DCU Expo 2024 - Final Year Projects

44 42. Data-Driven Traffic Flow in Dublin: AMachine Learning Perspective This project investigates traffic flow on Dublin’s M1motorway system. Through the use of time series analysis andmachine learning techniques, the aim is to improve traffic forecasting accuracy. The project utilises traffic sensor data sourced fromTransport Infrastructure Ireland, as well as incorporating external factors such as weather forecasts. While concerned about road traffic deaths and the environmental impact that congestion can lead to, the focus is also on road users and finding the optimal time to begin a journey. This project looks to tackle two problems; defining traffic congestion via vehicle counts at various locations, and building a prediction model that can output accurate predictions for a user. Student Programme Data Science Project Area Artificial Intelligence, Data Analytics, Sensor Data, Statistical Analysis, Intell Transport System Project Technology Python, Machine Learning Student Name(s) Seán O’Neill  |  Robert Sparks Email sean.oneill96@mail.dcu.ie  |   robert.sparks2@mail.dcu.ie Supervisor Dr Suzanne Little 43. KickOff: Wearable, Flexible Sensing Tool toMeasure Ball and Foot Impact This project aims to create a boot-mounted sensor array that can be used in training tomeasure the ball-to-foot impact of a player’s kick. The array was created using triboelectric nanogenerator units, largely created from3D printedmaterials to reduce costs and increase the speed of creation. The unit is designed to be capable of measuring both force and location of the impact to assist technique analysis during player training. Student Programme Mechatronic Engineering (Year 5) Project Area Arduino, Sensor Data, Wearable Technology Project Technology C/C++ Student Name(s) Conor Furniss Email conor.furniss2@mail.dcu.ie Supervisor Dr Shirley Coyle 44. Experimental SkinModels to Reproduce Adhesion Characteristics This project was developed to investigate the peel rate of adhesives on different substrates that are comparable to the skin in terms of their mechanical behaviour. Throughout the course of this project, different substrates were developed, including stainless steel 316L, silicone, and polyurethane, and were tested against existing knowledge of the skin to determine the difference in reaction to peel tests using several different pressure-sensitive adhesives. Changes in the dwell time and peel rate were tested on each substrate. Ultimately, the research conducted on this synthetic model skin will benefit future research on the usability and efficiency of pressure-sensitive adhesives while not using humans for testing purposes. Student Programme Biomedical Engineering (Year 4) Project Area Biomedical Engineering, Materials Testing Project Technology Excel/VB, Solidworks Student Name(s) EmmaMoriarty Email emma.moriarty7@mail.dcu.ie Supervisor Dr Garrett McGuinness