马尔可夫模型的建立及其在证券市场中的应用

模型假设股票价格变化满足齐次马氏性,并具有涨跌两种状态,初始概率的分布是平稳分布,建立了相应的模型,给出了模型中未知参数的极大似然估计,并将模型应用于确定上证综合指数、深证成指及个股的涨跌趋势,得到了令人满意的结果.

茂名小良林地资源类型动态模型预测

通过对小良林地资源类型利用状态的研究,根据样地资源的变化资料,应用马尔可夫法的分析理论,建立预测模型,以分析、预测林地资源利用的动态变化,以寻求一个稳定状态,为林地利用规划和经济发展决策提供科学的依据。

关于高炉炼铁中热风炉送风温度预测的研究

高炉炼铁作业时需要多个热风炉连续交替输送高热空气,以强化高炉冶炼、减少用煤量、增加产量,而烧炉期炉温的控制是最为重要的一环,空燃比的设定需要蓄热室平均温度的准确预测,提出了一种采用核模糊c-means的马尔可夫预测法,将热风炉送风温度记录看作是无后效性的事物,利用马尔可夫法实施预测,考虑到工业控制的复杂性,引入了改进的c-means算法,计算下一次送风终了时蓄热室的平均温度,以计算燃烧中所需煤气量。实验结果证明,改进后的聚类算法准确度提高明显;在与传统预测值比较中,改进的预测方法在温度区间划定以及温度值的拟合度都具备优势,为热风炉温度优化预测提供了依据。

艾滋病潜伏期研究方法

对艾滋病潜伏期的研究是艾滋病流行病学研究的重要内容,近十几年来国外对艾滋病潜伏期的研究方法进行了大量探索,其研究模型可分为参数、非参数两类,针对不同监测数据提出了不同的分析方法。本文就国外的研究模型和方法进行综述,并对不同的参数模型进行了比较。

电动汽车城市行驶工况构建

通过采集电动汽车及传统汽车的实际行驶数据,使用K-均值聚类法和马尔可夫链理论构建电动汽车与传统汽车城市行驶工况。构建出的城市行驶工况与当下较为流行的传统汽车的行驶工况进行比较。结果表明,电动汽车行驶工况与传统汽车行驶工况的加速度有较大的不同;这是因为电动汽车的电机扭矩响应速度快,低速时可以输出大扭矩。构建出的电动汽车行驶工况还可以作为评价电动汽车的经济性与碳排放的依据。

An adaptive genetic algorithm with diversity-guided mutation and its global convergence property

An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of (adaptive) genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.

Residual life estimation based on bivariate Wiener degradation process with measurement errors

An adaptive method of residual life estimation for deteriorated products with two performance characteristics (PCs) was proposed, which was sharply different from existing work that only utilized one-dimensional degradation data. Once new degradation information was available, the residual life of the product being monitored could be estimated in an adaptive manner. Here, it was assumed that the degradation of each PC over time was governed by a Wiener degradation process and the dependency between them was characterized by the Frank copula function. A bivariate Wiener process model with measurement errors was used to model the degradation measurements. A two-stage method and the Markov chain Monte Carlo (MCMC) method were combined to estimate the unknown parameters in sequence. Results from a numerical example about fatigue cracks show that the proposed method is valid as the relative error is small.

Pattern recognition of optimal traffic path based on HMM

In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate and poor instability of traditional model algorithms.At first,the HHM is obtained by training.Then according to dynamic planning principle,the traffic states of intersections are obtained by the Viterbi algorithm.Finally,the optimal path is selected based on the obtained traffic states of intersections.The experiment results show that the proposed method is superior to other algorithms in road unobstruction rate and recognition rate under complex road conditions.

Survivability modeling and analysis on 3D mobile ad-hoc networks

Most existing work on survivability in mobile ad-hoc networks(MANETs) focuses on two dimensional(2D) networks.However,many real applications run in three dimensional(3D) networks,e.g.,climate and ocean monitoring,and air defense systems.The impact on network survivability due to node behaviors was presented,and a quantitative analysis method on survivability was developed in 3D MANETs by modeling node behaviors and analyzing 3D network connectivity.Node behaviors were modeled by using a semi-Markov process.The node minimum degree of 3D MANETs was discussed.An effective approach to derive the survivability of k-connected networks was proposed through analyzing the connectivity of 3D MANETs caused by node misbehaviors,based on the model of node isolation.The quantitative analysis of node misbehaviors on the survivability in 3D MANETs is obtained through mathematical description,and the effectiveness and rationality of the proposed approach are verified through numerical analysis.The analytical results show that the effect from black and gray attack on network survivability is much severer than other misbehaviors.

Two-stage approach to full Chinese parsing

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

Nash Model Parameter Uncertainty Analysis by AM-MCMC Based on BFS and Probabilistic Flood Forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hy-drologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simu-lated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.

One-step random-walk process of nanoparticles in cement-based materials

Efficient modelling approaches capable of predicting the behavior and effects of nanoparticles in cement-based materials are required for conducting relevant experiments.From the microstructural characterization of a cement-nanoparticle system,this paper investigates the potential of cell-based weighted random-walk method to establish statistically significant relationships between chemical bonding and diffusion processes of nanoparticles within cement matrix.LaSr_(0.5)C_(0.5)O_(3)(LSCO)nanoparticles were employed to develop a discrete event system that accounts for the behavior of individual cells where nanoparticles and cement components were expected to interact.The stochastic model is based on annihilation(loss)and creation(gain)of a bond in the cell.The model considers both chemical reactions and transport mechanism of nanoparticles from cementitious cells,along with cement hydration process.This approach may be useful for simulating nanoparticle transport in complex 2D cement-based materials systems.

SEMI-BLIND CHANNEL ESTIMATION OF MULTIPLE-INPUT/MULTIPLE-OUTPUT SYSTEMS BASED ON MARKOV CHAIN MONTE CARLO METHODS

This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.

Cost of treating chronic hepatitis B:Comparison of current treatment guidelines

AIM:To compare program costs of chronic hepatitis B(CHB) screening and treatment using Australian and other published CHB treatment guidelines.METHODS:Economic modeling demonstrated that in Australia a strategy of hepatocellular cancer(HCC) prevention in patients with CHB is more cost-effective than current standard care,or HCC screening.Based upon this model,we developed the B positive program to optimize CHB management of Australians born in countries of high CHB prevalence.We estimated CHB program costs using the B positive program algorithm and compared them to estimated costs of using the CHB treatment guidelines published by the AsianPacific,American and European Associations for the Study of Liver Disease(APASL,AASLD,EASL) and those suggested by an independent United States hepatology panel.We used a Markov model that factored in the costs of CHB screening and treatment,individualized by viral load and alanine aminotransferase levels,and calculated the relative costs of program components.Costs were discounted by 5% and calculated in Australian dollars(AUD).RESULTS:Using the B positive algorithm,total program costs amount to 13 979 224 AUD,or 9634 AUD per patient.The least costly strategy is based upon using the AASLD guidelines,which would cost 34% less than our B positive algorithm.Using the EASL and the United States Expert Group guidelines would increase program costs by 46%.The largest expenditure relates to the cost of drug treatment(66.9% of total program costs).The contribution of CHB surveillance(20.2%) and HCC screening and surveillance(6.6%) is small-and together they represent only approximately a quarter of the total program costs.CONCLUSION:The significant cost variations in CHB screening and treatment using different guidelines are relevant for clinicians and policy makers involved in designing population-based disease control programs.

Markov Chain Induced by Random Dynamical System on Graph

In this paper, we define a model of random dynamical systems（RDS） on graphs and prove that they are actually homogeneous discrete-time Markov chains. Moreover, a necessary and sufficient condition is obtained for that two state vectors can communicate with each other in a random dynamical system（tLDS）.

An efficient priority service model with two-level-polling scheme

This paper considers an efficient priority service model with two-level-polling scheme which the message packets conform to the discrete-time Geom/G/1 queue with multiple vacations and bulk arrival. By the embedded Markov chain theory and the probability generating function method, we set up the mathematics functions and give closed form expressions for obtaining the mean cyclic period （MCP）, the mean queue length （MQL） and the mean waiting time （MWT） characteristics, the analytical results are also verified through extensive computer simulations. The performance analysis reveals that this priority polling scheme can gives better efficiency as well as impartiality in terms of system characteristics, and it can be used for differentiating priority service to guarantee better QoS and system stability in design and improvement of MAC protocol.

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

The State Equations Methods for Stochastic Control Problems

The state equations of stochastic control problems,which are controlled stochastic differential equations,are proposed to be discretized by the weak midpoint rule and predictor-corrector methods for the Markov chain approximation approach. Local consistency of the methods are proved.Numerical tests on a simplified Merton's portfolio model show better simulation to feedback control rules by these two methods, as compared with the weak Euler-Maruyama discretisation used by Krawczyk.This suggests a new approach of improving accuracy of approximating Markov chains for stochastic control problems.

Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems

Molecular dynamics （MD） simulation has become a powerful tool to investigate the structure- function relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets con- taining millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape （geometric clustering） and kinetics （kinetic clustering）. In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, ag- glomerative algorithms （single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage）, center-based algorithms （K-Means, K-Medoids, K-Centers, and APM）, density-based algorithms （neighbor-based, DBSCAN, density-peaks, and Robust-DB）, and spectral-based algorithms （PCCA and PCCA＋）. In particular, differences between geomet- ric and kinetic clustering metrics will be discussed along with the performances of diflhrent clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algo- rithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.

Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule

According to the sequential maximum a posteriori probability (SMAP) rule, this paper proposes a novel multi-scale Bayesian texture segmentation algorithm based on the wavelet domain Hidden Markov Tree (HMT) model. In the proposed scheme, interscale label transition probability is directly defined and resoled by an EM algorithm. In order to smooth out the variations in the homogeneous regions, intrascale context information is considered. A Gaussian mixture model (GMM) in the redundant wavelet domain is also exploited to formulate the pixel-level statistical features of texture pattern so as to avoid the influence of the variance of pixel brightness. The performance of the proposed method is compared with the state-of-the-art HMTSeg method and evaluated by the experiment results.