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Wen, Jiwei; Peng, Li

doi:https://doi.org/10.1155/2014/103626

Mathematical Problems in Engineering

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Abstract Introduction Preliminaries Numerical Example Conclusions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2014 | Article ID 103626 | https://doi.org/10.1155/2014/103626

Asynchronous Dynamic Output Feedback Control of Switched Time-Delay Systems with Sensor Nonlinearity and Missing Measurements

Jiwei Wen¹and Li Peng¹

Academic Editor: Asier Ibeas

Received13 Aug 2014

Revised09 Dec 2014

Accepted09 Dec 2014

Published31 Dec 2014

Abstract

The dynamic output feedback control problem for a class of discrete-time switched time-delay systems under asynchronous switching is investigated in this paper. Sensor nonlinearity and missing measurements are considered when collecting output knowledge of the system. Firstly, when there exists asynchronous switching between the switching modes and the candidate controllers, new results on the regional stability and gain analysis for the underlying system are given by allowing the Lyapunov-like function (LLF) to increase with a random probability. Then, a mean square stabilizing output feedback controller and a switching law subject to average dwell time (ADT) are obtained with a given disturbance attenuation level. Moreover, the mean square domain of attraction could be estimated by a convex combination of a set of ellipsoids, the number of which depends on the number of switching modes. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

1. Introduction

The past few decades have witnessed an ever increasing research interest in the control problems that are fundamental to the switched systems. These families of systems have great practical potential in many fields, such as power systems [1] and networked control systems [2]. A discrete-time switched system can be analytically expressed by a finite number of operation modes with switching signals between them. As a significant fact, the switching law depicts the transition between possible system behavior patterns; therefore, the average dwell time (ADT) approach has become one of the most effective methods to deal with the analysis and synthesis of switched system (see [3–5] and references therein). The main advantage of ADT switching is the fact that the stabilizability and other performance requirements could be achieved by regulating the switching rate, that is, to enlarge or reduce the number of switches over a finite time interval not less than a fixed value [6].

It is worth mentioning that an interesting research topic named “asynchronous switching” draws much attention in recent years. It usually means that the switching of controllers has a lag to the switching of system modes, because in many practical applications, it inevitably takes some time to identify the currently operating mode of the switched system and applies a matched controller. The stability, stabilizability, and filtering problem for such an asynchronous mechanism have been well studied by allowing the Lyapunov-like function (LLF) to increase during the unmatched period between the switching mode and the controller [7, 8]. Such an unmatched time depends on the identification of the switching mode and the scheduling of the candidate controller and the length of it may be time-varying according to different running environment. In most cases, the maximum value of the unmatched time must be known a priori [9–11]. Another effective method is to divide the active time of switching mode and controller into matched and mismatched intervals and corresponding controller gains could be obtained by solving coupled matrix inequality constraints [12–14]. The obtained results could soon be extended to the asynchronous filtering problems [15, 16]. Burgeoning research works in related areas such as finite-time control or state estimation, system with switching mechanism and time delay, and system with mismatched uncertainties could be reviewed in [17–22].

In most existing literatures concerning asynchronous switching, it has been implicitly assumed that the sensors can always provide unlimited amplitude signal and therefore ignore the possible effect of the sensor nonlinearity such as [23], although sensor nonlinearities are widely exist [24–26]. Moreover, the asynchronous controller design approaches for switched systems rely on the ideal hypothesis of perfect measurements [27]. However, in terms of engineering application, such hypothesis does not always hold. For example, due to the missing measurements [28] or incomplete measurements [29], the signal will be strongly influenced or even only contain noise, which indicate that real signal is jeopardized or missed. There is one more point that we want to touch on the fact that, in the abovementioned relevant references, the unmatched period between switching mode and controller is assumed known a priori. However, in almost all types of asynchronous mechanism, such unmatched period could be vague and random in the running time of different operating mode. Therefore, rather than having a large complexity to measure or estimate all the unmatched period or the largest one, it is significant and necessary to further develop more general asynchronous control strategy by allowing the LLF to increase with a random probability during the unmatched period.

In this technical note, we aim to investigate the asynchronous dynamic output feedback controller design problem for a class of time-delay switched system with both sensor nonlinearity and missing measurements. Note that the addressed system model is quite comprehensive to cover asynchronous switching, time delay, sensor nonlinearity, missing measurements, and performance requirement, hence reflecting the reality closely. The main contributions of this study are trifold: a new system model for time-delay switched system is established to take both sensor nonlinearity and missing measurements into account; new results on the regional stability and gain analysis for the underlying system are given by employing a binary switching sequence to depict the evolution of the LLF; a mode-dependent ellipsoid constraint, which represented by a convex combination of a set of ellipsoids, is developed to deal with sensor nonlinearity for considering the time-delay within the switching dynamics.

Notation. Notations in this paper are fairly standard. The superscript “” stands for matrices transport. and denote dimensional Euclidean space and set of all matrices, respectively. The brief notation or (where matrices and are symmetric) means that is positive semidefinite or positive definite, respectively. In symmetric matrices, we use as an ellipsis for the symmetric terms above or below the diagonal; and denote identity matrix and zero matrix with appropriate dimensions, respectively.

2. Preliminaries and Problem Formulations

Consider the following discrete-time switched system: and sensors with both saturation and missing measurements: where is the state vector, is the controlled output vector, and is the th sensor observations. Notations and represent the exogenous disturbance and measurement noise, respectively.

The switching signal is a piecewise constant function of time, which takes values in a finite integer set , and is the number of the switching modes. The time sequence represents every switching instant and time is also denoted by initial time. When , the th mode is active and therefore the trajectory of system (1) is the trajectory under the th mode. The jumps of the state at the mode switching instants are not considered here.

For each possible value of , we denote , , , , , , , , , and for simplicity.

The standard saturation function with appropriate dimensions , which is defined as , is employed to describe the sensor nonlinearity in this study. The notation “” denotes a signum function. Here we have slightly abused the notation by using to denote both the scalar valued and the vector valued saturation functions.

To deal with the asynchronous switching between switching mode and corresponding controller, we let , , denote the active time interval of some subsystems, while and represent the unmatched time and the matched time, respectively. The LLF is assumed to increase during and decrease during . Because and are randomly dispersed intervals, we assume they obey Bernoulli distribution.

The stochastic variables and in sensor model (2) are Bernoulli distributed white sequences taking values with It can be recalled from [28] that, in sensor model (2), if , it means that the sensor subjects to saturation only; if and , it implies that the sensor works normally; if and , the sensor detects noise only.

Assumption 1. We use stochastic variable to characterize the randomly dispersed intervals and , which satisfy If , it means that the system mode and controller are matched and the LLF is decreased during the interval ; if , it implies that the system mode and controller are unmatched and the LLF is increased during the interval .

In this study, a class of switching signals with ADT switching is designed when the controller is obtained. Thus, the definition of ADT is recalled.

Definition 2 (see [6] (average dwell time)). For a switching signal and any time , let be the switching numbers of over the finite interval . If, for any given and , we have , then and are called average dwell time and chatter bound, respectively. As commonly used in the references, we choose .
For notational brevity, we rewrite sensor model (2) as where Moreover, we set
In this note, we are interested in designing the following dynamic output feedback controller: where matrices , and are controller gains to be determined. By introducing new vectors and , the resulting closed-loop systems under output feedback controller (8) become where
In order to deal with the regional mean square stability of closed-loop systems (9)~(10) and the saturation nonlinearity , the following preliminaries are given.

Definition 3. Denote be the state trajectory of closed-loop systems (9)~(10) starting from the initial value ; then the set satisfying is said to be the mean square domain of attraction of the origin.

Lemma 4 (see [28]). Assume the nonlinear function satisfies and , where is a positive scalar satisfying . Set and define Then, it can be verified that and for each .

Definition 5. Define a switching level set as follows: where , are positive definite matrices.

Remark 6. The level set is graphic and simple in form but could not be directly employed to estimate the domain of attraction . This problem will be tackled in the following part of the paper.
The purpose of this study is to design dynamic output feedback controller of form (8) subject to ADT switching such that the following two conditions are satisfied.(i)Closed-loop systems (9)~(10) with are regional mean square stabilizable and the switching level set .(ii)Under the zero-initial condition, the controlled output satisfies where for any nonzero and a prescribed attenuation level .

3. Main Results

Let us start with tackling the switching level set . Given ellipsoid sets where matrices and are positive definite, then we denote where represents a convex hull. Viewing and as vertices of the level set and by using the property of the polytope [30], (19) immediately yields . Obviously, the estimation of the domain of attraction could be enlarged when we replace the level set by the ellipsoid set . Thus, the domain of attraction could be estimated by ellipsoid (19) only if is satisfied.

Theorem 7. Consider systems (9)~(10) with and let the controller gain , , and be given. If, for some given constants , , and , there exist matrices and and scalar such that then the system is regional mean square stable and ellipsoid is contained in the mean square domain of attraction for switching signal with ADT: where

Proof. For the convenience of manipulation, we assume the matrices and have the diagonal form; that is, and . Condition (20) means that if , then the nonlinear constraint could always be satisfied. In order to simplify the proof, we only consider closed-loop system (10). The results for system (9) can be obtained in a similar way.
Consider the quadratic LLF: then From the above equality, we have where which comes from the fact that It follows from (14) that where Because (21) holds, that is, , we know that Then, according to (31) and (33), one has Following the same lines of the proof of (34), we can get It follows from (4), (34), and (35) that If we denote , then, from the above, we can get Note that then, according to (23) and (26), one has Thus, If ADT satisfies (24), one has Therefore, we conclude that exponentially converges to zero as in the mean square sense; then the mean square stability can be deduced. From (19), we know that constraint (20) indicates that (see [31] for details). Therefore, for each
, it follows immediately that (see [32, 33] for details). Thus, the proof is completed.

Remark 8. In this work, we focus our study on the asynchronous switching by considering randomly occurring sensor nonlinearity and missing measurements when collecting the output knowledge. The saturation function, which describes the sensor nonlinearity, restricts the system state and controller state in a regional area of the state space. Since is expressed by a convex combination of and , it has two main advantages. First, the invariant ellipsoid depends on all switching modes not just a single one; thus it could enlarge the mean square domain of attraction. Second, the possible largest estimation of the mean square domain of attraction could be obtained by employing a similar approach presented in Algorithm 1 in [31].

Remark 9. The mean square domain of attraction could be estimated by , which is constructed by the convex combination of ellipsoid . It can be easily verified that where and satisfying .

Remark 10. Compared with the results presented in [7], the maximal value of the unmatched time interval is not required to be known a priori and the probability of the matched and unmatched time interval could be obtained by using a statistical method. If , the results of Theorem 7 degenerate to the case that the switching of the system modes and controllers are matched. A simple corollary is omitted here owing to limited space.

Next, we direct our attention to the gain analysis. Sufficient conditions for both the regional mean square stability and performance of the closed-loop systems (9)~(10) are derived in the following theorem.

Theorem 11. Consider systems (9)~(10) with and let the controller gain , , and be given. If, for some given constants , (), and , there exist matrices and and scalar such that then the system is regional mean square stable with a given disturbance attenuation level and ellipsoid is contained in the mean square domain of attraction for switching signal with ADT: where

Proof. In order to simplify the proof, we only consider closed-loop system (10). The results of system (9) can be obtained in a similar way. The LLF is chosen as the same to the one in the proof of Theorem 7.
Because (44) holds (i.e., ) and , we know that Because (49) holds and , we obtain (in (21)). Thus, it can be concluded that if . Similarly, we know that if . According to Theorem 7, conditions (43), (46), and (47) with and guarantee the mean square stability of systems (9)~(10) and ellipsoid is contained in the mean square domain of attraction .
Next, we pay our attention to the performance analysis. Define Since conditions (44) and (45) imply that Therefore, with the property of polytope [30], we get According to (51) and the above inequality, one further gets It follows immediately that . Therefore, (16) holds, which means a prescribed disturbance attenuation level is obtained. Thus, the proof is completed.

According to the regional performance analysis presented in Theorem 11, a solution to the asynchronous control for the underlying system with randomly occurring sensor nonlinearity and missing measurements is given in what follows. The dynamic output feedback gain can be obtained by solving a set of linear matrix inequalities (LMIs).

Theorem 12. Consider switched system (1) and sensors model (2) and let (), (), , be given constants. If there exist matrices , , , , , , , and and scalar , where , , such that then the system is regional mean square stable with given disturbance attenuation level and ellipsoid is contained in the mean square domain of attraction for switching signal with ADT: whereThe designed controller gain can be obtained as and , .

Proof. Constraint (55) is established by denoting . Inequality (56) can be directly obtained from because is a diagonal matrix. Noting that , we have With the property of polytope [30], we obtain which yields condition (58).
Conditions (61) and (62) come directly from (46) and (47).
Next, we pay our attention to obtaining (59). By using Schur complement to (44), one has where Noting that and by repeatedly using Schur complement, we can get inequality (59). The detailed proof is omitted owing to limited space. Following the same lines of the proof of (59), we can get inequality (60). Thus, the proof is completed.

Remark 13. It should be noted that the conditions stated in Theorem 12 are a set of LMIs with matrix inverse constraints. Although they are nonconvex, which are difficult to solve, we develop the following cone complementary linearization (CCL) algorithm to solve such matrix inequalities.

Algorithm 14. Consider the following steps.

Step 1. Initialize index and specify the number of iterations . Find matrices and , such that LMIs (56)~(61) and hold.

Step 2. For and obtained in the previous step, find and such that the following minimization problem has solutions:

Step 3. Check whether the solutions satisfy (59) and (60) via replacing by and check whether condition (55) is satisfied as precise as possible. If (59) and (60) are satisfied, Step 2 ceases and returns the value of Else, increment and get back to Step 2.

When , iterative process stops and feasible solutions could not be found.

4. Numerical Example

Consider a discrete-time switched time-delay system (1) with matrix parameters:

The considered sensor model is given with parameters:

The exogenous disturbance is taken as . The measurement noises are taken as . Time delay is taken as .

The probabilities are taken as , , , , and . The given disturbance attenuation level is . Some other parameters are chosen as , , and . The initial system state and controller state are taken as and .

By employing Algorithm 14, we obtain the controller gain:

The ellipsoid parameters are obtained as

Since we have we could find that the obtained controller is feasible.

The mean square domain of attraction could be described as system state: , controller state: .

The switching signals generated in Figure 1 depict the operating of the system modes and controllers, where the solid line represents the switching of the system modes and the dash-dotted line represents the switching of the controllers. The eigenvalues of modes and could be obtained as therefore the input-free switched system (with ) could be unstable. By solving ADT constraint (62), we get . We also could obtain that the real ADT of the switching mode is , which satisfies ADT constraint . Figure 2 shows the trajectories of system state and controller state under the asynchronous control move. The controlled system is mean square stable, which satisfactorily justifies the effectiveness of the proposed control method. The mean square domains of attraction including system states and controller states are shown in Figure 3.

5. Conclusions

The problems of asynchronous dynamic output feedback control for a class of time-delay switched systems subject to sensor nonlinearity and missing measurements are investigated in this study. New results on the regional stability, gain analysis, and regional controller design for the underlying system are given by allowing the Lyapunov-like function to increase with a random probability during the unmatched period of the switching mode and controller. A convex combination of a set of ellipsoids is employed to estimate the domain of attraction of the system.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This project was jointly supported by NSFC (61203126), NSFC (61374047), and BK2012111.

References

D. Liberzon, Switching in Systems and Control, Systems & Control: Foundations & Applications, Birkhäuser, Berlin, Germany, 2003.
View at: Publisher Site | MathSciNet
W.-A. Zhang and L. Yu, “Modelling and control of networked control systems with both network-induced delay and packet-dropout,” Automatica, vol. 44, no. 12, pp. 3206–3210, 2008.
View at: Publisher Site | Google Scholar | MathSciNet
B. Niu and J. Zhao, “Stabilization and $L_{2}$ -gain analysis for a class of cascade switched nonlinear systems: an average dwell-time method,” Nonlinear Analysis: Hybrid Systems, vol. 5, no. 4, pp. 671–680, 2011.
View at: Publisher Site | Google Scholar | MathSciNet
J. Zhang, Z. Han, F. Zhu, and J. Huang, “Stability and stabilization of positive switched systems with mode-dependent average dwell time,” Nonlinear Analysis: Hybrid Systems, vol. 9, pp. 42–55, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
L. L. Li, J. Zhao, and G. M. Dimirovski, “Observer-based reliable exponential stabilization and $H_{\infty}$ control for switched systems with faulty actuators: an average dwell time approach,” Nonlinear Analysis: Hybrid Systems, vol. 5, no. 3, pp. 479–491, 2011.
View at: Publisher Site | Google Scholar | MathSciNet
J. P. Hespanha and A. S. Morse, “Stability of switched systems with average dwell-time,” in Proceedings of the 38th IEEE Conference on Decision and Control (CDC '99), vol. 3, pp. 2655–2660, December 1999.
View at: Google Scholar
L. Zhang and P. Shi, “Stability, $l_{2}$ -gain and asynchronous $H_{\infty}$ control of discrete-time switched systems with average dwell time,” IEEE Transactions on Automatic Control, vol. 54, no. 9, pp. 2192–2199, 2009.
View at: Publisher Site | Google Scholar | MathSciNet
L. Zhang and H. Gao, “Asynchronously switched control of switched linear systems with average dwell time,” Automatica, vol. 46, no. 5, pp. 953–958, 2010.
View at: Publisher Site | Google Scholar | MathSciNet
L. X. Zhang, N. G. Cui, M. Liu, and Y. Zhao, “Asynchronous filtering of discrete-time switched linear systems with average dwell time,” IEEE Transactions on Circuits and Systems I, vol. 58, no. 5, pp. 1109–1118, 2011.
View at: Publisher Site | Google Scholar | MathSciNet
M. S. Mahmoud and P. Shi, “Asynchronous $H \infty$ filtering of discrete-time switched systems,” Signal Processing, vol. 92, no. 10, pp. 2356–2364, 2012.
View at: Publisher Site | Google Scholar
X. Zhao, P. Shi, and L. X. Zhang, “Asynchronously switched control of a class of slowly switched linear systems,” Systems & Control Letters, vol. 61, no. 12, pp. 1151–1156, 2012.
View at: Publisher Site | Google Scholar | MathSciNet
Z. R. Xiang and R. H. Wang, “Robust control for uncertain switched non-linear systems with time delay under asynchronous switching,” IET Control Theory & Applications, vol. 3, no. 8, pp. 1041–1050, 2009.
View at: Publisher Site | Google Scholar
Z. Xiang and Q. Chen, “Robust reliable control for uncertain switched nonlinear systems with time delay under asynchronous switching,” Applied Mathematics and Computation, vol. 216, no. 3, pp. 800–811, 2010.
View at: Publisher Site | Google Scholar | MathSciNet
S. Huang and Z. Xiang, “Robust $L_{\infty}$ reliable control for uncertain switched nonlinear systems with time delay under asynchronous switching,” Applied Mathematics and Computation, vol. 222, pp. 658–670, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
Z. Xiang, C. Qiao, and M. S. Mahmoud, “Robust $H \ s b \infty$ filtering for switched stochastic systems under asynchronous switching,” Journal of the Franklin Institute: Engineering and Applied Mathematics, vol. 349, no. 3, pp. 1213–1230, 2012.
View at: Publisher Site | Google Scholar | MathSciNet
Z. Xiang, C. Liang, and Q. Chen, “Robust $L_{2}$ – $L_{\infty}$ filtering for switched systems under asynchronous switching,” Communications in Nonlinear Science and Numerical Simulation, vol. 16, no. 8, pp. 3303–3318, 2011.
View at: Publisher Site | Google Scholar | MathSciNet
H. Liu, Y. Shen, and X. D. Zhao, “Asynchronous finite-time $H_{\infty}$ control for switched linear systems via mode-dependent dynamic state-feedback,” Nonlinear Analysis: Hybrid Systems, vol. 8, pp. 109–120, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
P. Shi, Y. Zhang, and R. Agarwal, “Stochastic finite-time state estimation for discrete time-delay neural networks with Markovian jumps,” Neurocomputing, vol. 151, part 1, pp. 168–174, 2014.
View at: Publisher Site | Google Scholar
J. Lian, C. W. Mu, and P. Shi, “Asynchronous $H_{\infty}$ filtering for switched stochastic systems with time-varying delay,” Information Sciences. An International Journal, vol. 224, pp. 200–212, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
K.-S. Park and J.-T. Lim, “Exponential stability of singularly perturbed discrete systems with time-delay,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 2, pp. 865–874, 2013.
View at: Google Scholar
F. Li, P. Shi, L. Wu, and X. Zhang, “Fuzzy-model-based stability and non-fragile control for discrete-time descriptor systems with multiple delays,” IEEE Transactions on Fuzzy Systems, vol. 22, no. 4, pp. 1019–1025, 2014.
View at: Google Scholar
F. Li, L. Wu, P. Shi, and C. C. Lim, “State estimation and sliding mode control for semi-Markovian jump systems with mismatched uncertainties,” Automatica, vol. 51, pp. 385–393, 2015.
View at: Publisher Site | Google Scholar | MathSciNet
J. Lian and Y. Ge, “Robust $H_{\infty}$ output tracking control for switched systems under asynchronous switching,” Nonlinear Analysis: Hybrid Systems, vol. 8, pp. 57–68, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
D. Ding, Z. Wang, B. Shen, and H. Shu, “ $H_{\infty}$ state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 5, pp. 725–736, 2012.
View at: Publisher Site | Google Scholar
Y. Xu, H. Su, Y. J. Pan, and Z. G. Wu, “Robust H_∞ filtering for networked stochastic systems with randomly occurring sensor nonlinearities and packet dropouts,” Signal Processing, vol. 93, no. 7, pp. 1794–1803, 2013.
View at: Publisher Site | Google Scholar
H. L. Dong, Z. D. Wang, and H. J. Gao, “On design of quantized fault detection filters with randomly occurring nonlinearities and mixed time-delays,” Signal Processing, vol. 92, no. 4, pp. 1117–1125, 2012.
View at: Publisher Site | Google Scholar
R. Wang, Z.-G. Wu, and P. Shi, “Dynamic output feedback control for a class of switched delay systems under asynchronous switching,” Information Sciences, vol. 225, pp. 72–80, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
Z. Wang, B. Shen, and X. Liu, “ $H_{\infty}$ filtering with randomly occurring sensor saturations and missing measurements,” Automatica, vol. 48, no. 3, pp. 556–562, 2012.
View at: Publisher Site | Google Scholar | MathSciNet
P. Shi, X. Luan, and F. Liu, “ $H_{\infty}$ filtering for discrete-time systems with stochastic incomplete measurement and mixed delays,” IEEE Transactions on Industrial Electronics, vol. 59, no. 6, pp. 2732–2739, 2012.
View at: Publisher Site | Google Scholar
S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, SIAM, Philadelphia, Pa, USA, 1994.
View at: MathSciNet
D. W. Li, Y. G. Xi, and Z. L. Lin, “An Nth-step set invariance approach to the analysis and design of discrete-time linear systems subject to actuator saturation,” Systems & Control Letters, vol. 60, no. 12, pp. 943–951, 2011.
View at: Publisher Site | Google Scholar
T. Hu, Z. Lin, and B. M. Chen, “An analysis and design method for linear systems subject to actuator saturation and disturbance,” Automatica, vol. 38, no. 2, pp. 351–359, 2002.
View at: Publisher Site | Google Scholar
T. S. Hu, Z. L. Lin, and B. M. Chen, “Analysis and design for discrete-time linear systems subject to actuator saturation and disturbance,” Systems and Control Letters, vol. 45, no. 2, pp. 97–112, 2002.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2014 Jiwei Wen and Li Peng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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