Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method
Journal Article
Yuan, H., Goh, K., Andras, P., Luo, W., Wang, C., & Gao, Y. (2024)
Steering angle sensorless control for four-wheel steering vehicle via sliding mode control method. Transactions of the Institute of Measurement and Control, 46(3), 453-462. https://doi.org/10.1177/01423312231181993
This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, ...
Federated Learning for Short-term Residential Load Forecasting
Journal Article
Briggs, C., Fan, Z., & Andras, P. (in press)
Federated Learning for Short-term Residential Load Forecasting. IEEE Open Access Journal of Power and Energy, https://doi.org/10.1109/oajpe.2022.3206220
Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply tra...
Scalability resilience framework using application-level fault injection for cloud-based software services
Journal Article
Al-Said Ahmad, A., & Andras, P. (2022)
Scalability resilience framework using application-level fault injection for cloud-based software services. Journal of cloud computing: advances, systems and applications, 11(1), Article 1. https://doi.org/10.1186/s13677-021-00277-z
This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework u...
A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things
Book Chapter
Farhad, A., Woolley, S. I., & Andras, P. (2021)
A Preliminary Scoping Study of Federated Learning for the Internet of Medical Things. In J. Mantas, L. Stoicu-Tivadar, C. Chronaki, A. Hasman, P. Weber, P. Gallos, …O. Sorina Chirila (Eds.), Public Health and Informatics (504-505). Amsterdam: IOS Press. https://doi.org/10.3233/SHTI210216
This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has ...
Compounding barriers to fairness in the digital technology ecosystem
Conference Proceeding
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021)
Compounding barriers to fairness in the digital technology ecosystem. In 2021 IEEE International Symposium on Technology and Society (ISTAS). https://doi.org/10.1109/istas52410.2021.9629166
A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are co...
A review of privacy-preserving federated learning for the Internet-of-Things
Book Chapter
Briggs, C., Fan, Z., & Andras, P. (2021)
A review of privacy-preserving federated learning for the Internet-of-Things. In M. Habib ur Rehman, & M. Medhat Gaber (Eds.), Federated Learning Systems: Towards Next-Generation AI (21-50). Cham: Springer. https://doi.org/10.1007/978-3-030-70604-3_2
The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is attributable to human activities and behavior. Collecting personal data and executing mach...
Where do successful populations originate from?
Journal Article
Andras, P., & Stanton, A. (2021)
Where do successful populations originate from?. Journal of Theoretical Biology, 524, Article 110734. https://doi.org/10.1016/j.jtbi.2021.110734
In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these po...
Federated Learning for Short-term Residential Energy Demand Forecasting
Working Paper
Briggs, C., Fan, Z., & Andras, P. (2021)
Federated Learning for Short-term Residential Energy Demand Forecasting
Energy demand forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As s...
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020)
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. In NeurIPS 2020 Workshop: Tackling Climate Change with Machine Learning
In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High res...
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
Conference Proceeding
Briggs, C., Fan, Z., & Andras, P. (2020)
Federated learning with hierarchical clustering of local updates to improve training on non-IID data. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207469
Federated learning (FL) is a well established method for performing machine learning tasks over massively distributed data. However in settings where data is distributed in a ...