基于马尔科夫链的导弹部队生存状态分析

针对导弹部队作战过程的动态性和无后效性,利用马尔科夫链方法研究导弹部队生存状态问题。通过分析各种因素在不同阶段对生存能力的影响,构建了不同阶段马尔科夫状态转移矩阵,计算得到不同阶段部队处于各种状态的概率,以分析部队在不同阶段的生存状况。

基于马尔科夫链的导弹部队生存状态分析

针对导弹部队作战过程的动态性和无后效性,利用马尔科夫链方法研究导弹部队生存状态问题。通过分析各种因素在不同阶段对生存能力的影响,构建了不同阶段马尔科夫状态转移矩阵,计算得到不同阶段部队处于各种状态的概率,以分析部队在不同阶段的生存状况。

QoS-based MAC protocol for cognitive radio networks

In order to improve the throughput performance of the secondary users （SUs） in the cognitive radio （CR） environment, a quality of service （QoS） based media access control （MAC） protocol is proposed. In this protocol, the CR node maps the channel state as a vector, and the transmitter and the receiver obtain the final channel map through an AND operation to prepare for an optional channel set. Data from the upper application layer are classified into two priority levels according to the QoS requirement. The data of each level relate to different contention windows so that the priority of real time data can be guaranteed. A two-dimensional discrete-time Markov chain is utilized to evaluate the system performance, and mathematical expressions of the system throughput are derived. Simulation results show that compared with the IEEE 802. 11 distributed coordination function （DCF）, the proposed MAC protocol can achieve higher throughput.

Modular solution of dynamic multiple-phased systems

A new modular solution to the state explosion problem caused by the Markov-based modular solution of dynamic multiple-phased systems is proposed. First, the solution makes full use of the static parts of dynamic multiple-phased systems and constructs cross-phase dynamic modules by combining the dynamic modules of phase fault trees. Secondly, the system binary decision diagram （BDD） from a modularized multiple- phased system （MPS）is generated by using variable ordering and BDD operations. The computational formulations of the BDD node event probability are derived for various node links and the system reliability results are figured out. Finally, a hypothetical multiple-phased system is given to demonstrate the advantages of the dynamic modular solution when the Markov state space and the size of the system BDD are reduced.

Applicability of Markov chain-based stochastic model for bubbling fluidized beds

A Markov chain-based stochastic model （MCM） is developed to simulate the movement of particles in a 2D bubbling fluidized bed （BFB）. The state spaces are determined by the discretized physical cells of the bed, and the transition probability matrix is directly calculated by the results of a discrete element method （DEM） simulation. The Markov property of the BFB is discussed by the comparison results calculated from both static and dynamic transition probability matrices. The static matrix is calculated based on the Markov chain while the dynamic matrix is calculated based on the memory property of the particle movement. Results show that the difference in the trends of particle movement between the static and dynamic matrix calculation is very small. Besides, the particle mixing curves of the MCM and DEM have the same trend and similar numerical values, and the details show the time averaged characteristic of the MCM and also expose its shortcoming in describing the instantaneous particle dynamics in the BFB.

Discrete-time Markov-based dynamic control approach for compressed sampling

To solve the problem that the signal sparsity level is time-varying and not known as a priori in most cases,a signal sparsity level prediction and optimal sampling rate determination scheme is proposed.The discrete-time Markov chain is used to model the signal sparsity level and analyze the transition between different states.According to the current state,the signal sparsity level state in the next sampling period and its probability are predicted.Furthermore,based on the prediction results,a dynamic control approach is proposed to find out the optimal sampling rate with the aim of maximizing the expected reward which considers both the energy consumption and the recovery accuracy.The proposed approach can balance the tradeoff between the energy consumption and the recovery accuracy.Simulation results show that the proposed dynamic control approach can significantly improve the sampling performance compared with the existing approach.

Forecasting freight volume based on wavelet denoising and FG-Markov

To eliminate the grey bias and improve ant-jamming performance of the standard grey-Markov forecasting model,a forecasting model based on wavelet packet decomposition and fuzzy grey Markov(FG-Markov)is proposed considering the characteristics of randomness and nonlinearility of freight volume forecasting.Firstly,based on the data analysis ability of wavelet packet to non-stationary random signal,wavelet packet decomposition is used to improve the analysis ability of data signal by decomposing historical freight volume data into wavelet packet component.On this basis,FG-Markov chain is proposed to obtain the transfer probability matrix of wavelet packet coefficients by introducing fuzzy grey variables,and forecast the freight volume by reconstructing wavelet packet coefficients.Finally,an example of Lanzhou railroad hub is carried out in order to testify the validity and applicability of this forecasting model.Compared with neural network model and other forecasting models,the proposed forecasting model can improve the forecasting accuracy under the same conditions.The forecasting accuracy of wavelet packet decomposition and FG-Markov is not only greater than that of any other single forecasting models,but also superior to that of other traditional combinational forecasting models,which can meet the actual requirements of freight volume forecasting.

Spatio-temporal Dynamic Evolution and the Factors Impacting Eco-efficiency in Chengdu-Chongqing Economic Circle

In response to the 14th National Five-year Plan of China and to better explore new strategies for promoting regional coordinated green development, the eco-efficiency values of Chengdu-Chongqing Economic Circle and the corresponding temporal analysis from 2004 to 2018 were assessed in this paper using the super-SBM model and Markov chain. Meanwhile, the spatial analysis of eco-efficiency was conducted by a geographically weighted regression model. Although eco-efficiency has risen at an increasing rate, the economic development of Chengdu-Chongqing Economic Circle was still ecologically ineffective. This means there is an urgent need to improve the efficiency of resource utilization and promote technological innovation. During the study period, the evolution of the eco-efficiency presented as a “π” shape, and was accompanied by the phenomenon of “club convergence”. There was also a strong tendency for eco-efficiency to maintain the original status quo, which indicates that it lacked sufficient momentum for improvement, so it was difficult to achieve a leapfrog transfer. Spatially, the eco-efficiency was distributed from northwest to southeast in a high-low-high manner. The spatial-temporal differences of eco-efficiency narrowed but the effect of agglomeration was relatively weak and there was a polarization trend. Further investigation suggests that the differences in the development level of urbanization, opening, technology, environmental regulation and advancement of industrial structure led to the spatial differences of eco-efficiency. Each city in the Economic Circle should make every effort to improve eco-efficiency accordingly, and thus to promote the green development of the whole region, so as to lay a foundation for driving the green and coordinated development of the central and western regions.

STUDYING THE IDENTIFIABILITY OF EPIDEMIOLOGICAL MODELS USING MCMC

Studying different theoretical properties of epidemiological models has been widely addressed, while numerical studies and especially the calibration of models, which are often complicated and loaded with a high number of unknown parameters, against mea- sured data have received less attention. In this paper, we describe how a combination of simulated data and Markov Chain Monte Carlo （MCMC） methods can be used to study the identifiability of model parameters with different type of measurements. Three known models are used as case studies to illustrate the importance of parameter identi- fiability： a basic SIR model, an influenza model with vaccination and treatment and a HIV-Malaria co-infection model. The analysis reveals that calibration of complex models commonly studied in mathematical epidemiology, such as the HIV Malaria co-dynamics model, can be difficult or impossible, even if the system would be fully observed. The pre- sented approach provides a tool for design and optimization of real-life field campaigns of collecting data, as well as for model selection.

Implicit Sampling,with Application to Data Assimilation

There are many computational tasks, in which it is necessary to sample a given probability density function （or pdf for short）, i.e., to use a computer to construct a sequence of independent random vectors x~ （i ---- 1, 2, ~ ~ ~ ）, whose histogram converges to the given pdf. This can be difficult because the sample space can be huge, and more importantly, because the portion of the space, where the density is significant, can be very small, so that one may miss it by an ill-designed sampling scheme. Indeed, Markov- chain Monte Carlo, the most widely used sampling scheme, can be thought of as a search algorithm, where one starts at an arbitrary point and one advances step-by-step towards the high probability region of the space. This can be expensive, in particular because one is typically interested in independent samples, while the chain has a memory. The authors present an alternative, in which samples are found by solving an algebraic equation with a random right-hand side rather than by following a chain; each sample is independent of the previous samples. The construction in the context of numerical integration is explained, and then it is applied to data assimilation.

OCCUPATION TIME PROCESSES OF FLEMING-VIOT PROCESSES

Suppose that Xt is the Fleming-Viot process associated with fractional power Laplacian operator -(-△)α/2 0 < α≥ 2, and Yt= f_0￣t Xs.ds is the so-called occupation time process.In this paper) the asymptotic behavior at a large time and the absolute continuity of Yt are investigated.