Speaker — Ning Chu (Institute of process equipment, College of Energy Engineering, Zhejiang University (Hangzhou, China))
Abstract — Remote monitoring and early warning of thermal source abnormality play more and more important roles in fire prevention for the museums and historical monuments (Notre dame de Paris e.g.), metro and electric vehicle (Tesla e.g.) etc. However, conventional thermal imaging techniques cannot obtain the accurate temperature distribution of thermal sources in the far-fields. This is due to the fact that true temperature of thermal sources, according to heat radiation model, depends on many complex factors such as background temperature, environment humidity and surface emissivity . To solve the above challenge, we propose a Bayesian deep learning approach in thermal remote imaging with hyper-resolution. And mixture Gaussian priors are employed to model the temperature distribution of thermal sources, as well as background temperature. Meanwhile, sparsity-enforcing prior of temperature gradient is also utilized for spatial hyper-resolution. Moreover, the environment humidity and surface emissivity in heat radiation model can be studied by latent variables in Bayesian Hierarchy Network, so that these two important parameters can be estimated by maximizing the entropy of variational Bayesian inference. Through this Bayesian deep learning framework (sampling-training-updating), temperature mapping of hot sources can be accurately obtained (about 0.5 degree Celsius variation) as far as 5-10 meters way through a cost-effective infra-red camera (<100 Euros, 7 degree Celsius variation ) . Even without knowing the exact environment information, proposed approach is able to learn rapidly from remote monitoring data about heat radiation parameters. Based on proposed approach, a carry-on system of remote thermal imaging system has been invented for monitor the abnormal heating in metro system in Guangzhou city China.
Biography — Ning Chu received the Bachelor in information engineering from the National University of Defense Technology in 2006. He obtain the master and PhD in automatic signal, and image processing from the University of Paris Sud, France in 2010 and 2014 respectively. He then won the positions of scientific collaborator in École Polytechnique Fédérale de Lausanne, Switzerland, and senior lecturer in Zhejiang Unviersity. His research interests mainly focus on acoustic source imaging, Bayesian deep learning in condition monitoring and inverse problem applied in super resolution imaging. He has published more than 22 peer-reviewed journal papers, invited for lectures by top international scientific conferences, own 5 China patents and 6 software copyrights.