A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Consideri...A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Considering the different types of service requirements,the MDP model and its reward function are constructed based on the quality of service(QoS)attribute parameters of the mobile users,and the network attribute weights are calculated by using the analytic hierarchy process(AHP).The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network,and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA),thus,users can seamlessly switch to the network with the best long-term expected reward value.Simulation results show that the proposed algorithm has good convergence performance,and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.展开更多
In this article, we investigate Programming Evaluation and Review Technique networks with independently and generally distributed activity durations. For any path in this network, we select all the activities related ...In this article, we investigate Programming Evaluation and Review Technique networks with independently and generally distributed activity durations. For any path in this network, we select all the activities related to this path such that the completion time of the sub-network （only consisting of all the related activities） is equal to the completion time of this path. We use the elapsed time as the supplementary variables and model this sub-network as a Markov skeleton process, the state space is related to the subnetwork structure. Then use the backward equation to compute the distribution of the sub-network＇s completion time, which is an important rule in project management and scheduling.展开更多
We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-...We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.展开更多
Mobile ad hoc networks （MANETs） have become a hot issue in the area of wireless networks for their non-infrastructure and mobile features. In this paper, a MANET is modeled so that the length of each link in the net...Mobile ad hoc networks （MANETs） have become a hot issue in the area of wireless networks for their non-infrastructure and mobile features. In this paper, a MANET is modeled so that the length of each link in the network is considered as a birthdeath process and the space is reused for n times in the flooding process, which is named as an n-spatiai reuse birth-death model （n-SRBDM）. We analyze the performance of the network under the dynamic source routing protocol （DSR） which is a famous reactive routing protocol. Some performance parameters of the route discovery are studied such as the probability distribution and the expectation of the flooding distance, the probability that a route is discovered by a query packet with a hop limit, the probability that a request packet finds a τ-time-valid route or a symmetric-valid route, and the average time needed to discover a valid route. For the route maintenance, some parameters are introduced and studied such as the average frequency of route recovery and the average time of a route to be valid. We compare the two models with spatial reuse and without spatial reuse by evaluating these parameters. It is shown that the spatial reuse model is much more effective in routing.展开更多
A two-stage directed Semi-Markov repairable network system is presented in this paper to model the performance of many transmission systems, such as power or oil transmission network, water or gas supply network, etc....A two-stage directed Semi-Markov repairable network system is presented in this paper to model the performance of many transmission systems, such as power or oil transmission network, water or gas supply network, etc. The availability of the system is discussed by using Markov renewal theory, Laplace transform and probability analysis methods. A numerical example is given to illustrate the results obtained in the paper.展开更多
The smart distribution system is the critical part of the smart grid, which also plays an important role in the safe and reliable operation of the power grid. The self-healing function of smart distribution network wi...The smart distribution system is the critical part of the smart grid, which also plays an important role in the safe and reliable operation of the power grid. The self-healing function of smart distribution network will effectively improve the security, reliability and efficiency, reduce the system losses, and promote the development of sustainable energy of the power grid. The risk identification process is the most fundamental and crucial part of risk analysis in the smart distribution network. The risk control strategies will carry out on fully recognizing and understanding of the risk events and the causes. On condition that the risk incidents and their reason are identified, the corresponding qualitative / quantitative risk assessment will be performed based on the influences and ultimately to develop effective control measures. This paper presents the concept and methodology on the risk identification by means of Hidden Semi-Markov Model (HSMM) based on the research of the relationship between the operating characteristics/indexes and the risk state, which provides the theoretical and practical support for the risk assessment and risk control technology.展开更多
Agents interactions in a social network are dynamic and stochastic. We model the dynamic interactions using the hidden Markov model, a probability model which has a wide array of applications. The transition matrix wi...Agents interactions in a social network are dynamic and stochastic. We model the dynamic interactions using the hidden Markov model, a probability model which has a wide array of applications. The transition matrix with three states, forgetting, reinforcement and exploration is estimated using simulation. Singular value decomposition estimates the observation matrix for emission of low, medium and high interaction rates. This is achieved when the rank approximation is applied to the transition matrix. The initial state probabilities are then estimated with rank approximation of the observation matrix. The transition and the observation matrices estimate the state and observed symbols in the model. Agents interactions in a social network account for between 20% and 50% of all the activities in the network. Noise contributes to the other portion due to interaction dynamics and rapid changes observable from the agents transitions in the network. In the model, the interaction proportions are low with 11%, medium with 56% and high with 33%. Hidden Markov model has a strong statistical and mathematical structure to model interactions in a social network.展开更多
The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilist...The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.展开更多
Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network applic...Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network application, and in many scenarios it is power and bandwidth limited application. The proposed scenario in this paper applies the advantages of NC over WSN to obtain such power and bandwidth efficient WSN. To take the advantages of NC over the one of the most needed applications i.e., WSN, we come up to what this paper is discussing. We consider a WSN (or its cluster) that consists of M nodes that transmit equal-length information packets to a common destination node D over wireless Rayleigh block-fading channel where the instantaneous SNR is assumed to be constant over a single packet transmission period. Finite-State packet level Markov chain (FSMC) model is applied to give the channel more practical aspect. The simulation results showed that applying NC over the WSN cluster improved the channel bandwidth significantly by decreasing the number of the Automatic Repeat Request (ARQ), resulting in improving the power consumption significantly. The results are collected for different transmission distances to evaluate the behavior to the proposed scenario with regard to the bath losses effect.展开更多
The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced ...The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality （LMI） approach. A simulation example illustrates the effectiveness of the proposed method.展开更多
In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) h...In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN The new model can preserve the strength of PBNs, the ability to capture the inter-dependence of the genes in the network, qnd at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are given to demonstrate the effectiveness of our proposed model and control policy.展开更多
基金partially supported by Nation Science Foundation of China (61661025, 61661026)Foundation of A hundred Youth Talents Training Program of Lanzhou Jiaotong University (152022)
文摘A network selection optimization algorithm based on the Markov decision process(MDP)is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment.Considering the different types of service requirements,the MDP model and its reward function are constructed based on the quality of service(QoS)attribute parameters of the mobile users,and the network attribute weights are calculated by using the analytic hierarchy process(AHP).The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network,and the MDP model is solved by using the genetic algorithm and simulated annealing(GA-SA),thus,users can seamlessly switch to the network with the best long-term expected reward value.Simulation results show that the proposed algorithm has good convergence performance,and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.
基金supported by the National Natural Science Foundation of China(10671212,10901164,90820302)the Graduate Research Innovation Projects in Hunan Province(CX2009B020)the Graduate Degree Thesis Innovation Foundation of Central Sourth University(2009ybfz11)
文摘In this article, we investigate Programming Evaluation and Review Technique networks with independently and generally distributed activity durations. For any path in this network, we select all the activities related to this path such that the completion time of the sub-network （only consisting of all the related activities） is equal to the completion time of this path. We use the elapsed time as the supplementary variables and model this sub-network as a Markov skeleton process, the state space is related to the subnetwork structure. Then use the backward equation to compute the distribution of the sub-network＇s completion time, which is an important rule in project management and scheduling.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61873002, 61703004, 61973199, 61573008, and 61973200)。
文摘We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.
基金Project supported by the National Natural Science Foundation of China (Nos.10471088 and 60572126)the Science Foundation of Shanghai Municipal Commission of Education (No.06ZZ84)
文摘Mobile ad hoc networks （MANETs） have become a hot issue in the area of wireless networks for their non-infrastructure and mobile features. In this paper, a MANET is modeled so that the length of each link in the network is considered as a birthdeath process and the space is reused for n times in the flooding process, which is named as an n-spatiai reuse birth-death model （n-SRBDM）. We analyze the performance of the network under the dynamic source routing protocol （DSR） which is a famous reactive routing protocol. Some performance parameters of the route discovery are studied such as the probability distribution and the expectation of the flooding distance, the probability that a route is discovered by a query packet with a hop limit, the probability that a request packet finds a τ-time-valid route or a symmetric-valid route, and the average time needed to discover a valid route. For the route maintenance, some parameters are introduced and studied such as the average frequency of route recovery and the average time of a route to be valid. We compare the two models with spatial reuse and without spatial reuse by evaluating these parameters. It is shown that the spatial reuse model is much more effective in routing.
文摘A two-stage directed Semi-Markov repairable network system is presented in this paper to model the performance of many transmission systems, such as power or oil transmission network, water or gas supply network, etc. The availability of the system is discussed by using Markov renewal theory, Laplace transform and probability analysis methods. A numerical example is given to illustrate the results obtained in the paper.
文摘The smart distribution system is the critical part of the smart grid, which also plays an important role in the safe and reliable operation of the power grid. The self-healing function of smart distribution network will effectively improve the security, reliability and efficiency, reduce the system losses, and promote the development of sustainable energy of the power grid. The risk identification process is the most fundamental and crucial part of risk analysis in the smart distribution network. The risk control strategies will carry out on fully recognizing and understanding of the risk events and the causes. On condition that the risk incidents and their reason are identified, the corresponding qualitative / quantitative risk assessment will be performed based on the influences and ultimately to develop effective control measures. This paper presents the concept and methodology on the risk identification by means of Hidden Semi-Markov Model (HSMM) based on the research of the relationship between the operating characteristics/indexes and the risk state, which provides the theoretical and practical support for the risk assessment and risk control technology.
文摘Agents interactions in a social network are dynamic and stochastic. We model the dynamic interactions using the hidden Markov model, a probability model which has a wide array of applications. The transition matrix with three states, forgetting, reinforcement and exploration is estimated using simulation. Singular value decomposition estimates the observation matrix for emission of low, medium and high interaction rates. This is achieved when the rank approximation is applied to the transition matrix. The initial state probabilities are then estimated with rank approximation of the observation matrix. The transition and the observation matrices estimate the state and observed symbols in the model. Agents interactions in a social network account for between 20% and 50% of all the activities in the network. Noise contributes to the other portion due to interaction dynamics and rapid changes observable from the agents transitions in the network. In the model, the interaction proportions are low with 11%, medium with 56% and high with 33%. Hidden Markov model has a strong statistical and mathematical structure to model interactions in a social network.
文摘The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.
文摘Network Coding (NC) is confirmed to be power and bandwidth efficient technique, because of the less number of transmitted packets over the network. Wireless Sensor Network (WSN) is usually power limited network application, and in many scenarios it is power and bandwidth limited application. The proposed scenario in this paper applies the advantages of NC over WSN to obtain such power and bandwidth efficient WSN. To take the advantages of NC over the one of the most needed applications i.e., WSN, we come up to what this paper is discussing. We consider a WSN (or its cluster) that consists of M nodes that transmit equal-length information packets to a common destination node D over wireless Rayleigh block-fading channel where the instantaneous SNR is assumed to be constant over a single packet transmission period. Finite-State packet level Markov chain (FSMC) model is applied to give the channel more practical aspect. The simulation results showed that applying NC over the WSN cluster improved the channel bandwidth significantly by decreasing the number of the Automatic Repeat Request (ARQ), resulting in improving the power consumption significantly. The results are collected for different transmission distances to evaluate the behavior to the proposed scenario with regard to the bath losses effect.
基金supported by the NSFC-Guangdong Joint Foundation Key Project(U0735003)the Overseas Cooperation Foundation(60828006)+1 种基金the Scientific Research Foundation for Returned Overseas Chinese Scholars,State Education Ministry,the Fundamental Research Funds for the Central Universities(2009ZM0076)the Natural Science Foundation of Guangdong Province(06105413)
文摘The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality （LMI） approach. A simulation example illustrates the effectiveness of the proposed method.
文摘In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN The new model can preserve the strength of PBNs, the ability to capture the inter-dependence of the genes in the network, qnd at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are given to demonstrate the effectiveness of our proposed model and control policy.
