An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.

An Information Visualisation Tool for Emergency Management

<p>The development of computerised information systems for large scale emergency management is lacking. These systems could present information and support information transfer across shifts. This is important as providing timely information is critical for efficient search and rescue operations in an emergency environment. This thesis contributes the design and prototype implementation for an interactive visualisation, called RescueTime, which is then evaluated. The evaluation showed that RescueTime is as effective as a traditional tool used by emergency managers. This demonstrates the feasibility of designing and developing larger information systems, for the purpose of emergency management.</p>

An Information Visualisation Tool for Emergency Management

<p>The development of computerised information systems for large scale emergency management is lacking. These systems could present information and support information transfer across shifts. This is important as providing timely information is critical for efficient search and rescue operations in an emergency environment. This thesis contributes the design and prototype implementation for an interactive visualisation, called RescueTime, which is then evaluated. The evaluation showed that RescueTime is as effective as a traditional tool used by emergency managers. This demonstrates the feasibility of designing and developing larger information systems, for the purpose of emergency management.</p>

VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

The use of machine learning rapidly increases in high-risk scenarios where decisions are required, for example in healthcare or industrial monitoring equipment. In crucial situations, a model that can offer meaningful explanations of its decision-making is essential. In industrial facilities, the equipment's well-timed maintenance is vital to ensure continuous operation to prevent money loss. Using machine learning, predictive and prescriptive maintenance attempt to anticipate and prevent eventual system failures. This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.

Interactive Visualisation of Sustainability Indicators for Water, Energy and Food Innovations

The Water-Energy-Food (WEF) nexus describes the synergies and trade-offs between water, energy and food. Despite the significant attention that the WEF nexus has received in recent years, challenges remain, primarily related to gaps in integrated data, information and knowledge related to the most critical inter-linkages and their dynamics. These WEF nexus complexities and uncertainty make decision-making and future forecasting extremely difficult. Policy makers and other stakeholders are currently faced with the task of understanding longer term environmental impacts and tJhe benefits and limitations of innovations that could be potentially beneficial, such as Anaerobic Digestion as a waste solution or insect protein production. This paper describes an approach to support decision making for local-level innovations within the WEF nexus by creating a set of sustainability indicators and an accompanying interactive visualisation. The indicators were derived from stakeholder consultation processes and workshops, and they were selected to include a much broader assessment than just financial aspects when considering the viability of such innovations. By taking this bottom-up approach and placing stakeholders at the heart of the project, we produced a visualisation tool to support sustainable decision making when considering the implementation of WEF innovations. Considering other, often overlooked factors and giving greater priority to these deepens knowledge and the recognition of influential issues that in conventional processes may be overlooked. This visualisation tool is designed to support decision makers to engage in a exploration of the different interlinkages, and to be the basis of stakeholder dialogue around sustainability. The visualisation tool developed was designed to be easily modifiable in order to be updated with new insights and to include other future innovations.

Prototype sea level planning and scenario visualization tool

Development of a prototype data-driven modelling and visualisation tool to be tested with selected stakeholders. The prototype will be used to create a roadmap for visualising data leading to better coastal resilience decisions in the management of future sea level rise. The deliverable will include a brief report.

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology and visualisation tool based on data analytics that allows the practitioner to anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine's settings are needed (tool replacement, repositioning the axis, etc). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.