DCU-Final-Year-Projects-Booklet-2025

96 192. IdentifyingKey Input Data for Accurate Building Energy Performance Simulations This project focuses on defining the essential input data needed for accurate Building Energy Performance Simulation (BEPS) models. Creating BEPSmodels that predict energy usage with high accuracy can be complex and time-consuming, often involving assumptions about required data. This project aims to develop amethodology to determine the most crucial input data for reliable simulations, addressing the challenge of applying regional rules of thumb to different geographic areas. The project will use sensitivity analysis on data from typical dwellingmodels to identify which parameters influence model accuracy best. The goal is to establish clear guidelines for selecting the most impactful input data, minimising uncertainty, and enhancing the precision of energy performance predictions. Student Programme Mechanical andManufacturing Engineering (Year 4) Project Area Energy Conservation, Simulation, Statistical Analysis, Environmental Mapping Project Technology Excel/VB, DesignBuilder Student Name(s) Cormac Byrne Email cormac.byrne343@mail.dcu.ie Supervisor Dr Reihaneh Aghamolaei 193. MelaScanX – ExploringMachine Learning for Skin Lesion Analysis MelaScanX is a web application developed by students to explore the application of machine learning in skin lesion analysis. This research-focused project leverages image processing andmachine learning algorithms to analyse uploaded images of moles and skin lesions, investigating the potential of ML in identifying visual patterns. The user-friendly interface allows for easy image uploads and provides feedback on detected features. MelaScanX also offers appointment scheduling with dermatologists and collaborates with skincare companies. The project highlights the intersection of computer science and healthcare technology while prioritising data privacy and security. It does not offer medical diagnoses or advice, serving purely as a research platform for machine learning applications. Student Programme Computing for Business Project Area Artificial Intelligence, Computer Vision, Image/Video Processing, Web Application Project Technology CSS, HTML5, JavaScript, MySQL, Python, Machine Learning Student Name(s) Erika Trifonova  |  Kristina Ladnova Email erika.trifonova2@mail.dcu.ie   |  kristina.ladnova2@mail.dcu.ie Supervisor Dr AndrewMcCarren 194. CAM415 – 3DPrinted Electronic Cymbal This project involved the designing and 3D printing of a prototype electronic cymbal that allows for testing with a variety of sensor configurations. It involved the printing and testing of smaller test pieces to determine the most suitable material/s and geometries. It also involved testing of the prototype and testing of a commercial cymbal for comparison. The sensors used were simple piezoelectric sensors. Student Programme Mechanical andManufacturing Engineering (Year 4) Project Area 3-DModelling, AdditiveManufacturing, Arduino, Sensor Data Project Technology ANSYSWorkbench, Excel/VB, Solidworks Student Name(s) Aidan O’Connor Email aidan.oconnor294@mail.dcu.ie Supervisor Dr Alan Kennedy