TO IMPROVE THE COMMUNICATION DELAY BYUPGRADING NODES IN A CONTINUOUS VERSION

In this paper, we consider a network communication delay improvement prob-lem, which is to upgrade nodes in a network with minimum cost such that the communication delay between any two nodes of the network is below a pre-specific level. In the upgrading model, the improvement by upgrading one node is a continuous variable, and the cost incurred by such an upgrading is a linear function of the improvement. We show that achieving an approximation ratio β In(|V|) for the problem is NP-hard for some constant β＞0 even if the underlying network is a bipartite graph. But if the underlying network is restricted as a tree, we show that it can be solved in a strongly polynomial time.     

Improved Algorithm for Minimum Data Aggregation Time Problem in Wireless Sensor Networks

Wireless sensor networks promise a new paradigm for gathering data via collaboration among sensors spreading over a large geometrical region. Many applications impose delay requirements for data gathering and ask for time-efficient schedules for aggregating sensed data and sending to the data sink. In this paper, the authors study the minimum data aggregation time problem under collision-free transmission model. In each time round, data sent by a sensor reaches all sensors within its transmission range, but a sensor can receive data only when it is the only data that reaches the sensor. The goal is to find the method that schedules data transmission and aggregation at sensors so that the time for all requested data to be sent to the data sink is minimal. The authors propose a 7△/log2｜s｜＋c, new approximation algorithm for this NP-hard problem with guaranteed performance ratio which significantly reduces the current best ratio of △- 1, where S is the set of sensors containing source data, A is the maximal number of sensors within the transmission range of any sensor, and e is a constant. The authors also conduct extensive simulation, the obtained results justify the improvement of proposed algorithm over the existing one.     

多阶段分散化投资组合效率估计

The existing multi-period portfolio diversification performance evaluation models are mainly divided into two categories. The first category is to build a performance evaluation model based on the return rate of portfolio at each period. The other category is to evaluate portfolio performance by using efficient frontier. However, the former ignores the dynamic relationship among the time periods, while the latter has limitations when there exists friction in financial market. To address this problem, the actual investment of the input-output process was clarified and defined, and the definition of multi-period portfolio efficiency were given based on the distance between portfolio under evaluation and its projection point on the efficient frontier. Under the multi-period mean-variance theoretical framework, the middle connection condition in the multi-period portfolio evaluation problem was derived by using the wealth dynamic process, so as to build a two-period portfolio efficiency estimation model under different orientation. Since the above-mentioned middle connection condition is random, it not workable in actual evaluation. Therefore, in this paper, the first moment and second moment of the above random conditions were used to replace the original one, and another kind of two-period portfolio efficiency estimation model was built. The numerical analyses show that this proposed model is effective.     

The Social Welfare Housing Project and Its Effects in China

It was planned to build 36 million units of social welfare housing during the twelve-five2011-2015 in China.This paper introduces the estimation of owner occupied dwelling sector and develops a dynamic computable general equilibrium model for China’s real estate and macro-economy,to simulate the policy effects.The simulation results show that this policy can meet the increased requirements of housing demand due to fast urbanization and improvement of living conditions,therefore it will effectively cool down the price boom of housing market.Meanwhile,although the investment on social welfare housing will reduce the investment on other sectors,it will still stimulate GDP growth.     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

OPTIMAL STARTING PRICE FOR EBAY-LIKE ONLINE AUCTIONS

Reserve price auctions are one of hot research issues in traditional auction theory. Here we study the starting price in an online auction, the counterpart of the public reserve price in a traditional auction. By considering three features of eBay-like online auctions： stochastic entry of bidders （subject to Poisson process）, insertion fee proportional to the starting price, and time discount, we have analyzed the properties of extremum points of the starting price for maximizing the seller＇s expected revenue, and found that, under certain conditions, the optimal starting price should be at the lowest Mlowable level, which is contrary to the results from the classic auction theory and finds its optimality in reality. We have also developed a general extended model of multistage auctions and carried out analysis on its properties. At last, some directions for further research are also put forward.     

Equivalent sample theory of networked control systems and its application

The equivalent sample theory and its application in analysis of networked control system （NCS） are presented. After analyzing NCS＇s scheduling in master-slave mode, the characteristics of time delay and sample are summarized. Looking on master station visiting the slave station as a special sample process, the theory of equivalent sample is presented. And based on it, the stability of a kind of NCS is analyzed. The criterion to determine the upper bound of transmission delay is introduced, which guarantees the stability. Finally, an example with simulation shows the availability and usability of this analysis method.     

ON AN SIS EPIDEMIC MODEL WITH STAGE STRUCTURE

A SIS infectious disease model with stage structure consisting of immature and mature stages is proposed using a discrete time delay. The aim of the paper is to investigate under which conditions the disease becomes endemic or not and to find the difference between the model with stage structure and the corresponding model without stage structure. It is shown that either there exists a unique endemic equilibrium point which is globally asymptotically stable or the disease dies out by using an iterative scheme. The effect of the time delay on the populations at equilibria is considered.     

Analyses of location-price game on networks with stochastic customer behavior and its heuristic algorithm

<正> In this paper,a two-stage model is developed to investigate the location strategy andthe commodity pricing strategy for a retail firm that wants to enter a spatial market with multiplecompetitive facilities,where a competitor firm is already operating as a monopoly with several outlets.Expected market shares are calculated based on the stochastic customer behavior on networks.Theauthors provide a sufficient condition for the existence of equilibrium prices in the price game for thefirst time.The existence and uniqueness of the pure strategy Nash equilibrium price with a specifiedutility function are proved in the subgame.A metaheuristic based on tabu search is proposed tosearch the optimal location-price solution of the model.In addition,the authors provide two numericalexamples to illustrate how to obtain the optimal solution and conduct sensitivity analysis.The analysisshows that the best location decision is robust for the follower firm,price game is more intense whenincomes of consumers are lower or there are more substitution products,and neither chain retail gainsfrom the price competition.     

DQN-based decentralized multi-agent JSAP resource allocation for UAV swarm communication

It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system. In order to address this challenge, a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U) link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another. The c...     

Joint DOA and time delay estimation method for space-time coherent distributed signals based on search

Under dense urban fading environment, performance of joint multi-path parameter estimation method based on traditional point signal model degrades seriously. In this paper, a new space and time signal model based on multipath distribution function is given after new space and time manifold is reconstructed. Then joint spacetime signal subspace is obtained by converting acquired channel from time domain to frequency domain. Then space and time spectrum is formulated by the space sub-matrix and time sub-matrix taken out of joint space-time signal subspace, and parameters are estimated by searching the minimum eigenvalues of the space matrix and the time matrix. Lastly, A space and time parameters matching process is performed by using the orthogonal property between joint noise subspace and the space-time manifold. In contrast with tradition MUSIC, the algorithm we present here only need two 1- dimension searching and was not sensitive to different distribution function.     

Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.     

Urban transit assignment model based on augmented network with in-vehicle congestion and transfer congestion

This paper presents an augmented network model to represent urban transit system.Through such network model,the urban transit assignment problem can be easily modeled like a generalized traffic network.Simultaneously,the feasible route in such augmented transit network is then defined in accordance with the passengers’ behaviors.The passengers’ travel costs including walking time,waiting time,in-vehicle time and transfer time are formulated while the congestions at stations and the congestions in transit vehicles are all taken into account.On the base of these,an equilibrium model for urban transit assignment problem is presented and an improved shortest path method based algorithm is also proposed to solve it.Finally,a numerical example is provided to illustrate our approach.     

STUDY ON AN SIS EPIDEMIC MODEL WITH TIME VARIANT DELAY

In this paper, we study an SIS epidemic model with a time variant delay. By means of Liapunov functional, some sufficient conditions of global stability to endemic equilibrium and disease free equilibrium have been obtained. The influence of time delay on the stability of equilibria is displayed.     

Model and Algorithm of Transportation Problem on Network

Transportation problem on network needs to determine the freight quantity and the transportation route between supply point and demand point. Therefore, taken the uncertainty of freight supply and demand into account, a collaborative optimization model is formulated with transportation capacity constraint. In addition, a two-stage genetic algorithm （GA） is put forward. Herein, the first stage of this GA is adopted a priority-based encoding method for determining the supply and demand relationship between different points. Then supply and demand relationship which the supply and the demand are both greater than zero is a minimum cost flow （MCF） problem on network in the second stage. Aim at the purpose to solve MCF problem, a GA is employed. Moreover, this algorithm is suitable for balance and unbalance transportation on directed network or undirected network. At last, the model and algorithm are verified to be efficient by a numerical example.     

Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic Systems over a Lossy Communication Channel

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous pulses.To improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.     

Building a Tax Predictive Model Based on the Cloud Neural Network

Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the ef-     

Analysis of Policy Factors in the Stock Market

One model of stochastic time series - Markov chain was presented in this paper. We study and discuss the application of this model. Some results that the policy factor make to stock price were offered when this model is applied to analyze the index of Shanghai stock market quantitatively.     

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.     

Genetic algorithm for pareto optimum-based route selection

A quality of service (QoS) or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path(MCP) problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.