Outputs (839)

Nonlinear adaptive backstepping control of permanent magnet synchronous motor (2021)
Journal Article
Ali, N., Alam, W., Pervaiz, M., & Iqbal, J. (2021). Nonlinear adaptive backstepping control of permanent magnet synchronous motor. Revue Roumaine des Sciences Techniques : Série Électrotechnique et Énergétique, 66(1), 9-14

This paper addresses the speed tracking problem of a permanent magnet synchronous motor (PMSM) under the influence of parametric uncertainties and external load torque disturbances. The nonlinear dynamics associated with both PMSM and load is conside... Read More about Nonlinear adaptive backstepping control of permanent magnet synchronous motor.

A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems (2021)
Journal Article
Khodayee, S. M., Chiacchio, F., & Papadopoulos, Y. (2021). A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems. IEEE Access, 9, 51029-51049. https://doi.org/10.1109/ACCESS.2021.3069807

Flaring has always been an inseparable part of oil production and exploration. Previously, waste gas collected from different parts of facilities was released for safety or operational reasons and combusted on top of a flare stack since there was not... Read More about A novel approach based on stochastic hybrid fault tree to compare alternative flare gas recovery systems.

Finding the most navigable path in road networks (2021)
Journal Article
Kaur, R., Goyal, V., & Gunturi, V. M. (2021). Finding the most navigable path in road networks. GeoInformatica, 25(1), 207-240. https://doi.org/10.1007/s10707-020-00428-5

Input to the Most Navigable Path (MNP) problem consists of the following: (a) a road network represented as a directed graph, where each edge is associated with numeric attributes of cost and “navigability score” values; (b) a source and a destinatio... Read More about Finding the most navigable path in road networks.

A Semantic Knowledge-Based Framework for Information Extraction and Exploration (2021)
Journal Article
Aljamel, A., Osman, T., & Thakker, D. (2021). A Semantic Knowledge-Based Framework for Information Extraction and Exploration. International Journal of Decision Support System Technology, 13(2), 85--109. https://doi.org/10.4018/IJDSST.2021040105

The availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from web data. This research proposes a novel comprehensive semantic knowledge-ba... Read More about A Semantic Knowledge-Based Framework for Information Extraction and Exploration.

Technologies for analysing and improving healthcare processes (2020)
Book Chapter
Gordon, N. (2020). Technologies for analysing and improving healthcare processes. In W. Leal Filho, T. Wall, A. Azul, L. Brandli, & P. Özuyar (Eds.), Good Health and Well-Being. Springer. https://doi.org/10.1007/978-3-319-95681-7

The right to a healthy life is a natural expectation and recognised as a human right (WHO, 2017) – and is further recognised as such in the United Nations Sustainable Development Goals, where Goal 3 focusses on “good health and well-being” (United Na... Read More about Technologies for analysing and improving healthcare processes.

Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J. (2020, August). Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines. Presented at 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), Online

As global efforts in transitioning to sustainable energy sources rise, wind energy has become a leading renewable energy resource. However, turbines are complex engineering systems and rely on effective operations & maintenance (O&M) to prevent catas... Read More about Explainable AI for Intelligent Decision Support in Operations & Maintenance of Wind Turbines.

Learning technologies for learning in health and well-being (2020)
Book Chapter
Gordon, N. (2020). Learning technologies for learning in health and well-being. In P. Gökcin Özuyar, W. Leal Filho, T. Wall, A. Marisa Azul, & L. Brandli (Eds.), Good Health and Well-Being. Springer. https://doi.org/10.1007/978-3-319-95681-7

The United Nations (U.N.) Sustainable Goal 3, for healthy lives and promoting well-being, notes that 45% of all countries have less than one physician per 1000 people, that figure rising to 90% for less developed countries (United Nations, 2018). Giv... Read More about Learning technologies for learning in health and well-being.

Identifying Gaps in Cybersecurity Teaching and Learning (2020)
Presentation / Conference Contribution
Brayshaw, M., Gordon, N., & Karatazgianni, A. (2020, July). Identifying Gaps in Cybersecurity Teaching and Learning. Presented at INSPIRE XXV : e-Learning as a solution during unprecedented times in the 21st Century

This paper explores perceptions and expectations of privacy when using computer-mediated communication and social media. In this paper we present the results of an empirical survey into this topic and explore the pedagogic implications for the teachi... Read More about Identifying Gaps in Cybersecurity Teaching and Learning.

Refining the Teaching of Programming (2020)
Presentation / Conference Contribution
Gordon, N., Cargill, M., Grey, S., & Brayshaw, M. (2020, July). Refining the Teaching of Programming. Presented at INSPIRE XXV: e-Learning as a Solution during Unprecedented Times in the 21st Century, Online

This paper considers issues around the teaching of programming, a critical yet challenging part of the computing education at all levels. This paper begins by outlining some of the key concerns around computing education-from secondary school, throug... Read More about Refining the Teaching of Programming.

A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease (2020)
Presentation / Conference Contribution
Rana, S. S., Ma, X., Pang, W., & Wolverson, E. (2020, December). A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease. Presented at 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Leicester, United Kingdom

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utili... Read More about A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease.