Future Projects - Institute of Applied Data Analytics

Wave and Tide Prediction for Brunei Deep-Water Operation


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.

Application of Multi-objective Evolutionary Computation in Human Activity Discovery


Grant: UBD/RSCH/1.11/FICBF(b)/2019/001. In this project, we aim to develop an evolutionary algorithm which is capable of performing human activities analysis for an autonomous robot. (FOS/IADA/DS project)

Application of Artificial Intelligence and Machine Learning in a) Material Science research, b) Driving modelling [Modeling driving patterns using simulation and sensor data for enhancing traffic safety]


a) The main objective of this study is to develop a framework using AI and Machine Learning techniques for decision making and solving problems in Material Science. (CAMES/IADA project) b) Grant UBD/RSCH/I.11lFICBF/2018/002. In this research project, we aim to study the driving patterns of drivers in Brunei using simulated and sensor data, identifying extensively detailed driving parameters including distance from traffic light, pressure during braking or acceleration, acceleration. These actual driving patterns along any road are not easily observable and measured by an analyst. By identifying different profiles (such as safe or unsafe driving) in the driving patterns, a system to warn the driver can be implemented. (FOS/IADA/DS project)