Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Reduced-order electro-thermal models for computationally efficient thermal analysis of power electronics modules

Silicon and Silicon Carbide-based power module are common in power electronic systems used in a wide range of applications, including renewable energy, industrial drives and transportation. Reliability of power electronics converters is very important in many applications. It is well known that reliability and ultimately the lifetime of power modules is affected by the running temperature during power cycles. Although accurate thermal models of power electronics assemblies are widely available, based e.g. on computational fluid dynamics (CFD) solvers, their computational complexity hinders the application in real-time temperature monitoring applications. In the thesis, geometry-based numerical thermal models and compact thermal models will be developed to address the fast thermal simulation in the electronic design process and real-time temperature monitoring, respectively. Accurate geometry-based mathematical models for dynamic thermal analyses can be established with the help of finite difference methods (FDM). However, the computational complexity result from the fine mesh and large dimension of ordinary differential equations (ODE) system matrix makes a drawback on the analysis in parametric studies. In this thesis, a novel multi-parameter order reduction technique is proposed, which can significantly improve the simulation efficiency without having a significant impact on the prediction accuracy. Based on the block Arnoldi method, this method is illustrated by referring to the multi-chip power module connected with air-force cooling system including plate-fin heatsink. In real-time temperature monitoring, more compact tools might be preferable, especially if operating and boundary conditions such as losses and cooling are now known accurately, as it’s often the case in practical applications. Compared with geometry-based model which is more suitable in the design of power modules, lumped parameter thermal compact model is simpler and can be applied in real-time temperature prediction during the power cycles of power modules. This thesis proposes a reduced order state space observer to minimize the error caused by air temperature and air flow rate. Additionally, a novel feedback mechanism for disturbance estimation is introduced to compensate the effect result from the error of input power loss, air flow and changes of other nonlinearities

Modelling And Optimization Of Micro-Channel And Thermal Energy Storage Heatsinks For Microelectronic Devices [TK7874. J44 2007 f rb].

Pemodelan dan pengoptimuman penyerap haba mikro-alur dan penyerap haba muatan dikaji dalam penyelidikan sini. Penyerap haba mikro-alur merupakan teknologi penyejukan yang berkesan untuk menyingkirkan tenaga haba yang tinggi daripada kawasan yang kecil dan terhad di dalam perkakasan mikroelektronik. The modelling and optimization of micro-channel and Thermal Energy Storage (TES) heatsinks in electronic cooling are investigated in the present study. The microchannels heatsinks is an efficient cooling technology to remove large amount of heat from very small and constrained areas of the high heat flux of microelectronic devices