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Bearings play an irreplaceable role in various categories of high-precision mechanical equipment. To reduce the adverse impact caused by sudden bearing failures, this paper proposes a method based on improved particle swarm optimization (IPSO), empirical mode decomposition (EMD), and support vector regression (SVR) to predict the remaining useful life (RUL) of bearing. The EMD method decomposes the raw vibration signal, and the components are chosen out according to the kurtosis criterion to reconstruct the vibration signal. After filtering out the noise, the features in time-domain and frequency-domain are extracted from the reconstructed vibration signal. The IPSO method is used to optimize three important parameters of the SVR model. Then the SVR model is trained with the extracted features to predict the bearing RUL. Actual bearing failure dataset is used to verify the validity of this method. Three different methods are used to compare with this proposed method. The results indicate that this proposed method performs best among the four methods in different evaluation indexes. The proposed method makes sense for bearing failure prediction.

This paper proposes a complete parametric approach for eigenstructure assignment in second-order system Mq+Dq+kq=cu using the displacement-plus-acceleration feedback controller. The expressions of closed-loop eigenvector matrix and feedback gains are in direct closed forms in terms of the closed-loop eigenvalues and a group parameter vectors, which represents the degree of design freedom. The main advantage of the proposed method is solved directly in matrix second-order framework, without transformation into the first-order form, and the numerical stability is better because it used only right coprime factorization and simple matrix transformation. The proposed algorithm is simple and does not contain ‘go back’ procedures. Finally, computational results show its effect and simplicity.

As traditional negative selection algorithms need to calculate the distances between initial detectors and all the self data, the efficiencies of them are too low. In this article, before the creation process of detectors, the self data is pretreated to be a SS-Tree, and then the negative selection process is transformed into the data query process in the self SS-Tree to improve the efficiency of detectors creation.

Attack and defense is the two important aspects of network security issue. Network security situational assessment has focused on attack roundly. Numbers of network security situational assessment models have been put forward based on attack and threat. Those methods have obtained good results. On the other hand, defense lacks careful consideration. Defense embodies system tolerance. System tolerance contains three important factors: system tolerance to attack, system asset tolerance, and system survivability. The article will introduce a network security situational assessment model based on attack and defense. Consult the existent models of network security situational assessment to summarize a hierarchical model. The hierarchical model based on attack mainly. Then we will use the three factors of system tolerance to mend the model. The new hierarchical model based on attack and defense will be closer to realistic security situation status. The corresponding experiment proves that the improved model considered attack and defense has better effect.

Forecast of network security situation is an important part of network security situational assessment. The concept of network security situational assessment will be introduced. Then a model of network security situational assessment is summarized based on the concept. On the other hand, The method of GM(1,1) modified by residual error is mentioned. And this method will be improved into optimal fuzzy grey model. The model of optimal fuzzy grey will be used to forecast value of network security situational assessment. The results show better effects in simulation test, compared to the original model.

Guaranteed cost control for a class of linear discrete-time switched systems with parameter uncertainty is studied. The purpose is to design a state feedback control law such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound for all admissible uncertainties. Two different sufficient conditions on the existence of guaranteed cost controllers are derived based on a switched Lyapunov function (SLF) method together with linear matrix inequality (LMI) approach. Furthermore, a convex optimization problem with LMIs constraints is formulated to design a suboptimal guaranteed cost controller. A numerical example demonstrates the effect of the proposed design approach.

This paper investigates robust L2 - Linfin filtering problem for a class of linear uncertain time-delay systems. The uncertainties in the system are time-varying and bounded, and time delays are time-varying. The objective is to design linear memoryless filters such that for all uncertainties, the resulting augmented system is robust stable independent of delays and satisfies the proposed L2 - Linfin performance. Based on Lyapunov stability theory in time-delay systems, a sufficient condition on the existence of robust L2 - Linfin filters is derived. The problem of robust L2 - Linfin filters is turned into the solution problem of linear matrix inequalities. A numerical example is given to demonstrate the effect of the proposed approach

This paper is concerned with the problems of robust sliding mode filter designs for a class of nonlinear systems with parameter uncertainties and time-vary delays. The nonlinearities are assumed to satisfy global Lipschitz conditions and parameter uncertainties reside in a polytope. The resulting filter is of the Luenberger type with discontinuous form, it guarantees the robust asymptotic stability of the filtering error dynamics for all admissible uncertainties. A sufficient condition with delay-dependent is given for the existence of such a filter in terms of linear matrix inequalities (LMIs). The resulting filter can be easily obtained from the optimal solutions of this set of LMIs.

In this paper, the problem of stochastically reliable H/sub /spl infin// control for a class of uncertain stochastic systems with time-varying delays and actuator failures is considered. The time-varying delay is assumed to be bounded but unknown. A sufficient condition on stochastic reliable stabilizability is established based on the stability theory for stochastic differential equations. For all admissible uncertainties and actuator failures occurred within the prespecified subset, stochastically reliable H/sub /spl infin// controllers are designed t in terms of linear matrix equalities such that the closed-loop system with actuator failures is stochastically stable independent of delay and satisfies performance constraint of disturbance attenuation level represented by an H/sub /spl infin// norm bound constrain.