Detecting anomalies from data sequences obtained from the space and the torrential data sources available is to require discovering change points within the data sequences. These change points will be formulated to be normal and abnormal changes, which are in turn referred to as anomalies. In our previous studies, three methods -wavelet, statistical martingales and fuzzy-inspired - have been explored, and the respective algorithms have been developed. These results will underpin a new strategy of detecting change points within data sequences and further development of new detection algorithms.
This project aims to develop more sophisticated change detection analytics by identifying change points and comparing two distributions: one estimated from the recorded data sequences and another from dynamic streams. This development may also involve application of the current state of arts deep learning technology. The algorithms developed will be employed to analyse electromagnetic data recorded by the Swarm satellites, China Seismo Electromagnetic Satellite (CSES), CSELF network, etc., with a focus on investigating the correlation between abnormal changes and natural hazards, such as earthquakes. The analysis results will be used to develop an intelligent system for risk assessment of natural hazards and forecast.
Anticipated research outcomes are the following:
1. State of the art change recognition algorithms that can be used to detect changes from electromagnetic data observed from both satellites and ground-based networks
2. Establishment of predictive models that can be used to discover and interpret the relation between the anomalies and natural hazards (earthquakes) over electromagnetic data.
3. Publications in journals and international conferences
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- Masters at 70%
This project is funded by: European Space Agency
This scholarship will cover tuition fees at the home/EU rate and a maintenance allowance of £14,777 per annum for three years.
The Doctoral College at Ulster University
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video