Guilin Airforce Academy

Guilin, China

Guilin Airforce Academy

Guilin, China
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Xie C.-M.,Beihang University | Xie C.-M.,Guilin Airforce Academy | Zhao Y.,Beihang University | Deng J.-Y.,Beihang University | Deng J.-Y.,Guilin Airforce Academy
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | Year: 2011

An improved adaptive square root filtering algorithm for solving transfer alignment under time varying noise is designed. In the algorithm, the state covariance adjusting factor matrix, adaptive adjusting system noise and measurement noise covariance, as well as the infinite noise memory scale are incorporated into the square root filtering structure which is computed by a sequential filtering algorithm. Through one-step controlling and multi-step adaptive adjusting process, the performance in numerical computing, noise refraining and adaptive adjusting of the algorithm is remarkable improved with low calculation cost. Simulation results show that the algorithm possesses strong stability, high filtering accuracy and is capable of quickly adjusting according to the real noise, which provides an effective rapid and accurate transfer alignment method.

Han X.,Tsinghua University | Han X.,Guilin Airforce Academy | Zhang H.,Tsinghua University | Meng H.,Tsinghua University
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2011

In this paper, we propose a new algorithm, TLS-FOCUSS, for sparse recovery for large underdetermined linear systems, based on total least square (TLS) method and FOCUSS(FOCal Underdetermined System Solver). The problem of sparse recovery when perturbations appear in both the measurements and the dictionary (sensing matrix) is considered. FOCUSS algorithm is extended with main idea of TLS to reduce the impact of the perturbation of dictionary on the performance of sparse recovery. The simulation results illustrate the advantage of TLS-FOCUSS on accuracy and stability compared with ordinary FOCUSS algorithm. © 2011 IEEE.

Li M.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy | Cheng J.,University of Electronic Science and Technology of China | Le X.,University of Electronic Science and Technology of China | Luo H.-M.,University of Electronic Science and Technology of China
Ruan Jian Xue Bao/Journal of Software | Year: 2012

Learning-Based super-resolution methods usually select several objects with similar features from some examples according to the low-resolution image, then estimate super-resolution result using optimization algorithm. But the result is usually limited by the quality of matching objects and only geometric construction of the images is selected as matching feature, so matching accuracy is relatively low. This paper presents a sparse dictionary model for image super-resolution, which unifies the feature patches of high-resolution (HR) and low-resolution (LR) images for sparse coding. To break through the aforementioned limitations, this method builds a sparse association between HR and LR images, and realized simultaneous matching and optimization methods. The study uses a MCA method to improve the accuracy for feature extraction and carry out super-resolution reconstruction and denoise simultaneously. Sparse K-SVD algorithm is adopted as optimization method to reduce the computation time of sparse coding. Some experiments with real images show that this method outperforms other learning-based super-resolution algorithms. © 2012 ISCAS.

Li M.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy | Cheng J.,University of Electronic Science and Technology of China | Li X.-W.,University of Electronic Science and Technology of China | Le X.,University of Electronic Science and Technology of China
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | Year: 2011

A novel learning-based image inpainting method is presented. As a further development of classical sparse representation model, the non-local self-similar patches are unified for joint sparse representation and learning dictionary, in which each element of the self-similar patches has the same sparse pattern. The method assures the self-similar patches possess similarity when projected on the sparse space, and efficiently builds the sparse association among them. This association is next taken as a priori knowledge for image inpainting. The paper uses numerous samples and non-local patches of input image to train overcomplete dictionary. The method not only takes into account the priori knowledge of samples, but also considers the non-local self-similar information of input image. Large and small region inpainting experiments and text removing experiments on natural images show the good performance of the method.

Li M.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy | Li S.,University of Electronic Science and Technology of China | Le X.,University of Electronic Science and Technology of China | And 2 more authors.
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2011

Most recent image inpainting methods only use valid information found in input image as the clue to fill the inpainting region. These methods usually have the defects of insufficient prior information and relatively poor adaptivity. A novel learned dictionary based image inpainting framework is presented. The key idea is to build a sparse relationship between raw image patches and their corresponding feature patches, then use this relationship as the priori to guide the inpainting. Our method not only uses the valid information of the input image itself, but also utilizes the prior information of the sample images to improve the adaptivity. Large and small region inpainting experiments and text removing experiments on nature images show the good performance of our method.

Ren S.,University of Electronic Science and Technology of China | Cheng J.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2010

Image fusion can enhance remote sensing image data information so that the fused image is more in line with the vision of the human and better to analysis and processing of the image. How to fuse the multispectral images with a high spectral and the panchromatic image with a high spatial making the fused images with a high spectral as well as a high spatial resolution is recently thought to be especially important in remote sensing image study. Image fusion based on the traditional methods are not ideal for describing the images with high dimensional singularities. In this paper, we proposed a remote sensing image fusion algorithm based on the Curvelet transform, which represents the image edges better and is anisotropy We have experimented with both IKONOS images of Wenchuan in Sichuan province after the 5.12 earthquake and a group of resource satellite images to testify the performance of the method. © 2010 IEEE.

Tan L.,National University of Defense Technology | Yang J.,Guilin Airforce Academy | Cheng Z.,National University of Defense Technology | Guo B.,National University of Defense Technology
Chinese Journal of Mechanical Engineering (English Edition) | Year: 2011

Investigators are attracted by the complexity and significance of preventive maintenance problem, and there are hundreds of maintenance models and methods to solve the maintenance problems of companies and army, going with a lot of investigative harvests. However, one-component system or series system is focused by most of the literature. The problem of preventive maintenance (PM) on cold standby repairable system does not attach importance despite the fact that the cold standby repairable system is ubiquitous in engineering systems. In this paper, an optimal replacement model for gamma deteriorating system is studied. This methodology presented uses a gamma distribution to model the material degradation, and the impact of imperfect maintenance actions on system reliability is investigated. After an imperfect maintenance action, the state of a degrading system is assumed as a random variable and the maintenance time follows a geometric process. A maintenance policy (N) is applied by which the system will be repaired whenever it experiences the Nth PM, and an optimal policy (N*) could be determined numerically or analytically for minimizing the long-run average cost per unit time. A numerical example about how to confirm the optimal maintenance time by the inspecting information of liquid coupling device is given to demonstrate the use of this policy. This paper presents a condition-based replacement policy for cold standby repairable system under continuous monitoring. Its contribution embody in two aspects, relaxing the restrictions of hypothesis and investigating the condition-based maintenance policy of the cold standby repairable system which is ignored by others. © 2011 Chinese Journal of Mechanical Engineering.

Zhang G.,Guilin Airforce Academy | Zhao B.,Guilin Airforce Academy | Liu L.,Guilin Airforce Academy
2013 IEEE Conference Anthology, ANTHOLOGY 2013 | Year: 2014

Genetic algorithm is a type of excellent optimization algorithm only depends on fitness function instead of external information. But because of particularity and complicacy of the problem, its fitness function is often not to be solved. A kind of coding method based on processes is proposed by improving genetic algorithm in full consideration of equipment size, impact strength and process of polymerization, combining with loading characteristic and requirements of airdrop container, which solve the problems of requirement optimization of airdrop container and loading in the course of genetic operation through determining Adaptive genetic algorithm (AGA) of the fitness function by utilizing simulative loading method. © 2013 IEEE.

Wang L.,Capital Medical University | Huang B.,Guilin Airforce Academy
International Journal of Antennas and Propagation | Year: 2012

A MIMO antenna composed by microstrip line-fed circular slot antenna is proposed. This antenna is used in ultra-wideband microwave imaging systems aimed for early breast cancer detection. The antenna is designed to operate across the ultra-wideband frequency band in the air. The mutual coupling between the antenna elements has been investigated to be low enough for MIMO medical imaging applications. Both the simulation and measurement results are shown to illustrate the performances of the proposed antenna. Copyright © 2012 Liting Wang and Bin Huang.

Huang B.,Guilin Airforce Academy | Xu Y.,Guilin Airforce Academy
2010 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2010 | Year: 2010

A novel 1 × 4 planar UWB antenna array is proposed. The utilized single element is a compact planar circular slot micro-strip antenna. The feeding network is 1 to 4 Wilkinson power divider. The bandwidth of the antenna array is from 3.1GHz to 12.4GHz and the radiation pattern is bidirectional. © 2010 IEEE.

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