National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Combustion characteristics in a supersonic combustor with hydrogen injection upstream of cavity flameholder

Abstract: Combustion characteristics in a supersonic combustor with hydrogen injection upstream of a cavity flameholder were investigated both experimentally and numerically. The combustion was observed to be stabilized in the cavity mode around the shear layer via a dynamic balance and then spread into the main stream in the region around the jet centerplane where the flow was decelerated and turned to the main stream, supplying a favorable condition for the combustion to spread. The combustion spreading from the cavity shear layer to the main stream seemed to be dominated not only by the traditional diffusion process but also by the convection process associated with the extended recirculation flows resulting from the heat release and the interaction between the jet and the cavity shear layer. Therefore, the cavity-stabilized combustion appeared to be a strongly coupled process of flow and heat release around the cavity flameholder.

Towards photodetection with high efficiency and tunable spectral selectivity: graphene plasmonics for light trapping and absorption engineering

Abstract: Plasmonics can be used to improve absorption in optoelectronic devices and has been intensively studied for solar cells and photodetectors. Graphene has recently emerged as a powerful plasmonic material. It shows significantly less loss compared to traditional plasmonic materials such as gold and silver and its plasmons can be tuned by changing the Fermi energy with chemical or electrical doping. Here we propose the use of graphene plasmonics for light trapping in optoelectronic devices and show that the excitation of localized plasmons in doped, nanostructured graphene can enhance optical absorption in its surrounding medium including both bulky and two-dimensional materials by tens of times, which may lead to a new generation of photodetectors with high efficiency and tunable spectral selectivity in the mid-infrared and THz ranges.

Globally Consistent Reconstruction of Ripped-Up Documents

Abstract: One of the most crucial steps for automatically reconstructing ripped-up documents is to find a globally consistent solution from the ambiguous candidate matches. However, little work has been done so far to solve this problem in a general computational framework without using application-specific features. In this paper, we propose a global approach for reconstructing ripped-up documents by first finding candidate matches from document fragments using curve matching and then disambiguating these candidates through a relaxation process to reconstruct the original document. The candidate disambiguation problem is formulated in a relaxation scheme in which the definition of compatibility between neighboring matches is proposed, and global consistency is defined as the global criterion. Initially, global match confidences are assigned to each of the candidate matches. After that, the overall local relationships among neighboring matches are evaluated by computing their global consistency. Then, these confidences are iteratively updated using the gradient projection method to maximize the criterion. This leads to a globally consistent solution and, thus, provides a sound document reconstruction. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the reconstruction of ripped-up documents up to 50 pieces is possibly accomplished automatically.

Unsupervised Representation Learning with Deep Convolutional Neural Network for Remote Sensing Images

Abstract: With the rapid growth in quantity and quality of remote sensing images, extracting the useful information in them effectively and efficiently becomes feasible but also challenging. Convolutional neural network (CNN) is a suitable method to deal with such challenge since it can effectively represent and extract the information. However, the CNN can release their potentials only when enough labelled data provided for the learning procedure. This is a very time-consuming task and even infeasible for the applications with non-cooperative objects or scenes. Unsupervised CNN learning methods, which relieve the need for the labels in the training data, is a feasible solution for the problem. In this work, we investigate a real-world motivated sparsity based unsupervised deep CNN learning method. At first, the method formulates a balanced data driven population and lifetime sparsity prior and thus construct the unsupervised learning method through a layerwise mean. Then we further perform the method on the deep model with multiple CNN layers. Finally, the method is used for the remote sensing image representation and scenes classification. The experimental results over the public UC-Merced Land-use dataset demonstrate that the developed algorithm obtained satisfactory results compared with the recent methods.