This paper investigates the remote state estimation for the multi-sensor industrial cyber-physical systems (ICPSs) with the help of cognitive radio (CR) technology. The remote state estimator estimates the system stat...
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ISBN:
(纸本)9781509002443
This paper investigates the remote state estimation for the multi-sensor industrial cyber-physical systems (ICPSs) with the help of cognitive radio (CR) technology. The remote state estimator estimates the system states based on the measurements received from multi-sensor via the unreliable communication media, therefore, the performance of remote state estimation is up to the transmission reliability. In this paper, the redundant transmission is adopted to improve the reliability. Furthermore, the CR technology, which can intelligently discover the available spectrum opportunities in licensed bands, is exploited to alleviate the spectrum over-crowd problem aggravated by the redundancy design to improve the state estimation performance by. Specifically, we firstly formulate two CR enabled sequential optimization problems to improve the accuracy of state estimation and enhance the understanding of industrial plant. The primary one is to minimize the CR enabled estimation error subjected to the limited resource. The secondary one is to maximize the CR enabled best-effort data transmission volume subjected to the primary one's solutions. Secondly, a new sequence is constructed to approximate the limit-form objective function of the primary optimization problem. Finally, the two optimization problems are transformed into convex programming with the Lagrangian relaxation and Lagrangian dual decomposition techniques to reduce the computational complexity. Numerical results demonstrate that the CR technology reduces the mean square error of state estimation by about 40% and increases the volume of the best-effort data transmission by about 320%.
In this paper, a distributed event-triggered control strategy is proposed such that consensus of multi-agent systems is achieved subject to input saturation in an output feedback setting. Instead of triggering the con...
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ISBN:
(纸本)9781467374439
In this paper, a distributed event-triggered control strategy is proposed such that consensus of multi-agent systems is achieved subject to input saturation in an output feedback setting. Instead of triggering the controller periodically, the proposed controller is not activated until the response of the system exceeds some pre-defined event-triggered limitation. The case of the output feedback is considered and the allowed saturation approach is introduced to deal with the nonlinearity caused by the input saturation. In the proposed scheme, since no hard constraint is imposed to the control input, we can make full use of the available actuator capacity. In addition, results on the minimization of the frequency of the event-triggered actions are provided.
A multiple activated anti-windup compensated algorithm for Active Disturbance Rejection control(ADRC) mechanism is proposed in this paper. This paradigm extends the multi-activated anti-windup scheme to the active dis...
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ISBN:
(纸本)9781467374439
A multiple activated anti-windup compensated algorithm for Active Disturbance Rejection control(ADRC) mechanism is proposed in this paper. This paradigm extends the multi-activated anti-windup scheme to the active disturbance rejection mechanism, to deal with the input saturation nonlinearity and meanwhile reject the disturbance automatically by using Extended State Observer(ESO). The output of anti-windup compensator is treated as a part of unknown disturbances and is introduced into the ESO, which can online observe both internal and external disturbances(parameter uncertainties and model mismatches). The controller input is yielded by using a nonlinear feedback combination, and it is used to compensate the integrator windup caused by the saturation nonlinearity element. On the other hand, in order to determine the parameters of the ESO and the anti-windup compensator feedback gain, the L2 gain design method is considered. The effectiveness and the robustness against model and parameter uncertainties of the proposed method is verified by an example of the seeker platform.
This paper presents a use of multi-core DSP TMS320C6678 for the squinted-looking synthetic aperture radars (SARs) real-time signal processing method. This paper first briefly introduces the squinted-looking SAR algori...
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ISBN:
(纸本)9781510822023
This paper presents a use of multi-core DSP TMS320C6678 for the squinted-looking synthetic aperture radars (SARs) real-time signal processing method. This paper first briefly introduces the squinted-looking SAR algorithm and analyzes the signal processing by use of spectral analysis (SPECAN). Then, according to the requirement of squinted-looking SAR real-time imaging system, the high performance TI multi-core DSP TMS320C6678 is used for realizing the imaging algorithm through the virtual single-node technology for data transmission and interaction and the parallel approach to data processing. Finally, the flight experiment is carried out and the results show that the virtual processing method meets the requirements of the real-time imaging based on the multi-core DSP hardware platform effectively.
The increasing demands on the indoor location service inspire the wide attentions to investigate the indoor position algorithms. Access point (AP) selection is critical important for increasing the estimation accuracy...
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This paper proposes a statistical method for no-reference image quality assessment using steerable pyramid decomposition without any prior knowledge about the distortions of the original image. Because the means of (l...
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Non-destructive testing (NDT) techniques have been employed in all kinds of fields successfully for many years. Newly developed and applied NDT methods of detecting and monitoring fatigue crack in metal material are r...
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In this paper,a novel randomized step frequency radar with weighted PSO is proposed to recover the range and velocity joint estimating by exploiting sparseness of the targets and invoking compressed sensing(CS) *** th...
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ISBN:
(纸本)9781479970186
In this paper,a novel randomized step frequency radar with weighted PSO is proposed to recover the range and velocity joint estimating by exploiting sparseness of the targets and invoking compressed sensing(CS) *** this algorithm,we abandons the exhaustive list method in the Orthogonal Matching Pursuit(OMP) scheme,which is easy to cause performance degradation in the radar ***,a weighted PSO dynamic optimal method is adopted,where the convergence speed is increased due to the weighted factor introduced in the Particle Swarm Optimization(PSO).The primary advantage of this method lies in being less sensitive to the initial value of target parameters in the case of online optimization ***,this method overcomes the limitation that the initial parameters must be selected close to the true value of the target,which is not constant in many *** is not necessary to know exactly the target parameters when using our approach,instead,coarse coding bounds of target parameters are enough for the algorithm,which can be done once and for all off-line,and it is only necessary to specify the initial scopes of the velocity and the range of the *** results demonstrate that the proposed weighted PSO approach provides a faster convergence speed and robustness against unpredictable perturbations for range and velocity joint estimating in randomized step frequency radar.
This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynami...
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This paper focuses on discovering bursty topics from news stream. Previous work usually apply Kleinberg's modeling of burst to topics estimated by a topic model such as Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM). However, Kleinberg's model is originally proposed for the burst of keywords, the frequency counts it models are not proper to describe the burst states of topics, leading to some unwanted results. A more reasonable way is to model the influence burst states put on each document's topic distribution. Considering this, we propose a unified statistical model that takes the burst states as markov latent variables that influence the topic allocation of documents. We derive a Gibbs sampling algorithm for the proposal. Experiment results confirm our model's advantages both qualitatively and quantitatively.
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