Lecturer, Faculty of Integrated Technologies and Lecturer, Faculty of Science
Grant Details: UBD/RSCH/1.11/FICBF(b)/2020/004 Reliable and accurate predictions of deep-water wave conditions is crucial for any ocean engineering activities, for example, safe ship navigation, design of marine structures (like oil platforms and harbours), and design and management of marine energy systems as it has an impact on human safety, economics and clean energy production. Different empirical, numerical and soft-computing approaches have been proposed for wave height prediction. Wave conditions in regions where historical data is available and collected can be predicted using numerical and soft-computing methods. However, for new regions of interest, in this case Brunei sea, historical data of wave conditions is not available. Hence, there is a need for collecting and predicting the wave conditions data in Brunei sea. This project will employ advanced artificial intelligence (AI) algorithms and modelling methods to improve the accuracy of the wave conditions prediction.
Grant Details: UBD/RSCH/1.3/FICBF(b)/2020/011 Due to the recent environmental concerns and long-term challenges in energy security, the Global energy scenarios are shifting more towards sustainable and renewable energy resources. As rightly reported by the International Renewable Energy Association (IRENA), renewable energy technologies are fast becoming cost effective and soon can compete with the traditional energy options. Brunei has planned to increase the use of cleaner energy technologies by contributing 10 percent or 954 GWh of renewable energy in its power generation mix by 2035. Out of the available renewable options, solar is the most promising one for Brunei, for example, the daily average solar installation is around 5kWh per day [Energy and Industry Department of Prime Minister’s office (2016]. Though solar energy is an abundant resource, for optimally designing and successfully managing solar power projects, its availability in different time scales are to be analysed and understood in a local context. The objectives of the proposed project is to develop such design and management tools by applying machine learning and deep learning algorithm. In the first part of the project, a tool for estimating the power produced by different commercial PV systems under the varying solar insolation at a prospective site will be developed. This machine learning model will highly be relevant for designing solar power systems under Bruneian environments as it is based on the long term performance data from the Tenaga Suria PV project. Further, a system for forecasting the power developed by the solar PV plants in different time scales will also be developed and tested. Such forecasting system will be useful in the power dispatch and management of the solar systems installed in Brunei. Apart from the scientific contributions through publications and scientific networking, the project is also expected to equip UBD to support the national vision of supplementing the energy base with renewable and sustainable energy resources.
The effect of ageing on biometric systems and particularly its impact on face recognition systems is a challenging area for research. Face ageing in humans is the result of multi-dimensional process of physical, physiological, and social change, which affects considerably the appearance of a human face. Being biological tissue in nature, facial biometric trait undergoes significant changes as a person ages. Ageing-related appearance variation due to bone growth normally occurs throughout childhood and puberty, whereas skin-related effects principally appear in older subjects. Looking from the face recognition point of view, ageing of the face images of the same person brings confusion which degrades the system performance dramatically. In many practical systems (e.g., passport control, etc.), the time intervals between two acquired images can lead up to several years. The ageing factor is very significant in face images. In comparison to other facial variation (pose, illumination, etc.), adaption to template ageing deserves a dedicated treatment of its own, since ageing is a lifelong process. Ageing also bring gradual changes in the data distribution over time, thus causing performance loss as a result of template becoming outdated. These factors indicate that template ageing process is very similar to the concept drift theory, based on the fact that real-worlds concepts change with the time resulting in underlying data distribution to change. When compared with other source of variation in face images, ageing variations is specific to a given individual. It can occur slowly and is affected significantly by other factors. The appearance of a human face is affected considerably by the ageing process.
Grant Details: UBD/RSCH/1.11/FICBF(b)/2020/004
Wave and Tide Prediction for Brunei Deep-Water Operation
Grant Details: UBD/RSCH/1.3/FICBF(b)/2020/011
Artificial intelligent management system (AIMS) for solar PV projects in Brunei Darussalam
Industry Experience working at Cisco Premier Partner Certified in Brunei Darussalam and Brunei Shell Petroleum (BSP)