Shaanxi Huanghe Group Co.

Fengcheng, China

Shaanxi Huanghe Group Co.

Fengcheng, China

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Chen B.,Wuhan Naval University of Engineering | Yang D.D.,Shaanxi Huanghe Group Co. | Feng H.X.,Shaanxi Huanghe Group Co.
Applied Mechanics and Materials | Year: 2014

In this study, a novel two- phase image segmentation algorithm (TPIS) by using nonlocal mean filter and kernel evolutionary clustering in local learning is proposed. Currently, the difficulties for image segmentation lie in its vast pixels with overlapping characteristic and the noise in the different process of imaging. Here, we want to use nonlocal mean filter to remove different types of noise in the image, and then, two kernel clustering indices are designed in evolutionary optimization. Besides, the local learning strategy is designed using local coefficient of variation of each local pixels or image patch is employed to update the quality of the local segments. The new algorithm is used to solve different image segmentation tasks. The experimental results show that TPIS is competent for segmenting majority of the test images with high quality. © (2014) Trans Tech Publications, Switzerland.


Rong F.,University of Science and Technology of China | Qianya L.,University of Science and Technology of China | Qianya L.,XiAn Winrise Electronic Co Ltd. | Bo H.,Thermal Power Research Institute | And 2 more authors.
Chinese Control Conference, CCC | Year: 2015

The prediction of flight delays is heavily investigated in the last few decades. However, there is a relatively low level of study on Random flight point delays in these important problems. In this paper, we present an influence factor model of random flight points by series analysis on actual airline data, which is combined with BN (Bayesian Network) and GMM-EM(Gaussian mixture model-expectation maximization algorithm) algorithm. The creation of the initial parameters is based on the analysis for the continuous flights fly over the same flight point. The test data is offered by some Air Traffic Management Bureau. And the test result clearly demonstrates the value of Bayesian Network for analyzing the system-level effects arising from micro-level causes. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.


Xu Y.,PLA Air Force Aviation University | Feng H.,Shaanxi Huanghe Group Co. | Tian S.,PLA Air Force Aviation University | Li J.,Institute 207 of CASIC Second Academy
Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011 | Year: 2011

In order to improve the edge accuracy and the areas consistency, to reduce the partition error rate in textural image segmentation, we propose a new method which using multi-scale wavelet analysis based on feature learning in this paper. It improves the textural image segmentation by reducing the effect of redundant features on segmentation results. The method includes three stages as feature extraction, optimizing the feature vectors and feature space clustering. In the stage of filtrating valid features, we optimize the feature vectors by feature learning. The experimental results demonstrate that the improved algorithm is effective for textural image segmentation. © 2011 IEEE.


Xu Y.,PLA Air Force Aviation University | Tian S.,PLA Air Force Aviation University | Li J.,Institute 207 of CASIC Second Academy | Feng H.,Shaanxi Huanghe Group Co.
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | Year: 2012

It is difficult for many current despeckling methods to produce good region smoothing and edge preservation simultaneously. In this paper, a new Plural Pervasion equation is designed, which combines a Shearlet transform and edge detection of module maximum, and a new SAR image despeckling method is proposed. Experimental results indicate that the method possesses advantages of speckle reduction, and point target and edge preservation.


Dongdong Y.,Xi'an University of Technology | Dongdong Y.,Shaanxi Huanghe Group Co. | Lei Z.,Xi'an University of Technology | Rong F.,Xi'an University of Technology | Hui Y.,Fourth Engineering Design and Research Institute of Engineer Corps
Chinese Control Conference, CCC | Year: 2015

This paper aims to present two novel techniques in synthetic aperture radar (SAR) image segmentation by cooperative competition, cooperative learning and evolutionary multi-objective clustering in kernel mapping thereof. First, we introduce an efficient implementation of cooperative/competition evolution by using two parallel implemented populations, which are divided by the Pareto domination and local density information. Second, two conflicting fuzzy clustering validity indices are incorporated into this framework and optimized in kernel distance measure simultaneously and. Finally, the proposed algorithm is tested on two complicated SAR images. Compared with four other state-of-the-art algorithms and our method achieve comparable results in terms of convergence, diversity metrics, and computational time. © 2015 Technical Committee on Control Theory, Chinese Association of Automation.


Zhang X.-W.,Shaanxi Huanghe Group Co. | Zhang X.-W.,Xidian University | Li M.,Xidian University | Qu J.-S.,Shaanxi Huanghe Group Co. | Yang H.,The Fourth Engineering Design and Research Institute of the General Staff
International Journal of Electronics | Year: 2015

For the high resolution radar (HRR), the problem of detecting the extended target is considered in this paper. Based on a single observation, a new two-step detection based on sparse representation (TSDSR) method is proposed to detect the extended target in the presence of Gaussian noise with unknown covariance. In the new method, the Sinc dictionary is introduced to sparsely represent the high resolution range profile (HRRP). Meanwhile, adaptive subspace pursuit (ASP) is presented to recover the HRRP embedded in the Gaussian noise and estimate the noise covariance matrix. Based on the Sinc dictionary and the estimated noise covariance matrix, one step subspace detector (OSSD) for the first-order Gaussian (FOG) model without secondary data is adopted to realise the extended target detection. Finally, the proposed TSDSR method is applied to raw HRR data. Experimental results demonstrate that HRRPs of different targets can be sparsely represented very well with the Sinc dictionary. Moreover, the new method can estimate the noise power with tiny errors and have a good detection performance. © 2015 Taylor & Francis


Zhang X.-W.,Shaanxi Huanghe Group Co. | Zhang X.-W.,Xidian University | Li M.,Xidian University | Qu J.-S.,Shaanxi Huanghe Group Co. | Yang H.,Fourth Engineering Design and Research Institute of the General Staff
International Journal of Electronics | Year: 2016

For the high resolution radar (HRR), the problem of detecting the extended target is considered in this paper. Based on a single observation, a new two-step detection based on sparse representation (TSDSR) method is proposed to detect the extended target in the presence of Gaussian noise with unknown covariance. In the new method, the Sinc dictionary is introduced to sparsely represent the high resolution range profile (HRRP). Meanwhile, adaptive subspace pursuit (ASP) is presented to recover the HRRP embedded in the Gaussian noise and estimate the noise covariance matrix. Based on the Sinc dictionary and the estimated noise covariance matrix, one step subspace detector (OSSD) for the first-order Gaussian (FOG) model without secondary data is adopted to realise the extended target detection. Finally, the proposed TSDSR method is applied to raw HRR data. Experimental results demonstrate that HRRPs of different targets can be sparsely represented very well with the Sinc dictionary. Moreover, the new method can estimate the noise power with tiny errors and have a good detection performance. © 2015 © 2015 Taylor & Francis.


Li M.,Weinan Vocational and Technical College | Li M.,Xidian University | Zhang Q.,Shaanxi HUANGHE Group Co. | Zhao J.,Xidian University
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | Year: 2014

In 3D reconstruction, from the aspects of feature extraction, in the feature extraction process with human visual attention mechanism simulation, the higher significant target is given more attention. The extracted features are selected, and the information quantity is reduced. The purpose is to reduce the computational burden of the computer, and improve the reconstruction efficiency.


Yang D.,Xidian University | Yang D.,Shaanxi Huanghe Group Co. | Jiao L.,Xidian University | Niu R.,Xidian University | Gong M.,Xidian University
Computational Intelligence | Year: 2014

Clustering validity indices play a core role in the unsupervised pattern classification. To date, some indices have been proposed and their individual performances were compared on different artificial clustering test instances and real image segmentation tasks. However, little focus has been placed on the issue that how about their combinational performances are, although multi-objective optimization has attracted much interest from current researchers in unsupervised classification. Here, we firstly evaluate the performance of five state-of-the-art clustering indices with different characteristics in a single-objective optimization algorithm designed in artificial immune system (AIS). Then, a multi-objective optimization algorithm in AIS with fair ability of adaptability and diversity maintaining is introduced in this study. After that, the clustering performances of combinational clustering indices are investigated in the multi-objective optimization framework. To test the effectiveness of the combinational clustering validity indices, 27 benchmark functions with different geometric structures and three complicated remote sensing images are employed in this study. Based on the computer simulations, some meaningful empirical guidelines are obtained for selecting the suitable combinational clustering indices for formulating an effective and robust multi-objective clustering algorithm in different clustering tasks. © 2012 Wiley Periodicals, Inc.


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Shaanxi Huanghe Group Co. | Date: 2012-05-22

Electric control panels; Galvanic batteries; Inverters; Junction boxes; Plates for batteries; Power supplies; Solar batteries.

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