The selection of wavelet threshold and the determination of thresholding function would directly affect the quality of the signal denoising using wavelet thresholding method. In the conventional thresholding denoising approaches, some aspects require improvement, such as the fixed threshold and the inflexible thresholding rules. To address these problems, a Kent chaos artificial bee colony (KCABC) based wavelet thresholding denoising approach is proposed in this paper. Firstly, a sine function based parametric wavelet thresholding function is put forward to devise the flexibility of the classical thresholding methods. Then, three strategies are employed to improve the performance of the basic ABC algorithm. The threshold and shape tuning parameter are initialized as the position of the individual, and the mean square error between the original and the thresholded signals is taken as the fitness function. Finally, the performances of the proposed algorithm and the existing methods are tested by denoising four benchmark signals with different noise cases. The simulation results indicate the proposed approach outperforms the existing methods in the capability of noise reduction.

Vibration monitoring signals are widely used for damage alarming among the structural health monitoring system. However, these signals are easily corrupted by the environmental noise in the collecting that hampers the accuracy and reliability of measured results. In this paper, a modified artificial bee colony (MABC) algorithm-based wavelet thresholding method has been proposed for noise reduction in the real measured vibration signals. Kent chaotic map and general opposition-based learning strategies are firstly adopted to initialize the colony. Tournament selection mechanism is then employed to choose the food source. Finally, the Kent chaotic search is applied to exploit the global optimum solution according to the current optimal value. Moreover, a generalized cross validation (GCV) based fitness function is constructed without requiring foreknowledge of the noise-free signals. A physical model experiment for a high-piled wharf structure is implemented to verify the feasibility of the proposed signal denoising approach. Particle swarm optimization (PSO) algorithm, basic artificial bee colony (BABC) algorithm and Logistic chaos artificial bee colony (LABC) algorithm and are also taken as contrast tests. Comparison results demonstrate that the proposed algorithm outperforms the other algorithms in terms of convergence speed and precision, and can effectively reduce the noise from the measured vibration signals of the high-piled wharf structure.

For decoding the asynchronous superposition of response signals from different sensors, it is a challenge to achieve correlation in a code division multiplexing (CDM) based passive wireless surface acoustic wave (SAW) multisensor system. Therefore, an on-chip correlator scheme is developed in this paper. In contrast to conventional CDM-based systems, this novel scheme enables the correlations to be operated at the SAW sensors, instead of the reader. Thus, the response signals arriving at the reader are the result of cross-correlation on the chips. It is then easy for the reader to distinguish the sensor that is matched with the interrogating signal. The operation principle, signal analysis, and simulation of the novel scheme are described in the paper. The simulation results show the response signals from the correlations of the sensors. A clear spike pulse is presented in the response signals, when a sensor code is matched with the interrogating code. Simulations verify the feasibility of the on-chip correlator concept.

To determine the reasonable parameter settings of particle swarm optimization (PSO) algorithm, this paper discusses the impact of the time-varying inertia weight and velocity-based mutation strategies on the performance of PSO algorithm. The performance of the PSO algorithm with these two kinds of parameters adjustment strategies are tested through four well-known benchmark functions. The simulation results show that the PSO algorithm has better convergence performance with the quickly decreasing inertia weight. Also, the velocity-based mutation strategy will slow down the convergence speed of PSO algorithm if the global solutions over the adjacent generations are close to each other.

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cable-stayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

In indoor environment, there are gross errors in random measured values of base station, which has effect on generalization ability of BP neural network and then results in low location accuracy. In order to improve location accuracy, location algorithm of BP Neural Network based on residual analysis is proposed, namely conducting pretreatment on measured values separately in training phase and location phase of BP neural network with twice residual analysis and getting rid of measured value with bigger error. The simulation result shows that such algorithm is better than BP algorithm both in aspects of convergence rate and location effect.

This paper deals with the problem of state feedback stabilization with finite-time stochastic stability for a class of discrete-time switched stochastic linear systems under asynchronous switching. The attention is focused on designing the feedback controller that guarantees the finite-time stochastic stability of the dynamic system. The finite-time stochastic stability definition of discrete-time switched stochastic systems is introduced. The asynchronous switching idea originates from the fact that switching instants of the controllers lag behind or exceed those of subsystems. On the basis of the average dwell time method and multiple Lyapunov functions approach, a finite-time stochastic stability condition is established. Then, an asynchronously switched controller is designed and the corresponding switching law is derived to guarantee the considered system be finite-time stochastically stable. Two numerical examples are provided to show the effectiveness of the developed results.

The problem of robust H-infinity finite-time fault-tolerant control of switched systems with asynchronous switching is investigated in this paper. The asynchronous switching idea originates from the fact that switching instants of the controllers lag behind or exceed those of the subsystems. In order to make systems antijamming and fault-tolerant, the attention is focused on designing a robust H-infinity fault-tolerant asynchronously switched controller that guarantees the finite-time properties of dynamic system. Using the average dwell time method and the multiple Lyapunov-like function technique, a finite-time stabilizable condition related to the dwell time is proposed. Also, the problem of H-infinity fault-tolerant control for switched systems with asynchronous switching is investigated and a state feedback controller is designed to guarantee finite-time stability of the switched systems. Furthermore, the design method of feedback controller is proposed to ensure robust H-infinity finite-time stability of the system for all admissible uncertainties, actuator fault and exogenous disturbance. Numerical examples are employed to verify the effectiveness of the proposed method.

An optimization based on particle swarm optimization (PSO) algorithm was put forward for optimal sensor placement (OSP) in the structural health monitoring system (SHMs) of long-span cable-stayed bridges. The mathematical model was firstly presented and dual-structure coding was adopted to improve the individual encoding method in the PSO algorithm. Fitness function was established to solve the optimal problem based on the root-mean-square value of off-diagonal elements of modal assurance criterion matrix. Finally, one long-span cable-stayed bridge was taken as an example, and implemented the sensor placement based on PSO. The stimulation results show that the proposed PSO algorithm has better improvement in search ability and computation efficiency when compared with genetic algorithm (GA).

Concerning the problems that Non-Line-Of-Sight (NLOS) error affects the location accuracy in cellular wireless location systems, a location and tracking algorithm based on the two-step Kalman filter algorithm was proposed. The algorithm first used the mean value of new information which was produced in the Kalman filter iteration process to identify and to eliminate the NLOS error, and then got the reconstructed measurements. Then the Kalman filter system noise covariance was improved and adjusted adaptively. The simulation results show that the proposed algorithm can mitigate NLOS error greatly, improve location accuracy in NLOS environments and obtain better location effect, which is much better than the classic EKF.

In this paper, the finite-time stabilization problems for switched stochastic systems are addressed. Firstly, when there exists asynchronous switching between the controller and the system, a sufficient condition for the existence of stabilizing switching law for switched stochastic systems is derived. It is proved that the switched stochastic systems are finite-time stabilizable under asynchronous switching satisfying the average dwell-time condition. Furthermore, the problem of reliable control for switched stochastic systems under asynchronous switching is also investigated. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

This paper is concerned with the problem of controller design for switched systems under asynchronous switching with exogenous disturbances. The attention is focused on designing the feedback controller that guarantees the finite‐time bounded and L ∞ finite‐time stability of the dynamic system. Firstly, when there exists asynchronous switching between the controller and the system, a sufficient condition for the existence of stabilizing switching law for the addressed switched system is derived. It is proved that the switched system is finite‐time stabilizable under asynchronous switching satisfying the average dwell‐time condition. Furthermore, the problem of L ∞ control for switched systems under asynchronous switching is also investigated. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

To efficiently balance the global search and local search ability,this paper presented a particle swarm optimization(PSO) algorithm with decreasing inertia weight based on Gaussian funtion(GDIWPSO),this algorithm took advantage of the distribution and locality property of Gaussian function to implement nonlinear inertia weight adjustment.In simulation experiment,optimizing the benchmark function to determine the strategy of decreasing inertia weight and comparing the performance with weight of linear decreasing,convex function decreasing and concave function decreasing.The stimulation results show that the proposed PSO algorithm has better improvement in search ability,convergence rate and computation efficiency.

Although BP algorithm can adaptive change with the environment, it can not guarantee that it can find the global optimal solution every time. A hybrid method of an improved genetic algorithm and BP algorithm is proposed to overcome the disadvantages of BP algorithm and that the genetic algorithm is prone to premature. Fault diagnosis results prove that the proposed IAGA-BP method is accuracy and the proposed IAGA-BP method is more high accuracy and smaller error than other methods. © 2010 IEEE.

Profibus, invented by Siemens, has many technological advantages and has been used widely, especially in manufacturing field. High reliability is its significant characteristic, and also focused by users. Most of today's research is about supervisory layer's reliability, and the reliability of the field layer has been little noticed, which is Very important in the whole system in fact. The paper studies the restriction factors in the field layer, and then designs afield layer's redundant system based on Profibus. The reliability difference is discussed compared with the module-level redundant system. Also a redundant Profibus slave station's implementation is presented. Finally the article gives the experiment results, which verify that the given redundant system structure is feasible, and the designed redundant slave station has satisfactory performance.

Now many of the small scale power plants are controlled by the conventional analog instruments with poor performance and reliability. It is an economic way to use the intelligent digital controller to upgrade the automatic control and instrument system of these power plants. In this article the intelligent feature of a new kind of digital controller is described briefly and then its application in the boiler combustion control is discussed. The controlled process is analyzed first and then a control strategy is designed to obtain good control performance with the PID parameters auto-tuned. The stable operation of this control system more than one year proves that the design of this system is successful.