About the Project
Traditional manual inspections of wind turbines, which requires inspectors to climb on the turbines, is unsafe, high cost, and limited human vision. A mini-Drone is a promising solution for breaking the limited human vision and improving the safety, while a mini-Drone suffers from two limitations: 1) limited battery life; 2) unstabilized video capturing caused by strong wind in the off-shore area. To tackle these issues, the concept of distributed multi-agent systems will be introduced to overcome the limitation of battery life and novel video stabilization techniques will be developed for detecting defects effectively. Collaboration with the Wind Turbine industry is also anticipated
Aim and Objectives:
The aim of the project is to develop vision-based autonomous inspection and monitoring techniques with considering the characteristics of multiple mini-Drones (e.g. unstabilized videos).
To achieve the aim, the following objectives are listed:
· To develop a video stabilization algorithm for obtaining stabilized videos from mini-Drones.
· To fuse information from multiple mini-Drone for reconstructing 3D model.
· To conduct autonomous inspections by using deep learning approaches (e.g. vision-based defects detection)
This project is also anticipated to collaborate with the Wind Turbine industry.
Candidates should have (or expect to achieve) a UK honours degree at 2.1 or above (or equivalent) in Computer Science, Statistics, Electrical and Electronic Engineering, Systems Engineering or equivalent experience.
We are looking for a PhD student, who has a strong interest in computer vision, machine learning, and data fusion. Ideally, the successful applicant should have a good understanding of Python, C or MATLAB programming. The PhD will involve developing machine learning techniques. The successful candidate is also expected to be an enthusiastic team player who can work both independently and communicate effectively with others.
It may be possible to undertake this project on a distance learning basis. If you are interested in this option you should discuss it with Dr Yi.
Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• Apply for Degree of Doctor of Philosophy in Computing Science
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV, Personal Statement and Intended source of funding
Informal inquiries can be made to Dr D Yi (Dewei.Yi@abdn.ac.uk), with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School (email@example.com)
The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website.
THERE IS NO FUNDING ATTACHED TO THIS PROJECT
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