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Chao L.,Shandong Normal University | Chao L.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Journal of Computational and Nonlinear Dynamics | Year: 2016

Compared with chaotic systems over the real number field, complex chaotic dynamics have some unique properties. In this paper, a kind of novel hybrid synchronizations of complex chaotic systems is discussed analytically and numerically. Between two nonidentical complex chaotic systems, modified projective synchronization (MPS) in the modulus space and complete synchronization in the phase space are simultaneously achieved by means of active control. Based on the Lyapunov stability theory, a controller is developed, in which time delay as an important consideration is involved. Furthermore, a switch-modulated digital secure communication system based on the proposed synchronization scheme is carried out. Different from the previous works, only one set of drive-response chaotic systems can implement switch-modulated secure communication, which could simplify the complexity of design. Furthermore, the latency of a signal transmitted between transmitter and receiver is simulated by channel delay. The corresponding numerical simulations demonstrate the effectiveness and feasibility of the proposed scheme. © 2016 by ASME.


Zhang H.,Shandong University | Song X.,Shandong Normal University | Song X.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology | Shi L.,Hong Kong University of Science and Technology
IEEE Transactions on Automatic Control | Year: 2012

We consider remote state estimation over a packet-dropping network. A new suboptimal filter is derived by minimizing the mean squared estimation error. The estimator is designed by solving one deterministic Riccati equation. Convergence of the estimation error covariance and mean square stability of the estimator are proved under standard assumptions. It is shown that the new estimator has smaller error covariance and has wider applications when compared with the linear minimum mean squared error estimator. One of the key techniques adopted in this technical note is the introduction of the innovation sequence for the multiplicative noise systems. © 2012 IEEE.


Zhang H.,Shandong Normal University | Zhang H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology | Wang Z.,Shandong Normal University | Wang Z.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

This study proposes a normal distribution-based over-sampling approach to balance the number of instances belonging to different classes in a data set. The balanced training data are used to learn unbiased classifiers for the original data set. Under some conditions, the proposed over-sampling approach generates samples with expected mean and variance similar to that of the original minority class data. As the approach tries to generate synthetic data with similar probability distributions to the original data, and expands the class boundaries of the minority class, it may increase the minority class classification performance. Experimental results show that the proposed approach outperforms alternative methods on benchmark data sets most of the times when implementing several classical classification algorithms. © 2011 Springer-Verlag.


Li S.,Shandong Normal University | Liu H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Proceedings of 2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012 | Year: 2012

The paper proposes a method for automatically identifying plants by computer based on the analysis that the plants and culture media have different RGB color coordinates and HSI color coordinates scales. The method can identify the plant from the complex culture media background quickly and correctly, and it provides basis for subsequent reconstruction of 3-dimensional structures of plant. The method is designed for processing laboratory soybean images. Experiment suggests that it can quickly identify the characteristics of the soybean and meet the requirements of providing real-time signals for controlling the follow-up variables, so it lays a foundation for further research. © 2012 IEEE.


Wang J.,Shandong Normal University | Liu H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Proceedings of 2012 International Symposium on Information Technologies in Medicine and Education, ITME 2012 | Year: 2012

To improve the accuracy of plant leaf area, a new measurement method of plant leaf area based on Snake model was adopted in this study. In this paper, we captured leaf counter with improved Snake model, built up 8-direction chain codes table, calculated the leaf area with the pixels in leaf contour, and visualized the statistical results with area histogram. This method was proved to be more accurate than prevalent method in this study as well, which has good potentials for practical applications. © 2012 IEEE.


Luo C.,Shandong Normal University | Luo C.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology | Wang X.,Dalian University of Technology | Liu H.,Shandong Normal University | Liu H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Chaos | Year: 2014

In this article, the controllability of asynchronous Boolean multiplex control networks (ABMCNs) is studied. First, the model of Boolean multiplex control networks under Harvey' asynchronous update is presented. By means of semi-tensor product approach, the logical dynamics is converted into linear representation, and a generalized formula of control-depending network transition matrices is achieved. Second, a necessary and sufficient condition is proposed to verify that only control-depending fixed points of ABMCNs can be controlled with probability one. Third, using two types of controls, the controllability of system is studied and formulae are given to show: (a) when an initial state is given, the reachable set at time s under a group of specified controls; (b) the reachable set at time s under arbitrary controls; (c) the specific probability values from a given initial state to destination states. Based on the above formulae, an algorithm to calculate overall reachable states from a specified initial state is presented. Moreover, we also discuss an approach to find the particular control sequence which steers the system between two states with maximum probability. Examples are shown to illustrate the feasibility of the proposed scheme. © 2014 AIP Publishing LLC.


Zhang H.,Shandong Normal University | Zhang H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology | Cao L.,Shandong Normal University | Cao L.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Neurocomputing | Year: 2014

This paper introduces a novel bagging ensemble classifier pruning approach. Most investigated pruning approaches employ heuristic functions to rank classifiers in the ensemble, and select part of them from the ranked ensemble, so redundancy may exist in the selected classifiers. Based on the idea that the selected classifiers should be accurate and diverse, we define classifier similarity according to the predictive accuracy and the diversity, and introduce a Spectral Clustering based classifier selection approach (SC). SC groups the classifiers into two clusters based on the classifier similarity, and retains one cluster of classifiers in the ensemble. Experimental results show that SC is competitive in terms of classification accuracy. © 2014 Elsevier B.V.


Bai J.,Shandong Normal University | Bai J.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology | Liu H.,Shandong Normal University | Liu H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Applied Intelligence | Year: 2016

This paper proposes a multi-objective artificial bee colony (MOABC) algorithm based on decomposition by penalty-based boundary intersection (PBI) method. It decomposes a MOP into a number of single-objective problems. The objective of every single-objective problem is based on the distance and angle from the current solution to their own ideal point so as to obtain the good convergence and distribution of the optimal solutions. In this way, the aggregate function is proposed by PBI method. Then the three phases of Artificial Bee Colony (ABC) algorithm are adopted, which are the employed bees sending, the unemployed bees following, and the scout bees converting. Among these phases, the improvement degree of every aggregate function is proposed as the fitness function, which could overcome the two shortcomings in the weighted sum function usually applied in the conventional MOABC. Besides, Boltzmann selection mechanism is used to obtain the probability of unemployed bees following the employed bees so that the selection pressure of unemployed bees in the optimizing process could be adjusted dynamically. The algorithm is validated on CEC2009 problems and the problems with complicated Pareto set shapes in terms of four indicators: IGD, HV, SPR, and EPS. Experimental results show that our proposed algorithm can perform better than other state-of-the-art algorithms in the convergence and diversity, and can be considered as a promising alternative to solve MOPs. © 2016 Springer Science+Business Media New York


Gu W.,Shandong Normal University | Gu W.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology | Zhang H.,Shandong Normal University | Zhang H.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Journal of Computational Information Systems | Year: 2014

In this paper, we propose Weighting Inter-class Separating Fuzzy Clustering (WISFC) algorithm and employ it to content-based image retrieval (CBIR). Firstly, the training data set is clustered by k-means algorithm. Then the output cluster centroid is taken as the initial centroid of WISFC and the data set is clustered once again. When submit a query image, get its membership degree for each class using WISFC and the class which has the maximum membership value is what the query example belongs to. Afterwards we can retrieve images which has the larger similarity to the query image only in the class which it belongs to, which greatly reduces the time complexity. Experiments indicate that the method can effectively improve the accuracy of image retrieval. © 2014 Binary Information Press.


Zhang H.,Shandong Normal University | Li M.,Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology
Information Fusion | Year: 2014

This study investigates how to alleviate the class imbalance problems for constructing unbiased classifiers when instances in one class are more than that in another. Since keeping the data distribution unchanged and expanding class boundaries after synthetic samples have been added influence the classification performance greatly, we take into account the above two factors, and propose a Random Walk Over-Sampling approach (RWO-Sampling) to balancing different class samples by creating synthetic samples through randomly walking from the real data. When some conditions are satisfied, it can be proved that, both the expected average and the standard deviation of the generated samples equal to that of the original minority class data. RWO-Sampling also expands the minority class boundary after synthetic samples have been generated. In this work, we perform a broad experimental evaluation, and experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms. © 2014 Elsevier B.V. All rights reserved.

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