Date:

2014-2018

Theme:

Marine Energy

Funding:

EPSRC (EP/L021560/1)

For the UK to meet legally binding renewable targets by year 2020 and beyond, a series of wind farms are under construction, and more new projects are being developed. The offshore grids and their connection to the existing transmission systems can only be realised using the HVDC links. Using high voltage will make power transmission more efficient and the only feasible solution is to use extreme upgrades to existing plant. Cables, the key part of the HVDC links, will be forced to operate well beyond their existing limits (currently about +/-500 kV and 1000 MW). This means that both cable insulation and operating conditions have to be pushed far beyond the limits currently considered acceptable. Therefore, a fundamental understanding of the performance of the insulation and the development of novel operating methodologies are urgently required to meet the challenges. This project will investigate the impact of inherent defects, space charge dynamics, partial discharges, electrical tree and power quality on electrical performance of highly stressed lapped cable insulation and develop novel operating methodologies that take account of the influence of the above factors to achieve reliable and safe operation for the HVDC links.

Primary investigators

• George Chen
• Professor Simon Rowland
• Professor Steve Swingler
• Paul Lewin

Secondary investigators

• Matthew Praeger
• Zhiqiang Xu

Associated research groups

• Electronics and Computer Science
• Electronics and Electrical Engineering

Date:

2015-2019

Themes:

Medical Engineering, Microfluidics and Lab-on-a-chip

Funding:

University PGR studentship

Mesenchymal stem cells (MSCs) can be isolated from the bone marrow and may be used for regenerative medicine applications because they can differentiate into fat, cartilage and bone cells. However, MSCs constitute less than 1% of the bone marrow mononuclear cell population, which presents challenges for their isolation and characterization. Moreover, these stem cells are heterogeneous, displaying variable potential for proliferation and differentiation, which complicates studies with conventional assays. In this project, we use a microfluidic cell trap device to array large numbers of MSCs for single-cell optical imaging. Immunolabelling of surface markers will be used to identify MSC subpopulations, while exposure to signalling pathway activators will yield information on the differentiation potential of specific MSC sub-types. The ultimate aim is to formulate drug carriers that are able to trigger the differentiation of susceptible stem cells into desired tissue, for example to enhance bone mass to reduce fracture risk, or to facilitate bone fracture healing.

Primary investigators

• Maurits De Planque
• Dr Nicholas Evans (Medicine)

Secondary investigator

• Mr Antonio De Grazia

Associated research group

• Electronics and Electrical Engineering

Date:

2014-2017

Themes:

Modeling and Simulation, Environmental modelling

Funding:

SMMI, FPSE, FEE PhD studentship

The understanding of transient wave dynamics spectra on solid and/or permeable structures is of paramount importance for manufacturing and designing new material for the next generation of coastal defences in the UK. The observed sea rises and more frequent severe storms due to climate change are exposing our coastal defences to serious damage, induced casualties and huge economic costs. The rigorous simulation of the dynamics of the flow prior to, at and after impact at complex structures and the understanding of their response in a more systematic way needs to be investigated. However, this represents a great fluid dynamics simulation with intensive computations as the structure of the porous medium gets complex. In this PhD work, free surface flow simulations, analyses of forces of impact, and the integrity and responses of structures to transient impulsive signals with compressed air bubbles will be investigated. Distributed computing intensive simulations will be achieved to discover optimised designs of resilient porous structures of the future.

Primary investigator

• zas (supervisor)

Secondary investigators

• Mr Robert Mayon (PhD student)
• Dr Tang Mingyi (co-supervisor)
• Dr Kamal Djijeli (co-supervisor)

Associated research group

• IT Innovation Centre

Date:

2015-2015

Themes:

Open & Linked Data, Data Science / Big Data, Machine Learning

Funding:

HEIF Research Collaboration Stimulus Fund

The project focusses on developing preliminary protocols for the processing of wireline and geological big data, then train specialised machine learning lithology classifiers. The classifiers shall automatically interpret borehole wireline logs from a well characterized borehole into the upper oceanic crust with targeted high Correct Classification Rates (CCRs>90%). They should be trained to successfully generalize in their lithology classifications when tested against geological knowledge based on drill core observations. The achievement of best lithology classification can be reached through accurate matching of the big data generated from wireline logging and core recoveries. Such correct data modelling by the classifiers can be achieved only through strong understanding of the domain knowledge for the labelling of the training datasets. The big data concerned in the learning experiments can be semi-structured and multi-spectral in most cases. Hence it exhibits high complexity, unbalancing, asynchronicities and more likely some gaps. Machine learning approaches for lithology classification techniques may need to be supported by additional domain knowledge reasoning models based on fuzzy logic to overcome persistent mis-classifications (low CCRs). In recent years (Sabeur et al, 2013, 2015) have worked on operational drilling data, under the TRIDEC (2010-2013) project, with optimised classifiers that sustain big data complexity, unbalancing and classes inseparability challenges.

G. V. Veres and Z. A. Sabeur (2015). Data Analytics for Operational Drilling States Classifications. 23rd European Symposium on Artificial Neural Networks, In Computational Intelligence and Machine Learning, 22nd -24th April 2015, Bruges, Belgium. ISBN 978-287587014-8.

G. V. Veres and Z. A. Sabeur (2013). Automated operational states detection for drilling systems control in critical conditions. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 24th-26th April 2013, Bruges, Belgium.

Primary investigator

• zas

Secondary investigator

• Professor Damon Teagle

Associated research group

• IT Innovation Centre

Date:

2006-2009

Themes:

Web of Things, Environmental Monitoring, Sensor Web

Funding:

EU

The European Commission (EC) and the European Space Agency (ESA) have established a joined Global Monitoring for Environment and Security (GMES) initiative. Sensors Anywhere Integrated Project (SANY) will contribute to this initiative by improving the interoperability of in-situ sensors and sensor networks, and allowing quick and cost-efficient reuse of data and services from currently incompatible sources in future environmental risk management applications.

Five major SANY objectives are: 1) Specify a standard open architecture for fixed and moving sensors and sensor networks capable of seamless "plug and measure" and sharing (virtual networks), applicable to all kinds of in-situ sensors, classical and ad-hoc sensor networks, virtual sensors (sensor-like software), roving and airborne sensors, and ensure interoperability between ground and in-orbit sensors. 2) Develop and validate re-usable data fusion and decision support service building blocks. 3) Assure a reference implementation of the architecture, i.e. an on-demand environment for accessing the GMES information and services is operational as GMES building block in 2008. 4) Assure the new architecture is generic and provides added value for end users. 5) Assure the outcome of SANY is accepted by end users and international organisations and contributes to a future standard applicable to GMES.

SANY inherits and extends the results of two high profile EC and ESA infrastructure projects; ORCHESTRA and MASS/SSE. All architecture specifications shall be publicly available and compatible with EC and ESA infrastructure initiatives, such as INSPIRE (standard interfaces with geospatial information) , and Heterogeneous Missions Accessibility project (standard interfaces for EO Ground Segments);

SANY specifications shall be validated by experts trough OGC technical committee and realised in three innovative risk management applications covering the areas of air pollution, marine risks and geo hazards.

Primary investigator

• zas

Secondary investigators

• Stuart Middleton
• gvv
• Zlatko Zlatev

Partners

• Austrian Institute of Technology, Austria.
• IOSB Fraunhofer Institute, Germany
• SpaceBel, Belgium
• BMT Group Limited
• SOLDATA SA
• Enviro Informatik Group
• OGC Europe

Associated research group

• IT Innovation Centre

Date:

2008-2011

Themes:

Machine Learning, Artificial Neural Networks, Image processing and Computer Vision

Funding:

EUFP7

SCOVIS investigated weakly supervised learning algorithms and self-adaptation strategies for analysis of visually observable operations in an industrial context environment. SCOVIS research directly affects ease of deployment and minimises effort of operation of monitoring systems and is unique in the sense that it links object learning using low-level object descriptors and procedure learning with adaptation mechanisms and active camera network coordination. SCOVIS advocates a synergistic approach that combines largely unsupervised learning and model evolution in a bootstrapping process; it involves continuous learning from visual content in order to enrich the models and, inversely, the direct use of these models to enhance the extraction. In the SCOVIS application scenario user interaction will be significantly reduced compared to current methods. The system will be able to calculate the camera spatial relations automatically (self-configuration) for coupled, uncoupled and active cameras. The user will define a set of objects and procedures of interest during a very short supervised learning phase, while the associations with low-level descriptors will be automatically learnt. The resulting models will be significantly enhanced through online data acquisition and unsupervised learning (adaptation). The enhanced models will be able to be verified and potentially adapted through relevance feedback. The main measurable objective of SCOVIS will be to significantly improve the versatility and the performance of current monitoring systems. The resulting technology will enable the easy installation of intelligent supervision systems, which has not been possible so far, due to the prohibitively high manual effort and the inability to model complex visual processes. The produced technology will be evaluated through realistic scenarios related to industry and public infrastructure. The proposed research will be performed with absolute respect to privacy and personal data of monitored individuals.

Primary investigator

• zas

Secondary investigators

• Mark Nixon
• ljm
• gvv

Partners

• NTUA, Greece
• ETHZ, Switzerland
• Atos, Spain

Associated research groups

• IT Innovation Centre
• Vision, Learning and Control

Date:

2011-2013

Themes:

Web Science, Semantic Web, Web of People

Funding:

EUFP7

As part of the FI-PPP programme, ENVIROFI consolidates the Future Internet requirements from the Environmental Usage Area perspective and provides technical specifications and prototypes of interoperable geospatial Environmental Enablers. These shall be deployed in the terrestrial, atmospheric and marine environments in collaboration with large stakeholder communities with the perspective of achieving sustainable socio-economic progress in Europe.

Atmospheric Condition Today, we have easy access to a great deal of information via television, radio and the World Wide Web, including pollution, pollen and meteorological data. All this data contributes to a common sense, but it is not tailored to the individual user’s needs. Future eEnvironment services shall aid users in tailoring information directly relevant to their individual requirements.

Marine Assets Synergies with the market and with policy needs are necessary to deliver significant value added to Europe from its vast marine resources. Enabling technology platforms are currently deployed across a range of existing marine related sectors including shipping, security and logistics, environmental monitoring and offshore energy. Next generation decision based management tools have to dissolve national borders. They shall address these developments in respect to distributed sensing, and wireless and cable comunications.

Biodiversity The UN and the EU have set a new target of halting the loss to biodiversity by the year 2020. In order to meet this goal we must merge observational data on biodiversity from all available sources while assuring high quality. Using outreach groups for data survey, we can greatly widen the base from which observational data may be gleaned. Scenarios on biodiversity occurrence illustrate the use of humans, supported by mobile devices such as smart phones as the main ‘sensor’ for data provision.

Primary investigator

• zas

Partners

• Atos, Spain
• SINTEF, Norway
• Fraunhofer Institute, Germany

Associated research group

• IT Innovation Centre

Date:

2011-2014

Themes:

Disaster Management, Crowd Sourcing, Data Science / Big Data

Funding:

EUFP7

DESURBS developed information tools to assist spatial planning professionals and urban managers to create and maintain safer urban spaces. The scope of the project was to make improvements by contributing new methodologies to aid in planning, design and engineering of urban spaces to make them less vulnerable to security threats. DESURBS had as a primary objective with the creation of a range of databases, tools and approaches that can be re-used, alone or in combination, by urban space stakeholders to create new, safer spaces or to reinforce existing urban infrastructure to make them more secure for people and for the surrounding environment. The range of threats and hazards covered in the project included terror, industrial accidents, crowd control issues such as stampede threat, and natural hazards like earthquakes, flood, landslide and volcanoes.

The consolidative tool of the project is the DESURBS Decision Support System Portal (DSSP) developed by Dr Sabeur's project team at the university of Southampton IT Innovation Centre. The web-based interactive technology was realized to help enable users to distinguish between strengths and weaknesses in urban spaces. This will allow them to recognize, minimize or remove the threats they face. It combines a number of the project’s technologies and results in a user-friendly package targeting urban planners, designers and engineers. The DSSP comprises an integrated security resilience design and assessment framework incorporating supporting tools to engage and support local stakeholders in recognizing weaknesses and enhancing urban spaces that might be subjected to security threats. The portal also contains an evolving urban space security event database that includes incidents with negative or potentially negative consequences, as well as preventive cases that illustrate current best practice.

Other DESURBS tools include modelling and computation based geotechnical strength-of-materials database incorporating failure calibration curves to optimize structural engineering materials decisions; a vulnerability curves database and visualization application for analyzing weak points in buildings and structures subjected to earthquake and blast; and an agent-based dynamic modelling tool for simulating urban catastrophe management scenarios. Additionally, a smart phone applications "MySafe" for crowd perception of safety in urban spaces, as well as two-way communication between authorities and citizens for security reporting have been developed and tested by Dr Sabeur's team. A second tailored security incidents mapping and visualisation tool has been realized in the project in collaboration with the University of Loughborough and Warwick.

Primary investigator

• zas

Secondary investigators

• ljm
• gvv

Partners

• University of Warwick
• University of Loughborough
• University of Crete (Greece)
• Research Management SA (NORWAY)
• CIMNE (Spain)

Associated research group

• IT Innovation Centre

Date:

2010-2013

Themes:

Data Science / Big Data, Intelligent Systems and Machine Learning, Disaster Management

Funding:

EUFP7

One of the most important strategies in 21st Century Earth Management related science and engineering disciplines concerns the integration and implementation of intelligent solutions for sensing the Earth environment, numerically simulating the natural and anthropogenic processes involved and the automated service delivery of extracted knowledge for decision-support. In situ, airborne and space-borne Earth observations which are performed by multiple research and industrial organizations around the world are now generating a large volumes of data and information about Earth processes and eco-systems. Nevertheless, such generated Big data and information cannot be efficiently managed using traditional methods of data storage, access and processing by a large community of multi-disciplinary and collaborative decision makers; particularly those specializing in Critical Earth Management.

Therefore, there is, an urgent need for the deployment of generic knowledge bases and decision-support services in the context of an event driven service architecture. Enhancements for the on-demand availability to large user communities in accordance to their professional requirements for conducting crises as they evolve in time as need to be undertaken. Required actions to mitigate the foreseeable impacts which may occur during crises have to be further refined.

TRIDEC as an Integrated Project – partly funded by the European Commission under the Seventh Framework Programme – focuses on new approaches and technologies for intelligent geo-information management in complex and critical decision-making processes. The key objective in TRIDEC is to design and implement a collaboration infrastructure of interoperable services through which the intelligent management of information and data, dynamically increasing both in terms of size and dimensionality, is critically supported. This will enable multiple decision-makers to respond efficiently using a collaborative decision-support environment.

TRIDEC will establish rapid and on-demand interoperability of inherited legacy applications and tools owned by the project consortium partners. By using collaborative computing techniques TRIDEC enhances the interoperability of the components to establish a decision-support enterprise system of services which can critically deliver timely information to decision-makers.

TRIDEC will be demonstrated in two scenarios. Both involve intelligent management of large volumes of data for critical decision-support. The first scenario concerns a large group of experts working collaboratively in crisis centres and government agencies using sensor networks. Their goal is to make critical decisions and save lives as well as infrastructural and industrial facilities in evolving tsunami crises.

The other scenario concerns a large group of consulting engineers and financial analysts from energy companies working collaboratively in sub-surface drilling operations. Their common objective is to monitor drilling operations in real-time using sensor networks, optimising drilling processes and critically detecting unusual trends of drilling systems functions. This prevents operational delays, financial losses, and environmental accidents and assures staff safety in drilling rigs.

A knowledge-based service framework is deployed for context information and intelligent information management with flexible orchestration of system resources. An adaptive framework for collaborative decision making is enabled with new functions for the support of complex business processes.

Based on a ‘Work Package‘ organization TRIDEC will conduct fundamental research as well as component and system development. Basic research will aim at new approaches for the architecture and service integration of crisis management systems with special emphasis on robustness and fault tolerance. Complementary research will focus on new approaches for the design and intelligent retrieval of knowledge-bases comprising among others historic data, prognostic models, and rules. Together with information about the actual status of the underlying sensor systems will this enable the development of new, effective, and efficient tools for decision-support processes in critical crisis situation with evolving conditions.

Primary investigator

• zas

Secondary investigators

• ljm
• gvv
• baz
• Stuart Middleton
• Zlatko Zlatev
• gc3

Partners

• GeoPhysics Centre, Postdam, Germany
• Fraunhofer Institute, IOSB, Germany
• Kandilli Earthquake Observatory, Turkey

Associated research group

• IT Innovation Centre

Date:

2013-2016

Funding:

European Commission

INFRARISK is a three-year EU funded FP7 project to develop a stress test framework to tackle cascading impacts of natural hazards on interdependent infrastructure networks through: •Identifying rare low-frequency natural hazard events, which have the potential to have extreme impacts on critical infrastructure. •Developing a stress test structure for specific natural hazards on CI networks and a framework for linear infrastructure systems with wider extents and many nodal points (roads, highways and railroads), though it is anticipated the outputs can be applied across a variety of networks (e.g telecom and energy)/ •An integrated approach to hazard assessment considering the interdependencies of infrastructure networks, the correlated nature of natural hazards, cascading hazards and cascading effects, and spatial and temporal vulnerability. •Facilitate implementation through the development of GIS based and web based stress test algorithms for complex infrastructure networks. •Testing the framework developed through simulation of complex case studies. •Exploitation strategies aimed at disseminating the 'knowledge' and not just the results (e.g training courses to industry, academic and media parties).

The methodological core of the project is based on the establishment of an 'overarching methodology' to evaluate the risks associated with multiple infrastructure networks for various hazards with spatial and temporal correlation. Interdependency will be formalised and damage will be defined in terms of capacity decrements. This will be the basis for the development of stress tests for multi-risk scenarios and will define the general framework, providing a tool for decision making based on the outcome of the stress test. Our research team at IT innovation Centre has a leading role in the implementation strategy of the project. The main objective goal is to design and develop a strategic INFRARISK Decision Support Tool (IDST) to ensure that the INFRARISK stress tests and the harmonised risks management methodologies and analytics modules are integrated and driven by an intelligent process workflow engine. The IDST platform is currently well advanced to provide risk management of natural hazards on critical infrastructure and access to critically harmonized data and information. The ability to predict the vulnerability and state of damage of large infrastructure from the element and rare events can now be assessed. The IDST is being validated in Italy and Spain.

Primary investigator

• zas

Partners

• Roughan and Donaghue (Ireland)
• University College london (UK)
• ETHZ, Switzerland

Associated research group

• IT Innovation Centre