Learning a Mixture of Deep Networks for Single Image Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices. The source codes are available in http://www.ifp.illinois.edu/~dingliu2/accv2016

Separated boundary layer transition under pressure gradient in the presence of free-stream turbulence

Large-eddy simulation (LES) has been carried out to investigate the transition process of a separated boundary layer on a flat plate. A streamwise pressure distribution is imposed to mimic the suction surface of a low-pressure turbine blade, and the free-stream turbulence intensity at the plate leading edge is 2.9%. A dynamic subgrid scale model is employed in the study, and the current LES results compare well with available experimental data and previous LES results. The transition process has been thoroughly analyzed, and streamwise streaky structures, known as the Klebanoff streaks, have been observed much further upstream of the separation. However, transition occurs in the separated shear layer and is caused by two mechanisms: streamwise streaks and the inviscid K-H instability. Analysis suggests that streamwise streaks play a dominant role in the transition process as those streaks severely disrupt and break up the K-H rolls once they are formed, leading to significant three-dimensional (3D) motions very rapidly. It is also demonstrated in the present study that the usual secondary instability stage under low free-stream turbulence intensity where coherent two-dimensional (2D) spanwise rolls get distorted gradually and eventually broken up into 3D structures has been bypassed.N/

Assessing Ageing Condition of Mineral Oil-Paper Insulation by Polarization/Depolarization Current

Accurately assessing the ageing status of oil-paper insulation in transformer is essential and important. Polarization and Depolarization Current (PDC) technique is effective in assessing the condition of oil-paper insulation system. Though the PDC behaviour of mineral oil-paper insulation has been widely investigated, there is no report about how to make the quantitative analysis of mineral oil-paper insulation ageing condition by PDC. The PDC characteristics of mineral oil-paper insulation samples were investigated over the ageing period at 110°C. A new method for assessing the ageing condition of mineral oil-paper insulation by calculating the depolarization charge quantity was proposed. Results show that the depolarization charge quantity of mineral oil-paper insulation sample is very sensitive to its ageing condition. The stable depolarization charge quantity could be used to predict the ageing condition of mineral oil-paper insulation