The robust H ∞ control problem for a class of uncertain switched linear systems by using the variable structure control is investigated. A robust H ∞ single sliding surface is shown to exist as long as a convex co...
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The robust H ∞ control problem for a class of uncertain switched linear systems by using the variable structure control is investigated. A robust H ∞ single sliding surface is shown to exist as long as a convex combination of the subsystems of the switched system is robustly stabilizable with disturbance attenuation level γ. A switching law is constructed via the single Lyapunov function technique. Variable structure controllers of subsystems are designed so as the resulting closed-loop system guarantees the robust H ∞ performance. An illustrative example and simulation results are given to demonstrate the effectiveness of the proposed design method.
The paper analyzed and model of a fault tolerant electromechanical controlled worm gear driven fuel shut off valve for aerospace application. The analysis is mainly on design a reduced order fractional controller. Thi...
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In cognitive radio networks (CRNs), localization of primary user is the key problem for location-aware spectrum allocation. In order to improve spectrum utilization and decrease interference to primary users, localiza...
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In cognitive radio networks (CRNs), localization of primary user is the key problem for location-aware spectrum allocation. In order to improve spectrum utilization and decrease interference to primary users, localization should be computationally efficient and as accurate as possible. Based on an accurate closed form localization method, the tradeoff between model accuracy and geometry structure is analyzed. By selecting part of the secondary users, the estimation accuracy is increased and the computation amount is reduced at the same time. Finally, the simulations are performed and the proposed tradeoff analysis is done. The simulation results show the significant improvement on the localization performance by sensor selection.
Under the given operation condition, the operating parameters of large-scale fan vary according to certain rule. With the fan aging, the rule is changing. In order to find the rule of deterioration of condition parame...
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In this paper we present a method for reconstructing static scene viewed through thick smoke using multiple images. Based on spatiotemporal statistical approach our method works well on noisy videos containing swirlin...
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In this paper we present a method for reconstructing static scene viewed through thick smoke using multiple images. Based on spatiotemporal statistical approach our method works well on noisy videos containing swirling smoke. We apply statistical analysis on regions of color input images, and show the way to reconstruct scene by transforming images to alter mean and deviation locally. We introduce a method to extract necessary parameters using multiple frames of a video. We verify our method with the widely used physical model of aerosols, highlighting some differences from removing haze and fog a widely studied area. Furthermore, our approach eliminates the need for complex optimization, making real-time processing possible. Results show that our method is capable of reconstructing scene in challenging cases.
A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural image...
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A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF provides an effective framework to model the statistical prior of natural images and leads to excellent performance in the application of image denoising and inpainting. Moreover, the framework will be extended to image deblurring in our work. Instead of commonly used maximum a-posteriori (MAP) estimation, which has several shortcomings, the high-order NLR-MRF prior is integrated into Bayesian minimum mean squared error (MMSE) estimation framework. Then, an efficient Gibbs sampling algorithm is adopted to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. We perform experiments on synthetic and real-world data to demonstrate the effectiveness of our method. Both quantitatively and qualitatively evaluations show superior or comparable results to the state-of-art deblurring methods.
In many robotic manipulation tasks a robotic hand is used to just grasp and fix the object of interest while the object motion is performed by the arm. Motivated by the analysis on human grasping using data reduction ...
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In many robotic manipulation tasks a robotic hand is used to just grasp and fix the object of interest while the object motion is performed by the arm. Motivated by the analysis on human grasping using data reduction techniques, we applied this concept to the DLR Hand II. Therefore, we analyzed the grasp database that was grown over the past years to find suitable robotic ¿synergy coordinates¿. 74% of these grasps can be represented by two coordinates that were originally defined by 12 joint variables. As a second step, a synergy impedance controller was derived and implemented extending the work on passivity based hand control at DLR. This controller for torque-controlled robot hands allows to imitate the behavior of a synergistic, respectively underactuated, hand. Such a controller provides furthermore a simplified interface for higher level grasping strategies and allows furthermore to manually teach new grasps easily. The controller was evaluated on the DLR Hand II by commanding steps that demonstrate the desired transient behavior. Finally, two objects were successfully grasped validating our approach.
State of the art approaches to autonomous systems face the challenge of sensor data fusion, abstraction, classification, and prediction of events. The trend is going towards the integration of more and more sensors in...
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State of the art approaches to autonomous systems face the challenge of sensor data fusion, abstraction, classification, and prediction of events. The trend is going towards the integration of more and more sensors into automation systems, which will reach a number of sensors comparable to the amount of sensory receptors in the human body in the not too distant future. While today's technical systems cannot cope with such a flood of information to be processed rapidly, these challenges are mastered exceptionally well by the human brain. Based on this observation, in prior work, a biologically inspired model for sensor data processing has been proposed [1]. This socalled neuro-symbolic information processing model is based on a functional model of the human perception system. Here, an extension of this concept to spatial and temporal aspects of perception is presented. The challenges for solving these tasks as well as the strategies to master these challenges based on perception-nets are presented.
This paper is concerned with the problem of admissibility and ℋ ∞ performance of a class of Takagi-Sugeno (T-S) fuzzy descriptor systems with time-delay. A delay-dependent sufficient condition is obtained such that ...
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This paper is concerned with the problem of admissibility and ℋ ∞ performance of a class of Takagi-Sugeno (T-S) fuzzy descriptor systems with time-delay. A delay-dependent sufficient condition is obtained such that the system is regular, impulse-free and asymptotically stable with the ℋ ∞ performance below a prescribed level. The salient feature of the approach include the introduction of a new type of Lyapunov-Krasovskii functional and the technique of delay partitioning. These conditions are formulated in the form of linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate the effectiveness of the proposed theories.
A multi-channel template extraction method is proposed for automatic EEG spike detection. The template is extracted automatically without any prior knowledge. The template extraction algorithm consists of three steps....
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A multi-channel template extraction method is proposed for automatic EEG spike detection. The template is extracted automatically without any prior knowledge. The template extraction algorithm consists of three steps. Firstly, all possible spike events are detected. Secondly, the focus channels are identified for each event. Thirdly, the multi-channel template is extracted for each focus channel. The algorithm produces template adapted to not only individual patient, but also individual focus. The method was evaluated using the recordings from two epilepsy patients. The results suggested that template method should be of multi-channel for spike detection.
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