Best URIS Research Project Award 2023

The Best URIS Research Project Award aims to recognise and reward outstanding URIS research projects that have achieved remarkable research achievements. In each URIS cohort, one Grand Award and two Merit Award will be presented. Nominations for the Award are invited in October every year.

Nomination Eligibility

URIS research projects completed on or before 31 August 2023 with the highest rating in the completion report are eligible for nomination.

Selection Criteria

A selection panel has thoroughly assessed and evaluated the nominations for the Award. The selection criteria are as follows:

Achievements and impact of the URIS research project; and
Research outputs and prestigious awards resulting from the project.

Awardees of URIS 2021/22 Cohort

Grand Award

Project Title: A Study of Glass Using a Population of Swap-initiating Particles

Chief Supervisor: Dr LAM Chi-Hang | Department of Applied Physics

Disordered systems, known as glasses, exhibit fascinating dynamics found in various natural phenomena. These dynamics, referred to as glassy dynamics, are observed in diverse systems like living cell groups, foams, and sand particle suspensions. However, developing a comprehensive theory capable of describing and predicting these phenomena has been a challenge for many years. The discovery of such a theory would represent a significant milestone in our comprehension of disordered systems. To address this, our study leverages the benefits of computer simulations. We propose a modification to the partial swap algorithm, enabling simulations that unveil a crucial aspect of glasses: the pivotal role played by defects or voids in the dynamics of these systems. These defects can interact and facilitate the movements of both themselves and the surrounding particles. We demonstrate that the concept of facilitating defects can account for a wide range of glass phenomena, bringing us closer to an all-encompassing theory.

This is an important step in building a theory of disordered systems and their rich behaviour. Such a theory would have far-reaching applications, from climate modelling to computer algorithms.

Department of Applied Physics

Gautham GOPINATH

Merit Award

Project Title: Older adults’ perceptions and acceptability toward the use of artificial intelligence (AI)-based health technologies: a qualitative study

Chief Supervisor: Dr WONG Kwan Ching Arkers | School of Nursing

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing efficiency and accuracy. As our population ages, it’s essential to understand how older adults perceive AI-based health technologies for self-monitoring and maintenance. My URIS project tackled this by gathering crucial insights from older adults, revealing their views on AI health tech and identifying what helps or hinders their use of these tools. Using well-established behavior change models, we developed recommendations for creating user-friendly and secure AI health technologies that could support human care. I'm dedicated to continuing this vital work to improve the integration of AI in elder care.

This URIS project identified the fire development mechanisms in modern building compartments with large glass windows, leading to a novel 'active opening' strategy potentially reforming the window design of buildings.

Department of Building Environment and Energy Building

LAM Ying Tung

Merit Award

Project Title: Study of Fire Impact on Glass Panels

Chief Supervisor: Dr JIANG Liming | Department of Building Environment and Energy Building

Glass facades and windows are important for the look, comfort, and energy efficiency of modern buildings. This project supervised by Dr Liming Jiang and conducted by Ying Tung Lam explored the different fire growth in compartments considering glass failure, which found that early cracks in the glass didn't always provide ventilation openings, and the spread of fire could be reduced by creating appropriate window openings. The project involved extensive computer simulations and laboratory tests, revealing that ideal glass behaviour for fire safety should be sensitive to internal fires and resistant to external fires. Especially, The project's follow-up work led to an innovative 'active opening' strategy to limit fire spread, which received the Sheldon Tieszen award in IAFSS2023 and was validated through real fire tests.

Empowered by data-driven methods and artificial intelligence technologies, this research can considerably accelerate the transition to low-carbon power systems, contributing to carbon neutrality in power systems.

Department of Electrical and Electronic Engineering

ZHU Zixuan

Awardees of URIS 2022/23 Cohort

Grand Award

Project Title: Development of a New Generation of Flow Energy Harvester

Chief Supervisor: Dr Tang Hui | Department of Mechanical Engineering

The URIS project achieved a breakthrough in harvesting energy from flowing water with a unique flapping-foil-based hydropower harvester. Unlike traditional turbine-based technologies, this harvester brings several advantages, such as lower cut-in speeds, slower tip speeds, better-filling factors, and scalability. To convert water flow into electricity, a hybrid power-take-off unit combining triboelectricity and electromagnetic induction was designed, developed, and tested. Through extensive water-tunnel experiments, the unit was optimized for different water speeds and foil movements. The flapping-foil technology operates at lower tip speeds, making it environmentally friendly for energy harvesting in rivers and oceans. Its applications also include charging low-power devices and measuring water speed simultaneously, especially in remote off-grid areas. This innovation opens up new possibilities for harnessing renewable energy from water sources and providing sustainable power solutions in various settings.

A hybrid power-take-off unit has been developed to complete an eco-friendly flapping-foil-based energy harvester that can operate in low-speed water flows.

Department of Mechanical Engineering

He Zhengyang

Merit Award

Project Title: Gradient Inversion Attacks with Limited Computing Resources

Chief Supervisor: Prof. Guo Song (Former professor in Department of Computing)

Recent studies have highlighted a significant privacy concern in Federated Learning: the sharing of parameters, such as model gradients, can lead to substantial user privacy leakage. This research specifically focuses on the privacy vulnerabilities inherent in shared gradients within the context of Federated Learning. Despite computational limitations, this project introduces an innovative multi-modal gradient inversion attack method (MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning) marking a first in the field. As it is known that there are certain correlations between multi-modality data, we argue that the threat of such attacks combined with Multi-modal Learning may cause more severe effects. In the meanwhile, our experimental results verify that multi-modality gradient inversion attacks are more likely to disclose private information than the existing single-modality attacks.

This research pioneers the integration of multi-modal and gradient attack methods, introducing the first-ever multi-modal gradient inversion attack algorithm, contributing to privacy concerns in Federated Learning.

Department of Electrical and Electronic Engineering

Liu Xuan

Merit Award

Project Title: A Research on Wireless Power Transfer of Household Appliance

Chief Supervisor: Dr Bu Siqi | Department of Electrical and Electronic Engineering

Wireless power transfer (WPT) is an exciting technology that offers a solution to the limitations of traditional power acquisition methods. This research focuses on investigating the output power and efficiency of WPT systems using theoretical modelling, circuit analysis, and numerical simulation. The circuit compensation network typology and the transmission coils are the two main objects being studied throughout this research. This project first analyzed and compared six different system structures, with the variable factors of input frequency and load resistance through the MATLAB simulation. The numerical analysis process was simplified, and the results were presented through a simulation model in Simulink that integrated four circuit typologies. Furthermore, the project has explored the design conditions for the transmission coil in detail. It is believed this project's results will further benefit the WPT system design.

This project examines the performance of a Wireless Power Transfer system from various angles, providing valuable theoretical insights for advancing the charging system's development.

Department of Electrical and Electronic Engineering

Jin Ziwei