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Current projects in Artificial Intelligence and Applications

The list below details the projects we are currently researching as well as projects that have been recently completed.

Current Projects

ASGARD: Analysis System for Gathered Raw Data

Dates: Project began in 2016 for 42 months
Researchers: Hui Wang, Bryan Scotney, Chris Nugent, Omar Nibouche (jointly with Built Environments and Social Sciences)
Funded by: EC Horizon 2020 FCT-1-2015

This project analyses various types of data to support fight against crime and terrorism

DESIREE: Decision Support and Information Management System for Breast Cancer

Dates: Feb 2016 - Jan 2019
Researchers: Hui Wang (PI), Bryan Scotney, John Winder (INHR), Philip Morrow, Maurice Stringer (PRI), Zhiwei Lin, Lynn Dunwoody (PRI)
Funded by: EC Horizon 2020 PH-30

Medical image analysis, data and knowledge modelling, case-based reasoning to transform decision making for breast cancer diagnosis and treatment

VIAD: Fast video-based anomaly detection on railway tracks

Dates: June 2014 - May 2016
Researchers:
Hui Wang (PI), Bryan Scotney, Jun Liu, Kathy Clawson
Funded by: Royal Society and NSFC

The VIAD project focuses on the problem of detecting anomaly on high-speed rail tracks based on video streams from train mounted cameras. The tracks are expected to be clear of any objects, moving or stationary, within a certain peripheral range.

The tracks are also expected to be in good shape. If any of these expected conditions are violated, the scene can be deemed abnormal and an alarm may be raised.

The aim of this project is to design a novel anomaly measure to enable a novel framework for environmental anomaly detection based on video streams from train mounted cameras, and evaluate on real video data through the China partners.

Projects recently completed

Explaining and Explaining Away

Dates: 2013 - 2015
Researchers: David Glass (PI), Mark McCartney
Funded by: The John Templeton Foundation

In this project we explored the concept of explaining away in order to show that it provides a fruitful way to understand various aspects of the interaction between science and religion.

The main research activities were the development of the theory of explaining away, the application of this work to a science and religion context, and the development and implementation of models for simulating how social groups update their beliefs in contexts where both scientific and religious explanations are present.

Outputs will be in the form of academic papers, presentations at conferences, popular articles, and in the organization of an interdisciplinary workshop for academics and a training event for ministers and laypersons.

This project will make a difference for academics by showing that the concept of explaining away can shed new light on some discussions in the field of science and religion and that formal approaches have a powerful and useful role to play in understanding the science and religion dynamic. It also aims to raise awareness to a lay audience that scientific explanations need not undermine religious explanations, but that they can be mutually supportive.

The research has the potential to generate novel avenues in the dialogue between science and religion by providing a new way in which apparent conflicts can be resolved and by developing computational models of belief change in a social group.

Given that one aspect of the project’s innovation comes from its interdisciplinary nature, drawing on research in the fields of artificial intelligence, formal epistemology and computer simulations of complex social networks, it is hoped that it will also have an impact beyond the field of science and religion.

DASGED: Detecting Anomalies from Satellite and Terrestrial Electromagnetic Data Sources by Big Data Analytics

Dates: 2012 - 2016
Researchers: Yaxin Bi (PI), Hui Wang, Terry Anderson
Funded by: European Space Agency


The evolutionary process of earthquakes is rich of complex features, from stochastic to chaotic or pseudo-periodic dynamics, often depending on the different geo-tectonic regime and focal mechanisms.

A wide range of fundamental research in the lithosphere, such as from fault rupture mechanics to seismic wave propagation, has been undertaken on different regions in past decades.

Many case studies show there are some seismic anomalies before earthquakes and associated phenomena after earthquakes in respect of ground deformation, active faults (slip rates and geometry), tectonic stress fields and geomagnetic fields, but systematic understanding of preparation process of earthquakes and their seismic cycles is very limited to date.

There is no pragmatic approach to predicting earthquakes. Earth observation (EO) building on satellites provides the enormous capability of observing regional and global areas of our planet. It records a wide range and huge volumes of spatial-temporal measurements, which capture a variety of activities occurring on the earth and in its interior. Meanwhile, EO provides new possibilities for scientists to investigate the earthquake behaviour by using a broad range of abnormal phenomena reflected in ionosphere and ground displacement activities observed from space and improve understanding of preparation process of earthquakes from the world scale and the perspectives of coupling ionosphere with lithosphere.

The project is aimed to investigate spaceborne DEMETER data, SWARM, SAR and other satellite data for detecting a range of precursors reflected in ionosphere and lithosphere, before large earthquakes and to assess correlations between the selected precursors and seismic events. The specific objectives are to

  • evaluate HJ-1B, CBERS-01&02, CBERS-03, HY-1 & 2, ENVISAT/ASAR, etc. satellites for monitoring deformation and thermal infrared spectrum, and DEMETER, SWARM and CSBS for electromagnetic emission; *improve our knowledge and understanding on ionospheric perturbations and abnormal changes in electromagnetic emission intensity and electron density using DEMETER, SWARM, CSBS;
  • develop effective methodologies based on computational intelligence (machine learning and data mining), statistical methods and existing methods developed for handling ground deformation and thermal infrared spectrum to extract anomalies;
  • define a uniform entropy measure to transform the diverse anomalies to a uniform representation without loss of information content and precision;
  • perform information fusion on unified anomalies and examine the divergence among combined results and select convergent anomalies for further analyses;
  • analyze correlations and coherences between the anomalies and seismic cycles and;
  • develop a uncertainty model based on information fusion theory, Bayesian probability and Theory of Belief Functions to provide an approach to estimating probabilities of potential seismic activities in a given region.

MEWODM: Monitoring and Early Warning of Epidemic Diseases by Earth Observation Data Mining

Dates: 2012 - 2016
Researchers:
Yaxin Bi (PI), Chris Nugent and Shengli Wu
Funded by:
European Space Agency

Many of epidemic and infectious diseases are closely related to natural environment for their living, breeding and transmission of pathogens, especially vector-borne diseases (e.g. schistosomiasis) that rely heavily on their vectors.

The project is aimed at developing a viable approach for discovering and monitoring diseases, which will be used to prevent and control vector-borne diseases.

SMS: Sentiment Mining for Security

Dates: 2013 - 2014
Researchers: Maurice Mulvenna (PI) and Yaxin Bi
Funded by: Knowledge Transfer Partnerships, Technology Strategy Board

Increase competitiveness and growth by developing unique sentiment-mining techniques, utilising cloud-based parallel/multi-core processing resources, to increase data-scaling across multiple data-centres, databases and processing engines for detecting security related events.

SAVASA: Standards based Approach to Video Archive Search and Analysis

Dates: September Dec 2011 - May 2014
Researchers: Hui Wang (PI), Bryan Scotney, Jun Liu
Funded by: EU FP7 Security Program

Video archive search -- video event recognition

DEEPFLOW: Extraction of argument structures from text to support innovation and decision

Dates: September 2010 - September 2014
Researchers: Hui Wang (PI), Yaxin Bi
Funded by: Invest Northern Ireland R&D Programme and SAP

DeepFlow seeks to address some of the key problems related to understanding and managing interrelated design documents as collated on a given manufacturing project in the Aerospace, Defence and Automotive industries.

Design specification documents consist of large sections of unstructured textual data, many of which over the duration of the project are subject to revision based on a expert-led discourse over the strengths and weaknesses of the various design components.

The project aims to form a unique combination of research into natural language processing and argumentation theory, to develop a project summarization tool that assists engineers to comprehend the topical content and various discourse themes within a given project. Such a tool is of enormous benefit to the industry as a design project may typically contain many thousands documents.

The tool will have the added benefit of allowing different projects to be compared and contrasted in terms of their underlying content.

This provides a significant progression over current document management applications currently in use. Such systems provide only limited text analysis mechanisms whereas DeepFlow will allow designers to be able to discover the gist of a project and quickly re-evaluate the design elements of existing specifications, when considering a new project.