Implementing a hybrid spatial discretisation within an agent based evacuation model

Within all evacuation and pedestrian dynamics models, the physical space in which the agents move and interact is represented in some way. Models typically use one of three basic approaches to represent space namely a continuous representation of space, a fine network of nodes or a coarse network of nodes. Each approach has its benefits and limitations; the continuous approach allows for an accurate representation of the building space and the movement and interaction of individual agents but suffers from relative poor computational performance; the coarse nodal approach allows for very rapid computation but suffers from an inability to accurately represent the physical interaction of individual agents with each other and with the structure. The fine nodal approach represents a compromise between the two extremes providing an ability to represent the interaction of agents while providing good computational performance. This dissertation is an attempt to develop a technology which encompasses the benefits of the three spatial representation methods and maximises computational efficiency while providing an optimal environment to represent the movement and interaction of agents. This was achieved through a number of phases. The initial part of the research focused on the investigation of the spatial representation technique employed in current evacuation models and their respective capabilities. This was followed by a comprehensive review of the current state of knowledge regarding circulation and egress data. The outcome of the analytical phases provided a foundation for eliciting the failings in current evacuation models and identifying approaches which would be conducive towards the sophistication of the current state of evacuation modelling. These concepts led to the generation of a blueprint comprising of algorithmic procedures, which were used as input in the implementation phase. The buildingEXODUS evacuation model was used as a computational shell for the deployment of the new procedures. This shell features a sophisticated plug-in architecture which provided the appropriate platform for the incremental implementation, validation and integration of the newly developed models. The Continuous Model developed during the implementation phase comprises of advanced algorithms which provide a more detailed and thorough representation of human behaviour and movement. Moreover, this research has resulted in the development of a novel approach, called Hybrid Spatial Discretisation (HSD), which provides the flexibility of using a combination of fine node networks, coarse node networks and continuous regions for spatial representations in evacuation models. Furthermore, the validation phase has demonstrated the suitability and scalability of the HSD approach towards modelling the evacuation of large geometries while maximising computational efficiency

Implementing a Chatbot Music Recommender System Based on User Emotion

The use of chatbots has become increasingly popular in recent years, as more organisations try to improve and streamline their customer service operations. One area which has been gaining momentum is the use of chatbots for music recommendation. Such systems utilise AI technologies to deliver personalised music recommendations to users via conversational interfaces. Chatbot music recommender systems present several benefits namely; they provide a personalised and natural experience which can be engaging for the users. Moreover, the users can engage in a dialogue whereby the system can better interpret the user context and preferences. This work presents the development of a chatbot personalised music recommender system, based on Natural Language Processing (NLP) techniques, coupled with a web interface that can provide song recommendations based on the user’s emotions

Real-Time Customer Emotion Analysis in E-Commerce based on Social Media Data: Insights and Opportunities

In this era of social media, it's essential for businesses to monitor their customers options and feelings regarding their services and products in a timely manner. Due to the ease of sharing opinions and feedback on social media, the customers can share their reviews about the business or a product instantly. This feedback can have a significant impact on the business's reputation and in turn on its revenue. In this regard, sentiment analysis has developed into a vital tool that companies can use to comprehend the emotional factors that influence client behavior and to aid them in making decisions that will increase customer pleasure. This work presents the use of social media data for real-time consumer emotion analysis in e-commerce. The study aims to identify the most expressed emotions and provide businesses with the ability to tailor their product and services accordingly. The employed dataset consists of 58,000 English comments that have been labelled for 27 different emotion categories. The study uses machine learning methods to categorize the emotions expressed in the comments, including convolutional neural networks and Bidirectional Encoder Representations from Transformers (BERT). The practical result of this research shows the importance of machine learning model coupled with a user interface that can provide stakeholders, such as e-commerce companies, with insights into consumer emotion as well as realtime customer sentiment about their goods and services

Investigating the application of a hybrid space discretisation for urban scale evacuation simulation

The devastating effects of wildfires cannot be overlooked; these include massive resettlement of people, destruction of property and loss of lives. The considerable distances over which wild fires spread and the rates at which these fires can spread is a major concern as this places considerable challenges on the evacuation mechanisms that need to be put in place. It is therefore crucial for personnel, involved in evacuation planning, to obtain reliable estimates of evacuation times faster than real time, to assist their decision making in response to actual unfolding of events. In this work, we present a hybrid approach, which we refer to as the Hybrid Spatial Discretisation (HSD) for large scale evacuation simulation. The HSD integrates the three spatial representation techniques typically used for representing space usage in evacuation models; namely Coarse regions, Fine nodes and Continuous regions. In this work, we describe the core models constituting the HSD coupled with the approaches used for representing the transition of agents across the different spatial types. Using a large scale case, we demonstrate how the HSD can be used to obtain higher resolution of results where it is most required while optimising the use of available computational resources for the overall simulation. The HSD is seen to provide improvements in run times of more than 40% when compared to modelling the whole area using just the Fine node method

Increasing the simulation performance of large-scale evacuations using parallel computing techniques based on domain decomposition

Evacuation simulation has the potential to be used as part of a decision support system during large-scale incidents to provide advice to incident commanders. To be viable in these applications, it is essential that the simulation can run many times faster than real time. Parallel processing is a method of reducing run times for very large computational simulations by distributing the workload amongst a number of processors. This paper presents the development of a parallel version of the rule based evacuation simulation software buildingEXODUS using domain decomposition. Four Case Studies (CS) were tested using a cluster, consisting of 10 Intel Core 2 Duo (dual core) 3.16 GHz CPUs. CS-1 involved an idealised large geometry, with 20 exits, intended to illustrate the peak computational speed up performance of the parallel implementation, the population consisted of 100,000 agents; the peak computational speedup (PCS) was 14.6 and the peak real-time speedup (PRTS) was 4.0. CS-2 was a long area with a single exit area with a population of 100,000 agents; the PCS was 13.2 and the PRTS was 17.2. CS-3 was a 50 storey high rise building with a population of 8000/16,000 agents; the PCS was 2.48/4.49 and the PRTS was 17.9/12.9. CS-4 is a large realistic urban area with 60,000/120,000 agents; the PCS was 5.3/6.89 and the PRTS was 5.31/3.0. This type of computational performance opens evacuation simulation to a range of new innovative application areas such as real-time incident support, dynamic signage in smart buildings and virtual training environments

The simulation of urban-scale evacuation scenarios with application to the Swinley forest fire

Forest fires are an annual occurrence in many parts of the world forcing large-scale evacuation. The frequent and growing occurrence of these events makes it necessary to develop appropriate evacuation plans for areas that are susceptible to forest fires. The buildingEXODUS evacuation model has been extended to model large-scale urban evacuations by including the road network and open spaces (e.g. parks, green spaces and town squares) along with buildings. The evacuation simulation results have been coupled with the results of a forest fire spread model and applied to the Swinley forest fire which occurred in Berkshire, UK in May 2011. Four evacuation procedures differing in the routes taken by the pedestrians were evaluated providing key evacuation statistics such as time to reach the assembly location, the distance travelled, congestion experienced by the agents and the safety margins associated with using each evacuation route. A key finding of this work is the importance of formulating evacuation procedures that identifies the threatened population, provides timely evacuation notice, identifies appropriate routes that maintains a safe distance from the hazard front thereby maximising safety margins even at the cost of taking longer evacuation routes. Evacuation simulation offers a means of achieving these goals

Applicability of Federated Learning for Securing Critical Energy Infrastructures

Energy grids are becoming more intelligent due to the use of a vast array of technologies, including the Internet of Things and Intelligent Systems. These Critical Energy Infrastructures, which are essentially cyber-physical systems, are particularly vulnerable to cyber threats. Machine Learning (ML) techniques have been increasingly used in security applications, and the energy domain is no exception. One approach, in particular, Federated Learning (FL), employs a distributed architecture and has potential in security applications, as it counters the issue of having a centralized data warehouse. In this work, a review of FL and its applications in security and privacy are presented. Moreover, a demonstration case involving a simulated model of FL for enhancing the security of systems is implemented and discussed. This demonstration case has provided added insight into potential issues and challenges as well as mitigation strategies

A Comprehensive Review of Mobile User Interfaces in mHealth applications for elderly and the related ageing barriers

Purpose: The adoption of mobile health technology can assist in enhancing the quality of life of elderly people. Over the last few years, though mHealth research has expanded, the usage of mHealth applications among elderly is still minimal. This study aims to evaluate mobile User Interfaces in mHealth applications and elicit the key ageing barriers that limit the use of such apps amongst the elderly. Methods: The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) technique was used whereby 28 papers were identified and examined out of 742. In addition to the systematic review, an experiment of using 5 existing mHealth apps, was conducted with 10 individuals within the age-group of 60-79 to determine additional ageing barriers and usage challenges. The Questionnaire for User Interface Satisfaction (QUIS) approach was used to prepare a questionnaire to assess the overall system satisfaction and was provided to participants after one week of usage. Results: In this work, issues with the user interface that impact the elderly have been highlighted. Three important ageing barriers hindering the use of mHealth among the elderly have been identified via PRISMA, namely: physical, cognitive and perspective. Empirical findings from the experiment carried out further consolidate the findings obtained from the PRISMA approach. Conclusion: This study's investigation emphasised on the performance of older persons with mHealth apps, their needs, and the challenges they confront when adopting mHealth technologies. As a result, technology designers will benefit from this information when developing and designing mHealth apps and services that are suitable for older adults

An agent based evacuation model utilising hybrid space discretisation

Egress models typically use one of three methods to represent the physical space in which the agents move: coarse network, fine network or continuous. In this work, we present a novel approach to represent space, which we call the 'Hybrid Spatial Discretisation' (HSD), in which all three spatial representations can be utilised to represent the physical space of the geometry within a single integrated software tool. The aim of the HSD approach is to encompass the benefits of the three spatial representation methods and maximise computational efficiency while providing an optimal environment to represent the movement and interaction of agents