A new definition of survivability based on constrained Markov Decision Processes is introduced. And a multi-( layered) network survivability framework and a new method of network survivability design are also proposed.

Stability analysis problem is investigated for a class of Markovian jumping genetic regulatory networks with mixed time delays and stochastic perturbations.

研究了带有混合时滞的马尔可夫跳基因调控网络的随机稳定性问题。

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Comparative Study of Continuous Hidden Markov Models and Artificial Neural Network on Speaker Recognition

连续隐马尔可夫模型和神经网络在说话人识别中的比较

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A Model of Spam Zombie Network Evolvement Based on Markov Chains

一个基于马尔可夫链的僵尸网络演化模型

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Through introduce the HMM model and the ANN model, the paper put forward a new model-HMMNN, and accomplished the theoretical analyses.

在训练与识别部分，通过介绍隐含马尔可夫模型和神经网络模型，采用将CDHMM和自组织神经网络结合而形成的新模型－HMMNN，并进行了理论分析。

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( 2) Dynamic Time Warping, Vector Quantization, Hidden Markov Model and Artificial Neural Network can be used in speaker recognition.

现有的说话人识别方法有动态时间规整法、矢量量化法、隐马尔可夫模型和神经网络法等，其中HMM已成为目前最佳的说话人识别处理模型。

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In the stage of real-time assessment hidden markov model was used.

动态实时评估阶段使用隐马尔可夫模型评估网络安全状态，这种基于模型的评估方法增强了实时评估的时效性和可行性。

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A Study of Networked Control Systems Based on Hidden Markov Models

基于隐马尔可夫模型的网络控制系统研究

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The second stage is to joint the Word Sense Disambiguation and Semantic Role Labeling with Markov Logic Networks based on that all word sense is useful for Semantic Role Labeling, and analyze the experimental results.

第二阶段是如果全词词义信息对语义角色标注有帮助，我们通过马尔可夫逻辑网络将全词词义消歧与语义角色标注进行融合，将级联方式和融合方式的实验结果对比，进行分析。

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Network real-time risk assessment based on hidden Markov models

基于隐马尔可夫的网络实时风险评估

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Stability of networked control systems with time-delays governed by Markov chains

马尔可夫时滞网络控制系统的稳定性

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It ′ s showed that the elevator configuration based on the new model can decrease the average waiting time of passengers and the elevator average load effectively. So it is superior and more practicable to model the elevator traffic flow using Markov network queuing theory.

结果表明，基于新模型的电梯配置可有效降低乘客平均候梯时间和电梯平均载荷，从而证明了利用马尔可夫网络排队论建立电梯交通模型的可行性和优越性。

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It is an important and difficult research project to learn decomposable Markov network structure with missing data. Missing data makes the dependency relationship between variables more disordered and it impossible to learn decomposable Markov network structure directly.

具有丢失数据的可分解马尔可夫网络结构学习是一个重要而困难的研究课题，数据的丢失使变量之间的依赖关系变得混乱，无法直接进行可靠的结构学习。

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The traffic flow model is founded based on Markov network queuing theory and the results of the model are applied to elevator configuration. Then this method of elevator configuration is compared with traditional method.

利用马尔可夫网络排队论对电梯交通流建模，并求解该模型，然后将求解结果应用到电梯配置中，通过实例与传统的电梯配置作比较。

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However, the elevator-optimizing configuration based on elevator traffic flow Markov network queuing model, which is according to real traffic flow station and synthetically consider all sorts of factor which influence elevator system configuration.

而基于电梯交通流马尔可夫网络排队模型的电梯配置，根据实际交通流情况，并综合考虑各种影响因素，使得电梯配置方案更加符合实际情况。

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The article applies the Markov network theory to build the elevator traffic model. Based on the model elevator configuration parameters of the serving stations are calculated.

利用马尔可夫网络排队理论建立了电梯交通模型，在此基础上对各服务站的电梯配置交通进行计算，得到电梯的配置参数。

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Then, By Using Markov Network as learning Model, this paper studies the special kind of face image.

然后，以马尔可夫网络为学习模型，研究人脸这一特殊类别图像。

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Then we implement a system, which utilizes Markov network to learn the prior on the high-resolution images.

然后研究基于学习的一般图像超分辨率技术，利用马尔可夫网络来学习图像的先验知识。并且给出了一种基于学习的图像超分辨率算法的系统实现。

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Noise smoothing in learning the parameters of decomposable Markov networks

可分解马尔可夫网络参数学习中的噪声平滑

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Model and Application of Elevator Traffic Based on Markov Network Queuing Theory

基于马尔可夫网络排队论的电梯交通建模及应用

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This thesis adopts Markov network ( MN) model to propose a new frame description restructuring mechanism.

然后，本文采用马尔可夫网络（MN）模型提出了一个新的框架描述重构机制。

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