Robust nonlinear controller design with constraint on the poles' location of the linear part of closed-loop system is proposed. The design method is based on the integrator backstepping procedure and linear constr...
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The problem of designing a linear controller for nonlinear polynomial systems that results the largest domain of attraction is considered. Moreover, using the proposed algorithm, the shape of the approximation of doma...
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This paper addresses the design of state feedback H∞ robust controller that satisfies additional constraints on closed-loop poles location. Desired closed-loop poles location is considered to be a clipped sector, cor...
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In this paper, we propose a modified probabilistic neural network, and applied it to brain magnetic resonance (MR) image segmentation. Comparing to conventional probabilistic neural network (PNN), the so-called weight...
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In this presentation, we show some counter-examples to a naive belief that the security of QKD is based on no-cloning theorem. One example is shown by explicitly proving that one can indeed generate an unconditionally...
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ISBN:
(纸本)0780391519
In this presentation, we show some counter-examples to a naive belief that the security of QKD is based on no-cloning theorem. One example is shown by explicitly proving that one can indeed generate an unconditionally secure key from Alice's two-photon emission part in "SARG04 protocol" proposed by V. Scarani et al., in Phys. Rev. Lett. 92, 057901 (2004). This protocol differs from BB84 only in the classical communication. It is, thus, interesting to see how only the classical communication of QKD protocol might qualitatively change its security. We also show that one can generate an unconditionally secure key from the single to the four-photon part in a generalized SARG04 that uses six states. Finally, we also compare the bit error rate threshold of these protocols with the one in BB84 and the original six-state protocol assuming a depolarizing channel.
The accurate classification of protein structure is critical and essential for protein function determination in Bioinformatics and Proteomics. A reasonably high rate of prediction accuracy for protein structure class...
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The accurate classification of protein structure is critical and essential for protein function determination in Bioinformatics and Proteomics. A reasonably high rate of prediction accuracy for protein structure classification has been achieved recently in coarse-grained protein class assignment according to their primary amino acid sequences, such as classifying proteins into four classes in SCOP. However, it is still quite a challenge for fine-grained protein fold assignment, especially when the number of possible folding patterns as those defined in SCOP is large. In our previous work, hierarchical learning architecture (HLA) neural networks have been used to differentiate proteins according to their classes and folding patterns. A better prediction accuracy rate for 27 folding categories was 65.5% which improves previous results by Ding and Dubchak with 56.5% prediction accuracy rate. The success of the protein structure classification depends heavily on the computational methods used and the features selected. Here combinatorial fusion analysis (CFA) techniques are used to facilitate feature selection and combination for improving prediction accuracy rate of protein structure classification. The resulting classification has an overall prediction accuracy rate of 87.8% for coarse-grained 4 classes and 70.9% for fine-grained 27 folding categories by applying the concept of CFA to our previous work using neural network with the HLA framework. These results are significantly higher than others and our previous work. They further demonstrate that the CFA techniques can greatly enhance the machine learning method (such as NN in the paper) in the protein structure prediction problem.
in this paper, now types of Recursive least-square (RLS) algorithms, without using the initial information of a parameter or a state to be estimated, are proposed. The proposed RLS algorithm is first obtained for a ge...
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In this paper, we deal with the issue of robust delay-independent asymptotic stability and robust disturbance attenuation problem for linear parameter-dependent systems. Using Hamiltonian-Jacoby-Isaac approach, a para...
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In this paper, an intelligent controller is applied to speed control of a switched reluctance motor. Two techniques are used which have been successfully used in other intelligent modeling and control applications. Fi...
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In this paper, fuzzy sliding mode controller is designed to govern the dynamics of switched reluctance motor. First, magnetic characteristics data of the motor obtained by Finite Element Method (FEM) is utilized for a...
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