This paper considers the control of discrete linear repetitive processes, which are a distinct class of two-dimensional (2-D) discrete linear systems, i.e. information propagation in two independent directions, which ...
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This paper considers the control of discrete linear repetitive processes, which are a distinct class of two-dimensional (2-D) discrete linear systems, i.e. information propagation in two independent directions, which are of both system-theoretic and applications interest. In this paper we report new results on the design of control laws with guaranteed levels of performance. In particular, develop algorithms for the design of an H infin and lscr 2 - lscr infin dynamic output feedback controller which guarantees that the resulting controlled process is stable and has prescribed disturbance attenuation performance as measured by H infin and lscr 2 - lscr infin norms.
This paper is concerned with the question of, for a physical plant to be controlled, whether or not its internal dynamics and external disturbances can be realistically estimated in real time from its input-output dat...
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This paper is concerned with the question of, for a physical plant to be controlled, whether or not its internal dynamics and external disturbances can be realistically estimated in real time from its input-output data. A positive answer would have significant implications on control system design, because it means that an accurate model of the plant is perhaps no longer required. Based on the linear extended state observer (LESO), it is shown that, for a nth order plant, the answer to the above question is indeed yes. In particular, it is shown that the estimation error (1) converges to the origin asymptotically when the model of the plant is given; (2) is bounded and inversely proportional to the bandwidth of the observer when the plant model is mostly unknown. Note that this is not another parameter estimation algorithm in the framework of adaptive control. It applies to a large class of nonlinear, time-varying processes with unknown dynamics. The solution is deceivingly simple and easy to implement. The results of the mathematical analysis are verified in a simulation study and a motion control hardware test.
Communications in secrecy are often required in many commercial and military applications. Unfortunately, many cryptographic schemes in use today such as public-key cryptography based on the RSA algorithm would be bro...
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Communications in secrecy are often required in many commercial and military applications. Unfortunately, many cryptographic schemes in use today such as public-key cryptography based on the RSA algorithm would be broken with either unanticipated advances in hardware and algorithm or the advent of quantum computers. Quantum cryptography, on the other hand, has been proven secure even against the most general attack allowed by the laws of physics and is a promising technology poised for widespread adoption in realistic cryptographic applications. Quantum cryptography allows two parties to expand on a secret key that they have previously shared. Various quantum cryptographic protocols have been proposed to perform this task. In this paper, we survey some popular quantum cryptographic protocols (including the famous Bennett-Brassard 1984 protocol) and discuss their security. Specifically, we consider their security in two cases: the ideal case where a perfect single-photon source is used and the practical case where a realistic laser source is used. We compare the protocols and find that the efficient six-state protocol outperforms the others both in the tolerable quantum bit error rate and in the key generation rate when a realistic laser source is used.
The cyclic solar activity has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for the prediction of sunspot number, which is a common measure of solar activit...
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The cyclic solar activity has significant effects on earth, climate, satellites and space missions. Several methods have been introduced for the prediction of sunspot number, which is a common measure of solar activity. In this study a co-evolutionary algorithm is presented for inferring the topology and parameters of a multilayered neural network with the minimum of experimentation to the sunspot number time series which will be used as a predictor in predicting such phenomena. The algorithm synthesizes an explicit model directly from the observed data produced by intelligently generated tests. The algorithm is composed of two co-evolving populations; one population evolves candidate neural networks. The second population evolves informative tests that either extract new information from the hidden system or elicit desirable behavior from it. The fitness of candidate neural networks is their ability to explain behavior of the target chaotic system observed in response to all tests carried out so far; the fitness of candidate tests is their ability to make the models disagree in their predictions. The generality of this modeling-evaluation algorithm is demonstrated by applying the chosen model of this algorithm to predict sunspot number and the results depict the power of this training method which yields proper model to predict such chaotic time series.
This paper presents preliminary results on the design of a nonlinear robust controller for an air-breathing hypersonic vehicle model. To overcome the analytical intractability of a dynamical model derived from first p...
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ISBN:
(纸本)1563479044
This paper presents preliminary results on the design of a nonlinear robust controller for an air-breathing hypersonic vehicle model. To overcome the analytical intractability of a dynamical model derived from first principles, a simplified control-oriented model is used for control design. The control-oriented model retains most of the features of the original model, including non-minimum phase characteristic of the flight-path angle dynamics and strong couplings between the engine and flight dynamics, whereas flexibility effects, included in the simulation model, are regarded as a dynamic perturbation. In adopting reduced-complexity models for controller design, the issue of robustness with respect to model uncertainty must be carefully addressed and included at the design level. Dynamic inversion-based design methods do not lend themselves easily to quantitative robustness analysis, due to the complexity of the inverse model of the plant. In this paper, a nonlinear sequential loop-closure approach is adopted to design a dynamic state-feedback controllers that provide stable tracking of velocity and altitude reference trajectories. The approach considered in this paper utilizes a combination of adaptive and robust design methods based on both classic and recently developed nonlinear design tools. Simulation results indicate that the proposed methodology may constitute a feasible approach towards the development of robust nonlinear controllers that satisfactorily address the issue of model uncertainty for this type of applications.
This paper presents a methodology to select input variables for time series prediction. A main motivation is to find some proper input variables which describe the time series dynamics properly. It is shown that even ...
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This paper presents a methodology to select input variables for time series prediction. A main motivation is to find some proper input variables which describe the time series dynamics properly. It is shown that even when the choice of input variables is confined to the lagged values of the process to be predicted, a nonlinear analysis of the most significant factors is crucial for improving the prediction quality. The proposed method is used to select the appropriate input variables for neuro fuzzy models utilized for time series prediction benchmark in NN3 competition as well as a second benchmark to show the generality of the claims. Results depict the effectiveness of the proposed method in proper input selection for neuro fuzzy models for prediction task.
The mitigation of the impact of lightning and the individual control cabinet (ICC) protection of an approach lighting system (ALS) against over-voltage is the focus of this paper. It is the objective of this paper to ...
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The mitigation of the impact of lightning and the individual control cabinet (ICC) protection of an approach lighting system (ALS) against over-voltage is the focus of this paper. It is the objective of this paper to evaluate three proposed ALS topologies and select the grounding configuration which optimally mitigates the high voltage and current levels caused by lightning, as well as to assess the over-voltage reduction capability of a metal-oxide varistor and its ability to provide protection to the individual control cabinet to prevent damage. Simulation of the grounding system of each ALS topology using a sensitivity study, which incorporates changes in conductor and environmental characteristics, is performed to determine the robustness of each ALS topology. Performance of a hands-on destructive test is also carried out to obtain practical results to compare with simulation results. This paper provides the foundation for improving the current ALS grounding and protective measures by updating standards and specifications to promote increased reliability and safety, as well as reduced equipment cost.
It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific f...
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It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (left eye, right eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm uses the whole face features in the first step of recognition task. If the decision machine fails to assign a class (with high confidence) then the individual facial components are processed and the resulting information are combined with those obtained from the whole face to assign the output. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method. Experimental results also show that the proposed system is robust against small errors in facial component extractor.
The unique characteristic of a repetitive process is a series of sweeps or passes through a set of dynamics defined over a finite duration known as the pass length. At the end of each pass, the process is reset and th...
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The unique characteristic of a repetitive process is a series of sweeps or passes through a set of dynamics defined over a finite duration known as the pass length. At the end of each pass, the process is reset and the next time through the output, or pass profile, produced on the previous pass acts as a forcing function on, an hence contributes to, the dynamics of the new pass profile. They are hence a class of systems with information propagation in two independent directions where here the dynamics over the finite pass length are described by a matrix linear differential equation and that from pass to pass has a discrete updating structure. This means that filtering/estimation theory/algorithms for, in particular, 2D discrete linear systems is not applicable. In this paper we solve a general filtering problem with a view toward use in the many applications where such action will be required.
It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific f...
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ISBN:
(纸本)9781424407781
It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm first performs a holistic search using the whole image features and selects probable candidates for further processing. Then the facial features of these probable candidates are compared and classified with the stored patterns in the train set. Finally the classification results are fused with a weighted majority voting to form the final decision. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method.
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