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Mu X.,PLA Second Artillery Engineering University | Chang R.,The Engineering University of Armed Police Force | Song G.,PLA Second Artillery Engineering University | Song H.,The Second Artillery Petty Officer College
Gaojishu Tongxin/Chinese High Technology Letters | Year: 2012

To improve the accuracy of defect prediction for unlabeled software data sets, a novel software defect prediction method based on the combination of spectral clustering and chaotic immune is presented. The method first introduces the Ng-Jordan-Weiss (NJW) algorithm, a spectral clustering algorithm, into the field of software defect prediction, and then uses a new chaotic immune clustering algorithm go replace the K-Means algorithm to overcome the K-Means's problem of easily getting trap local optima in spectral clustering. And under the framework of immune clone selection, it designs a new affinity function for immune clone clustering and gives the layered chaotic mutation operator based on the immune and chaotic theory to diversify the antibodies and improve the accuracy of software defect prediction. Two case studies are used to validate the method on the Iris and three commercial software data sets. The experimental results illustrate the effectiveness of the proposed method.


Shen X.,PLA Second Artillery Engineering University | Jia W.,PLA Second Artillery Engineering University | Yao M.,PLA Second Artillery Engineering University | Chang R.,The Engineering University of Armed Police Force
Gaojishu Tongxin/Chinese High Technology Letters | Year: 2012

Aiming at the overload computational complexity in an unscented Kalman filter (UKF), an additive spherical simplex square root UKF (ASSRUKF) was proposed. To decrease the computational complexity, the algorithm for the proposed filter, called the ASSRUKF algorithm, used an additive non-augmented unscented transform and a spherical simplex sampling to reduce the state dimension and the number of sigma points, respectively. Meantime, the covariance matrix was replaced with a new matrix whose entries were square roots of the covariance matrix in the process of estimation to ensure the efficiency and stability of the filter. Under the condition of additive noise, the proposed algorithm was applied to the attitude determination model of an unmanned aerial vehicle (UAV), which combined a MEMS inertial measurement unit and a magnetometer. The simulation results showed that the estimation precision of the proposed algorithm was similar to the standard UKF, while the computational time was only 36.8% of the UKF, which effectively reduced the computational complexity.


Zhang Q.,PLA Second Artillery Engineering University | Qiao Y.-K.,The Engineering University of Armed Police Force | Kong X.-Y.,PLA Second Artillery Engineering University | Si X.-S.,PLA Second Artillery Engineering University
Wuli Xuebao/Acta Physica Sinica | Year: 2014

To solve the degeneracy phenomenon and to improve the ability for tracking the breaking states are two difficult problems in the application of particle filter. Sequential important re-sampling can reduce orilliminate degeneracy, but the sample impoverishment is a secondary result. Extended particle filter can also reduce the degeneracy, but it cannot track the breaking states. The ability to track the breaking states can be improved by a strong tracking particle filter, but the degeneracy phenomenon will not be well solved still. A stochastic perturbation strong tracking particle filter is proposed for solving the above problems, in which a stochastically perturbative re-sampling is introduced into a strong tracking particle filter. Thus a stochastic perturbation is added to the particle with maximal weight to form some new particles, and the degenerative particles are displaced by the new particles to solve the degeneracy phenomenon and so the sample impoverishment improves the diversity of the samples. The ability of the proposed algorithm to track breaking states is also improved, and the feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the system with constants in different periods of time. © 2014 Chinese Physical Society.


Zhang W.Z.,The Engineering University of Armed Police Force | Wu W.,The Engineering University of Armed Police Force | Shen X.G.,The Engineering University of Armed Police Force
Advanced Materials Research | Year: 2014

This paper presented a detecting algorithm based on improved Gaussian mixture model, which improved the speed of establishing and updating the model. Whilst, according to the detection requirements in different time periods, using the method of combining temporal differencing and background differencing to detect moving body improved the instantaneity and veracity of moving body detection. © (2014) Trans Tech Publications, Switzerland.


Wu W.,The Engineering University of Armed Police Force
Advanced Materials Research | Year: 2014

A method for driver fatigue detection based on eye locating was researched in this paper.. The eye location was achieved by combining gray information with shape information, and matched the eye template of image with which was in the open state. To observe images within a certain time interval was to identify the open or closed state of the drivers' eyes, so as to determine if they have fatigue driving. The results showed that the algorithm could suppress gaussian noise and impulse noise very effectively, and had better filtering performance than the standard median filters. © (2014) Trans Tech Publications, Switzerland.

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