Prof. Yu-Dong Zhang
Title: Advanced neural networks for COVID-19 diagnosis
Bio:
Prof. Yu-Dong Zhang received a Ph.D. degree in Signal and Information Processing from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, USA, and as an Assistant Research Scientist from 2012 to 2013 with the Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as a Professor at the School of Computing and Mathematical Sciences, University of Leicester, UK. He is Fellow of IET, Fellow of EAI, Fellow of BCS, Senior Members of IEEE, IES, and ACM, and Distinguished Speaker of ACM. He was included in Most Cited Chinese Researchers (Computer Science) by Elsevier from 2014 to 2018. He was the 2019 & 2021 recipient of Clarivate Highly Cited Researcher.
Abstract:
COVID-19 is a pandemic disease that caused more than 6.25 million deaths until 10/May/2022. A CT scan is a medical imaging technique used in radiology to get detailed images of the body noninvasively for diagnostic purposes. This talk will discuss the advanced neural networks for chest CT-based COVID-19 diagnosis. This talk will cover other chest-related diseases: secondary pulmonary tuberculosis and community-acquired pneumonia.
Dr. Zhan Li
Title: Task-constrained and fault-tolerant motion planning and control for robotic manipulators
Bio:
Dr. Zhan Li received his Ph.D. degree in rehabilitation robotics from University of Montpellier and LIRMM/INRIA, France. Dr Zhan Li’ main interests include AI, robotics, intelligent control and rehabilitation engineering. He is an Editorial Board Member of PLOS One, the Guest Editor of Frontiers in Neurorobotics, Frontiers in Neuroscience and Journal of Healthcare Engineering, and serves as IEEE RAS Technical Committee Member on Collaborative Automation for Flexible Manufacturing.
Abstract:
Nowadays, industrial robotic manipulators have been playing important roles in manufacturing fields, such as welding and assembling, by performing repetitive and dull work. Such long-term industrial operations usually require redundant manipulators to keep good working conditions and maintain the steadiness of joint actuation. However, some joints of redundant manipulators may fall into fault status after enduring long-period heavy manipulations, causing the desired industrial tasks cannot be accomplished accurately. We propose a novel fault-tolerant method with a simultaneous fault-diagnose function for motion planning and control of industrial redundant manipulators. The proposed approach can adaptively localize which joints run away from the normal state to be the fault, and it can guarantee to finish the desired path tracking control even when these fault joints lose their velocity to actuate. Simulation and experiment results on a Kuka LBR iiwa manipulator demonstrate the efficiency of the proposed fault-tolerant method for motion control of the redundant manipulator.