Nanchang Hangkong University

About: Nanchang Hangkong University is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 7004 authors who have published 5270 publications receiving 62162 citations. The organization is also known as: Nanchang Aviation University.

Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval

Abstract: With the increasing amount of high-resolution remote sensing images, it becomes more and more urgent to retrieve remote sensing images from large archives efficiently. The existing methods are mainly based on shallow features to retrieve images, while shallow features are easily affected by artificial intervention. Recently, convolutional neural networks (CNNs) are capable of learning feature representations automatically, and CNNs pre-trained on large-scale datasets are generic. This paper exploits representations from pre-trained CNNs for high-resolution remote sensing image retrieval. CNN representations from AlexNet, VGGM, VGG16, and GoogLeNet are first transferred for high-resolution remote sensing images, and then CNN features are extracted via two approaches. One is extracting the outputs of high-level layers directly and the other is aggregating the outputs of mid-level layers by means of average pooling with different pooling regions. Given the generalization and high dimensionality of the CNN features, feature combination and feature compression are also adopted to improve the feature representation. Experimental results demonstrate that aggregated features with pooling region smaller than the feature map size perform excellently, especially for VGG16 and GoogLeNet. Shallow feature makes a great contribution to enhance the retrieval precision when combined with CNN features, and compressed features reduce redundancy effectively. Compared with the state-of-the-art methods, the proposed feature extraction methods are very simple, and the features are able to improve retrieval performance significantly.

Acoustic emission analysis on tensile failure of air plasma-sprayed thermal barrier coatings

Abstract: article i nfo An acoustic emission technique was used to monitor the cracking behavior and fracture process of thermal barrier coatings subjected to tensile loading. Acoustic emission signals were extracted and preformed by fast Fourier transform, and their characteristic frequency spectrums and dominant bands were obtained to reveal fracture modes. Three different characteristic frequency bands were confirmed, corresponding to sub- strate deformation, surface vertical cracking and interface delamination, with the aid of scanning electronic microscopy observations. A map of the tensile failure mechanism of air plasma-sprayed thermal barrier coat- ings was established. The fracture strength and interfacial shear strength were estimated as 45.73±3.92 MPa