design predictive models using input predictors of online quizzes, written assignments, and examination scores. About 60% of randomised predictor grade data were applied to train the model (with 25% of the training set used for validation) and 40% to test the model. Based on the cross-correlation of inputs vs. the WS, 12 distinct combinations with single (i.e., M1–M5) and multiple (M6–M12) features were created to assess the influence of each on the WS with results bench-marked via a decision tree regression (DTR), kernel ridge regression (KRR), and a k-nearest neighbour (KNN) model. The influence of each predictor on WS clearly showed that online quizzes provide the least contribution. However, the MARS model improved dramatically by including written assignments and examination scores. The research demonstrates the merits of the proposed MARS model in uncovering relationships among continuous learning variables, which also provides a distinct advantage to educators in developing early intervention and moderating their teaching by predicting the performance of students ahead of final outcome for a course. The findings and future application have significant practical implications in teaching and learning interventions or planning aimed to improve graduate outcomes in undergraduate engineering program cohorts

On asynchronous running of synchronous machines and related mechanical stresses

Typescript (photocopy).Asynchronous running of synchronous generators is considered during system disturbances with particular reference to shaft stresses. Such an abnormal running may lead to mechanical stresses in the turbine-generator shaft system. These stresses can lead to shaft fatigue loss of life which is evaluated by a proper fatigue model. Theoretical aspects of the parametric resonance phenomenon, which is of importance during asynchronous running, are also considered. Effect of variation of certain important parameters on asynchronous running is also described. Directions as regards the factor of safety during shaft specification have been indicated

On asynchronous running of synchronous machines and related mechanical stresses

Typescript (photocopy).Asynchronous running of synchronous generators is considered during system disturbances with particular reference to shaft stresses. Such an abnormal running may lead to mechanical stresses in the turbine-generator shaft system. These stresses can lead to shaft fatigue loss of life which is evaluated by a proper fatigue model. Theoretical aspects of the parametric resonance phenomenon, which is of importance during asynchronous running, are also considered. Effect of variation of certain important parameters on asynchronous running is also described. Directions as regards the factor of safety during shaft specification have been indicated

Special Section on Condition Monitoring and Fault Accommodation in Electric and Hybrid Propulsion Systems

LGEP 2014 ID = 1675International audienc

Hybrid electric vehicles: principles and applications with prantical perspectives

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Future vision for reduction of range anxiety by using an improved state of charge estimation algorithm for electric vehicle batteries implemented with low-cost microcontrollers

The fear of being stranded by a depleted electric vehicle (EV) battery is commonly referred to as \u27range anxiety\u27. This study explores a future vision for a comprehensive driver alerting algorithm to reduce the range anxiety by increasing the accuracy of the EV range estimation method. The key piece of information required to achieve this is an accurate battery state of charge (SoC) estimation. This study proposes an improved SoC estimation algorithm for implementation using low-cost microcontrollers. A method by which this improved algorithm can be implemented in a distributed battery management system is presented. The improved SoC estimation can be used to provide an enhanced range estimation method that can take into consideration a variety of environmental and behavioural factors. The proposed range estimate is more accurate than when only SoC is considered and can be implemented as part of a comprehensive driver alerting system to alert the EV driver of the expected energy required to reach the destination, the expected range with current SoC, an advisory if charging will be required prior to reaching the destination and the suggested duration of the charging required. By reducing the uncertainty surrounding EV range, it is hoped that the uptake of EVs can be improved