Forecast of Flood in Chaohu Lake Basin of China Based on Grey-Markov Theory

Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.

Spectrum Sensing Model for Wireless Internet of Things

In the last few years, the number of devices operating in wireless Internet of Things （IoT） has experienced tremendous growth. On the other hand, the growth results in spectrum scarcity. Cog- nitive Radio （CR） systems have been proposed to efficiently exploit the spectra that have been assigned but are underutilized. In this paper, a spectrum sensing model based on Markov chain is proposed to predict the spectrum hole for CR in wireless IoT. Theoretical analysis and simulation results have been evaluated that a Markov model with two- state or four-state works well enough in wireless loT whereas a model with more states is not necessary for it is complex.

The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

A new analytical algorithm for computing probability distribution of project completion time

An analytical algorithm was presented for the exact computation of the probability distribution of the project completion time in stochastic networks,where the activity durations are mutually independent and continuously distributed random variables. Firstly,stochastic activity networks were modeled as continuous-time Markov process with a single absorbing state by the well-know method of supplementary variables and the time changed from the initial state to absorbing state is equal to the project completion time.Then,the Markov process was regarded as a special case of Markov skeleton process.By taking advantage of the backward equations of Markov skeleton processes,a backward algorithm was proposed to compute the probability distribution of the project completion time.Finally,a numerical example was solved to demonstrate the performance of the proposed methodology.The results show that the proposed algorithm is capable of computing the exact distribution function of the project completion time,and the expectation and variance are obtained.

A Novel Dynamic Decision Model in 2-player Symmetric Repeated Games

Considering the dynamic character of repeated games and Markov process, this paper presented a novel dynamic decision model for symmetric repeated games. In this model, players＇ actions were mapped to a Markov decision process with payoffs, and the Boltzmann distribution was intousluced. Our dynamic model is different from others＇ , we used this dynamic model to study the iterated prisoner＇ s dilemma, and the results show that this decision model can successfully be used in symmetric repeated games and has an ability of adaptive learning.

On strong Markov property for Fleming-Viot processes

In this note,we prove that any Fleming-Viot process on a Polish space is strongly Markovian provided so is the mutation process.

Number-resolved master equation approach to quantum transport under the self-consistent Born approximation

We construct a particle-number （n）-resolved master equation （ME） approach under the self-consistent Born approximation （SCBA） for quantum transport through mesoscopic systems. The formulation is essentially non-Markovian and incorporates the interplay of the multi-tunneling processes and many-body correlations. The proposed n-SCBA-ME goes beyond the scope of the Born- Markov master equation, being applicable to transport under small bias voltage, in non-Markovian regime and with strong Coulomb correlations. For steady state, it can recover not only the exact result of noninteracting transport under arbitrary voltages, but also the challenging nonequilibrium Kondo effect. Moreover, the n-SCBA-ME approach is efficient for the study of shot noise. We demonstrate the application by a couple of representative examples, including particularly the nonequilibrium Kondo system.

Non-Markovianity Measure Based on Brukner–Zeilinger Invariant Information for Unital Quantum Dynamical Maps

A non-Markovianity measure based on Brukner–Zeilinger invariant information to characterize nonMarkovian effect of open systems undergoing unital dynamical maps is proposed. The method takes advantage of non-increasing property of the Brukner–Zeilinger invariant information under completely positive and trace-preserving unital maps. The simplicity of computing the Brukner–Zeilinger invariant information is the advantage of the proposed measure because of mainly depending on the purity of quantum state. The measure effectively captures the characteristics of non-Markovianity of unital dynamical maps. As some concrete application, we consider two typical non-Markovian noise channels, i.e., the phase damping channel and the random unitary channel to show the sensitivity of the proposed measure. By investigation, we find that the conditions of detecting the non-Markovianity for the phase damping channel are consistent with the results of existing measures for non-Markovianity, i.e., information flow, divisibility and quantum mutual information. However, for the random unitary channel non-Markovian conditions are same to that of the information flow, but is different from that of the divisibility and quantum mutual information.

A QUANTITATIVE MODEL FOR INTRADAY STOCK PRICE CHANGES BASED ON ORDER FLOWS

This paper proposes a double Markov model of the double continuous auction for describing intra-day price changes. The model splits intra-day price changes as the repetition of one tick price moves and assumes order arrivals are independent Poisson random processes. The dynamic process of price formation is described by a birth-death process of the double M/M/1 server queue corresponding to the best bid/ask. The initial depths of the best bid and ask are defined as different constants depending on the last price change. Thus, the price changes in the model follow a first-order Markov process. As the initial depth of the best bid/ask is originally larger than that of the opposite side when the last price is down/up, the model may explain the negative autocorrelations of the price of the best bid/ask. The estimated parameters are based on the real tick-by-tick data of the Nikkei 225 futures listed in Osaka Stock Exchanges. The authors find the model accurately predicts the returns of Osaka Stock Exchange average.

Enhancement of Quantum Correlations in Qubit-Qutrit Systems under the non-Markovian Environment

We investigate the time evolution of quantum correlations of a hybrid qubit-qutrit system under the classical Ornstein-Uhlenbeck(OU) noise. Here we consider two different one-parameter families of qubit-qutrit states which independently interact with the non-Markovian reservoirs. A comparison with the Markovian dynamics reveals that for the same set of initial condition parameters, the non-Markovian behavior of the environment plays an important role in the enhancement of the survival time of quantum correlations. In addition, it is observed that the non-Markovian strength(γ/Γ) has a positive impact on the correlations time. For the initial separable states it is found that there is a finite time interval in which the geometric quantum discord is frozen despite the presence of a noisy environment and that interval can be further prolonged by using the non-Markovian property. Moreover, its decay can be significantly delayed.