In the past nearly two decades, DNA self-assembly technology as a promising technology, a body of laboratory work has been emerged in an endless stream. Single-stranded DNA tile (SST) assembly provides a simple, modul...
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Disrupting the circadian rhythms will cause health problems such as sleep disorders, memory disorders and obesity. Finding the suitable external stimulation to synchronize a model with a desired phase is a biologicall...
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ISBN:
(纸本)9781467374439
Disrupting the circadian rhythms will cause health problems such as sleep disorders, memory disorders and obesity. Finding the suitable external stimulation to synchronize a model with a desired phase is a biologically significant issue. The phase control of circadian rhythms for Drosophila is considered from control engineering viewpoint in this paper. If all parameters of the model are known, we can use the feedback linearization to design a tracking controller for the phase ***, in practice, parameters uncertainties always exist. To deal with this problem, a slide-mode controller is proposed for the phase tracking control of circadian rhythms. The simulation results show the effectiveness of the proposed method.
This paper proposes a dependency-enhanced pre-reordering method for Chinese-English statistical machine translation(SMT).Firstly,two kinds of dependency structure-based rules are extracted based on the source-side dep...
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ISBN:
(纸本)9781509009107
This paper proposes a dependency-enhanced pre-reordering method for Chinese-English statistical machine translation(SMT).Firstly,two kinds of dependency structure-based rules are extracted based on the source-side dependency tree and corresponding word alignments between the source-side and the target-side *** a maximum entropy classifier is used to calculate the orientation probability in terms of swap or monotone between two *** a result,a reordering rule set is *** different ways are proposed to filter out the rule ***,the dependency parsing trees of the training data,development set and the test set are traversed,and if the syntactic sub-tree structure matches the rules in the rule set,the word orders will be ***,a reordered source-side sentence is generated and then fed into an SMT system for *** conducted on NIST Chinese-English MT data sets show that the proposed method significantly improves translation performance by 0.46 BLEU compared to the baseline system.
Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian...
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ISBN:
(纸本)9781467374439
Nonlinear system identification is one of the most important topics all over the word. Until now, there are many of off-line identification methods which exhibit well performance. The online approach with non-Gaussian noise, however, is still a challenge. For a class of nonlinear systems where all of the candidate parameters are contained in a definite parameter set, an online parameters and sates estimation method is proposed based on particle filter and Bayes theorem as the following steps. Firstly, regarding all of the candidates, the states are estimated by particle filter(PF) algorithm. Secondly the posterior probabilities of all of candidates are calculated according to the Bayes theorem;then the weights of all of the candidates are obtained through normalization. Lastly, the parameters and sates are estimated ultimately according to the weighted sum of all of the candidates and states. Numerical illustrations are presented to exhibit the application of the method proposed herein, and the performance of the method is examined.
Spiking neural P systems are a class of dis- tributed parallel computing models inspired from the way neurons communicate with each other by means of electri- cal impulses (called "spikes"). In this paper, w...
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Spiking neural P systems are a class of dis- tributed parallel computing models inspired from the way neurons communicate with each other by means of electri- cal impulses (called "spikes"). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the gener- ating and accepting modes).
To solve the problem of low performance of network intrusion detection,a deep learning intrusion detection model based on space-time fusion features and attention mechanism—CLT-net is *** this model,space-time fusion...
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ISBN:
(纸本)9781665431293
To solve the problem of low performance of network intrusion detection,a deep learning intrusion detection model based on space-time fusion features and attention mechanism—CLT-net is *** this model,space-time fusion features are obtained by integrating convolutional neural network and long short-time memory network,and attention module is added to calculate the importance of the input features,and softmax function is used for *** a large number of simulation experiments on NSL-KDD data sets,CLT-net has significantly improved the convergence of the training set and the accuracy of the test *** with the traditional CNN model with similar structure and the space-time fusion CLSTM the accuracy of the model increased by 11.8% and 10.9%*** shows that this model has great potential in the application field of network intrusion detection.
In this paper, a method for a sort of nonlinear system identification with stochastic time-varying parameter is investigated. This kind of nonlinear systems is referring to the system where probability density functio...
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ISBN:
(纸本)9781509009107
In this paper, a method for a sort of nonlinear system identification with stochastic time-varying parameter is investigated. This kind of nonlinear systems is referring to the system where probability density functions(PDFs) of the parameters are known. This parameter identification and states estimation method is realized based on expectation maximization(EM) algorithm and particle filter. Firstly, parameter particles are generated randomly according to the PDF of parameter. Secondly, the particle filter is employed to estimate system states corresponding to each group of the parameters, and the weight of each group parameters is calculated according to the Bayesian theory. Then the new iteration of parameter is obtained by adopting the expectation maximization algorithm. Lastly, the real parameters are obtained along with system operation. Numerical illustrations are presented to exhibit the effectiveness of the method proposed herein, and the performance of the method is examined.
The thesis studies the semi-global scaled edge-consensus of linear discrete-time multi-agent systems under both the directed networks and undirected networks, where the states of each edge are subject to input saturat...
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A new defect detection algorithm base on Support Vector Data Description (SVDD) is proposed. A fabric texture model is built on the gray-level histogram of textural fabric image. Two Gray-level Co-occurrence Matrix (G...
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In this paper, a center matching scheme is proposed for constructing a consensus function in the k-means cluster ensemble learning. Each k-means clusterer outputs a sequence with k cluster centers. We randomly select ...
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