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Yang K.,Xian University of Science and Technology | Mu L.,Polytechnic University of Mozambique | Yang D.,Xian University of Science and Technology | Yang D.,Shaanxi Huanghe Group Co. | And 3 more authors.
Scientific World Journal | Year: 2014

A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics. © 2014 Kaifeng Yang et al.

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.

Dongdong Y.,Xian University of Technology | Dongdong Y.,Shaanxi Huanghe Group Co. | Lei Z.,Xian University of Technology | Rong F.,Xian 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.,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.

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.

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