Intelligent Visual Data Exploration and Analytics for Automotive Engineering
Lead Research Organisation:
University of Brighton
Department Name: Sch of Computing, Engineering & Maths
Abstract
The project aims to create a visual analytic tool facilitating the exploration of tremendous volumes of automotive engineering experimental data (e.g. filtering data sets and examining statistics), integrated with a machine learning approach, thereby enhancing the productivity of engineers or researchers. As a specific timely and vitally important application, it will propose a predictive model of low-speed pre-ignition, a recently identified abnormal combustion phenomena that predominantly affects state-of-the-art boosted, downsized, gasoline engines (that are currently seen as the most viable option for achieving emissions targets).
Organisations
People |
ORCID iD |
Andrew Fish (Primary Supervisor) | |
Maria Diapouli (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N509607/1 | 30/09/2016 | 30/03/2023 | |||
1793433 | Studentship | EP/N509607/1 | 02/10/2016 | 01/10/2019 | Maria Diapouli |