Outputs (25)

HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients (2024)
Journal Article
Petch, L., Moustafa, A., Ma, X., & Yasser, M. (2025). HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients. Applied Intelligence, 55(2), Article 170. https://doi.org/10.1007/s10489-024-05924-x

This paper introduces a novel approach for training generative adversarial networks using federated machine learning. Generative adversarial networks have gained plenty of attention in the research community especially with their abilities to produce... Read More about HFL-GAN: scalable hierarchical federated learning GAN for high quantity heterogeneous clients.

Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective (2024)
Presentation / Conference Contribution
Tuton, E., Ma, X., & Dethlefs, N. (2024, May). Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective. Presented at The Science of Making Torque from Wind (TORQUE 2024), Florence, Italy

Digital Twin (DT) technology has seen an explosion in popularity, with wind energy no exception. This is particularly true for Operations & Maintenance (O&M) applications. However, this expanded use has been accompanied by loose, conflicting, definit... Read More about Redefining Digital Twins - A Wind Energy Operations and Maintenance Perspective.

Intelligent digital twin - machine learning system for real-time wind turbine wind speed and power generation forecasting (2023)
Presentation / Conference Contribution
Tuton, E., Ma, X., & Dethlefs, N. (2023, August). Intelligent digital twin - machine learning system for real-time wind turbine wind speed and power generation forecasting. Presented at The 6th International Conference on Renewable Energy and Environment Engineering REEE 2023, Brest , France

Wind power is a key pillar in efforts to decarbonise energy production. However, variability in wind speed and resultant wind turbine power generation poses a challenge for power grid integration. Digital Twin (DT) technology provides intelligent ser... Read More about Intelligent digital twin - machine learning system for real-time wind turbine wind speed and power generation forecasting.

Patient specific training: development of a CT-based mixed reality fibreoptic intubation simulator (2022)
Journal Article
Wright, D., Ma, X., Atkin, W., Wang, L., Fagan, M., & Wellbelove, Z. (2022). Patient specific training: development of a CT-based mixed reality fibreoptic intubation simulator. International Journal of Healthcare Simulation, 2(1), A86. https://doi.org/10.54531/QOJS8275

Fibreoptic intubation training has traditionally been performed using real fibreoptic scopes and manikins or improvised airway ‘boxes’, recently progressing to virtual reality training devices [1]. The latter are populated with computer generated ima... Read More about Patient specific training: development of a CT-based mixed reality fibreoptic intubation simulator.

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.

GANS-based data augmentation for citrus disease severity detection using deep learning (2020)
Journal Article
Zeng, Q., Ma, X., Cheng, B., Zhou, E., & Pang, W. (2020). GANS-based data augmentation for citrus disease severity detection using deep learning. IEEE Access, 8, 172882-172891. https://doi.org/10.1109/ACCESS.2020.3025196

Recently, many Deep Learning models have been employed to classify different kinds of plant diseases, but very little work has been done for disease severity detection. However, it is more important to master the severities of plant diseases accurate... Read More about GANS-based data augmentation for citrus disease severity detection using deep learning.

A 3D cephalometric protocol for the accurate quantification of the craniofacial symmetry and facial growth (2019)
Journal Article
Pinheiro, M., Ma, X., Fagan, M. J., McIntyre, G. T., Lin, P., Sivamurthy, G., & Mossey, P. A. (2019). A 3D cephalometric protocol for the accurate quantification of the craniofacial symmetry and facial growth. Journal of Biological Engineering, 13(1), https://doi.org/10.1186/s13036-019-0171-6

© 2019 The Author(s). Background: Cephalometric analysis is used to evaluate facial growth, to study the anatomical relationships within the face. Cephalometric assessment is based on 2D radiographic images, either the sagittal or coronal planes and... Read More about A 3D cephalometric protocol for the accurate quantification of the craniofacial symmetry and facial growth.

Towards additive manufacturing oriented geometric modeling using implicit functions (2018)
Journal Article
Li, Q., Hong, Q., Qi, Q., Ma, X., Han, X., & Tian, J. (2018). Towards additive manufacturing oriented geometric modeling using implicit functions. Visual Computing for Industry, Biomedicine, and Art, 1(1), Article 9. https://doi.org/10.1186/s42492-018-0009-y

Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools. However, it is not an ideal solution for additive manufacturing because t... Read More about Towards additive manufacturing oriented geometric modeling using implicit functions.

Surface fitting for quasi scattered data from coordinate measuring systems (2018)
Journal Article
Mao, Q., Liu, S., Wang, S., & Ma, X. (2018). Surface fitting for quasi scattered data from coordinate measuring systems. Sensors, 18(1), 214. https://doi.org/10.3390/s18010214

Non-uniform rational B-spline (NURBS) surface fitting from data points is wildly used in the fields of computer aided design (CAD), medical imaging, cultural relic representation and object-shape detection. Usually, the measured data acquired from co... Read More about Surface fitting for quasi scattered data from coordinate measuring systems.

T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM (2017)
Journal Article
Han, Z., Wang, Y., Ma, X., Liu, S., Zhang, X., & Zhang, G. (2017). T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM. Applied Sciences, 7(10), 1092. https://doi.org/10.3390/app7101092

With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (... Read More about T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM.