Yunjie Yang is a University of Edinburgh doctoral graduate, having received his PhD in Engineering Electronics from the University in 2018. He is a specialist in tomography, a technique for creating images of solid objects by ‘slicing’ them with various penetrating waves, such as X-rays, electrical pulses, or acoustic waves, and combining the ‘slices’ into composite images. Dr Yang’s work at the Intelligent Sensing Laboratory brings a wide range of tomographic methods together with machine learning. The aim is to create better visualisations of complex industrial and biomedical processes, and then to use these images to improve system control and fault detection.
This kind of imaging requires specialist equipment that is often large and more or less immobile. But Dr Yang is exploring sensing and imaging for industrial and biomedical applications with ‘agile’ tomography, which, as the name suggests, is more flexible and mobile. Agile tomography includes the development of methods that might enable a patient to be given a scan at home instead of having to travel to a hospital, or help technicians map the flow of oil at a potential weak spot in a pipeline. Dr Yang describes his main challenge as how to “efficiently utilise and interpret enormous volumes of sensing/imaging data generated from various sensors or tomography systems, and ultimately link them to direct process information to facilitate improvement of product quality, reduction of cost and emission and optimisation of management.”
As with all Data-Driven Innovation, agile tomography — particularly when combined with machine learning — opens up vast possibilities. Continuously evolving data science and innovation provide unprecedented methodologies that can be used to portray the complex relations between raw signals taken from sensor networks and key information within fast-changing processes. “The primary objective of my research is to advance data-driven methods in this specific area, and apply them to benefit research and real industries,” Dr Yang adds. It’s no surprise, then, that his research has generated over 50 peer-reviewed journal and international conference publications in a relatively short space of time. And Dr Yang seems to take the agile ethos to heart, adding to his busy schedule of research and teaching a portfolio that includes extensive peer review, international scientific committee work, and an Associate Editorship of the IEEE Access journal.
The primary objective of my research is to advance data-driven methods in this specific area, and apply them to benefit research and real industries