Realms of aesthetic experience of classical Chinese gardens based on semantic analysis of online tourism reviews by Mengyuan Xu, Yuanyu Wang, Haitao Yu

“…A probability transition model of the realms of aesthetic experience was constructed based on a Markov chain and used to mine the transition pattern of the aesthetic realms. …”
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Research on scheduled WLAN MAC protocol with failure retries on RoF-DAS architecture by Zheng GUAN, Lei XIONG, Yao JIA, Min HE, Zhijun YANG

“…Aim to reduce control frames consumption and improve the delay performance,a scheduled gated-service polling access (SGPA) mechanism was proposed,in which the total data packets in the buffer at the polling instant were aggregated into a data set,that was transmitted and acknowledged uniformly.Then,an inner-arrival gated-service polling (IAGP) model was proposed to analyze the quantitative relationship between failure retry mechanism and network access performance of the wireless local area network (WLAN) in RoF-DAS.The failure retry mechanism were modeled as the arrival and service process of inner customers in IAGP.Using the Markov chain and generating function method,the closed-form expressions for the quantitative relationship between delay performance and retrial parameters,such as retry times,and the probability of retransmission,were achieved.The simulation results show that the proposed SGPA can achieve a lower delay than IEEE 802.11,and the theoretical performance evaluation calculated by IAGP are identical with the simulation results,which provides an effective evaluation mechanism for the access performance in RoF-DAS WLAN,with inperfect channel and failure retry mechanism.…”
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Value-difference learning based mMTC devices access algorithm in multi-cell network by Xin LI, Jun SUN

“…In the massive machine type communication scenario of 5G, the access congestion problem of massive machine type communication devices (mMTCD) in multi-cell network is very important.A double deep Q network with value-difference based exploration (VDBE-DDQN) algorithm was proposed.The algorithm focused on the solution that could reduce the collision when a number of mMTCDs accessed to eNB in multi-cell network.The state transition process of the deep reinforcement learning algorithm was modeled as Markov decision process.Furthermore, the algorithm used a double deep Q network to fit the target state-action value function, and it employed an exploration strategy based on value-difference to adapt the change of the environment, which could take advantage of both current conditions and expected future needs.Moreover, each mMTCD updated the probability of exploration according to the difference between the current value function and the next value function estimated by the network, rather than using the same standard to select the best base eNB for the mMTCD.Simulation results show that the proposed algorithm can effectively improve the access success rate of the system.…”
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Environment-aware based access point deployment optimization for cell-free massive MIMO system by Jing JIANG, Yongqiang LIU, Fengyang YAN, Sha TAO, Sutthiphan WORAKRIN

“…Cell-free massive multiple-input multiple-output (MIMO) systems deploy a large number of access point (AP) across the coverage area which can provide uniform high-rate services to users.However, the quality of coverage would be affected by path loss, shadow fading scatters, and environmental occlusions around the randomly placed AP in conventional cell-free massive MIMO systems that do not consider their impact.Considering the impact of actual wireless propagation environments, an AP deployment scheme was proposed to acquire uniform and consistent coverage.Firstly, a hybrid probabilistic path loss model was utilized to characterize various wireless propagation environments.Then, the AP deployment optimization problem was solved with the objective of maximizing the average throughput.Finally, the problem was transformed into a Markov game process and solved by the multi-agent deep deterministic policy gradient (MADDPG) algorithm.The simulation results demonstrate that the proposed scheme can provide more uniform coverage in complex environments and serve users with reliable and consistent service compared to random AP deployment and existing AP deployment methods.…”
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Optimal energy efficiency routing strategy based on community in mobile social network by Ying PENG, Nao WANG, Gao-cai WANG

“…An optimal energy efficiency routing strategy based on community was proposed,which minimized the network energy consumption under the given delay constraint.Firstly the expected energy consumption and delay of message delivery in the connected network were obtained through Markov chain.Then the comprehensive cost function for delivering message from source node to destination node was designed,which was combined with energy consumption and delay.Thus,the optimization function to comprehensive cost of relay node delivering message was obtained,and further the reward function of relay node was gotten.Finally the optimal expected reward of optimal relay node was achieved using the optimal stopping theory,so as to realize the optimal energy efficiency routing strategy.In simulations,the average energy consumption,the average delay and the average delivery ratio of routing optimization strategy were compared with those of other routing strategies in related literatures.The results show that the strategy proposed has smaller average energy consumption,shorter average delay and higher average delivery ratio,gaining better energy consumption optimization effect.…”
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Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment by Renke He, Shuxin Chen, Hao Wu, Zhuowei Liu, Jianhua Chen

“…The quantization method was used to discretize the BOT process and calculate the transition matrix of Markov chain; to achieve quantization in the beginning of each period, CKF was applied to provide the initial state estimate and the corresponding error covariance. …”
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Joint beam hopping and coverage control optimization algorithm for multibeam satellite system by Guoliang XU, Feng TAN, Yongyi RAN, Feng CHEN

“…To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.…”
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Study on energy saving strategy and Nash equilibrium of base station in cognitive radio network by Xiao-tong MA, Shun-fu JIN, Jian-ping LIU, Zhan-qiang HUO

“…Accordingly, a two-dimensional discrete time Markov stochastic model with preemptive priority service and single working vacation was established. …”
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Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System by Yong-Sheng Zhang, En-Jian Yao

“…A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. …”
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Deception defense method against intelligent penetration attack by Jinyin CHEN, Shulong HU, Changyou XING, Guomin ZHANG

“…The intelligent penetration attack based on reinforcement learning aims to model the penetration process as a Markov decision process, and train the attacker to optimize the penetration path in a trial-and-error manner, so as to achieve strong attack performance.In order to prevent intelligent penetration attacks from being maliciously exploited, a deception defense method for intelligent penetration attack based on reinforcement learning was proposed.Firstly, obtaining the necessary information for the attacker to construct the penetration model, which included state, action and reward.Secondly, conducting deception defense against the attacker through inverting the state dimension, disrupting the action generation, and flipping the reward value sign, respectively, which corresponded to the early, middle and final stages of the penetration attack.At last, the three-stage defense comparison experiments were carried out in the same network environment.The results show that the proposed method can effectively reduce the success rate of intelligent penetration attacks based on reinforcement learning.Besides, the deception method that disrupts the action generation of the attacker can reduce the penetration attack success rate to 0 when the interference ratio is 20%.…”
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Delay-Dependent Finite-Time H∞ Filtering for Markovian Jump Systems with Different System Modes by Yong Zeng, Jun Cheng, Shouming Zhong, Xiucheng Dong

“…Particularly, two different Markov processes have been considered for modeling the randomness of system matrix and the state delay. …”
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Métodos para la medición de la movilidad intergeneracional educativa en América Latina y Colombia. Análisis y perspectivas by Martha Yánez-Contreras, Concepción García-Correa

“…Los resultados señalan que no hay un consenso en la medición; sin embargo, los métodos más utilizados son las matrices de transición, modelos Markov e índices sintéticos. La evidencia muestra que la movilidad absoluta ha aumentado en América Latina. …”
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BeiDou satellites cross-regional communication path assignment model and resource management by Sheng Liu, Di Wu, Lanyong Zhang

“…Therefore, in this study, we develop a path assignment model based on the idea of clustering and Markov chain. The optimal path is determined by the objective function based on the maximum transition probability. …”
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Cooperative MAC protocol based on network coding and space-time coding by Qian-bin CHEN, Jian LIU, Yong FENG, Lun TANG

“…Aiming at the problems in wireless ad hoc networks,such as relays’ low efficiency caused by cooperation and difficult to meet the different QoS requirements,a co e MAC protocol was proposed based on the combination of network coding and space-time coding(NSTCMAC).Based on NSTCMAC protocol with the combination of Network coding and space-time coding techniques,a kind of cooperative MAC transmission mechanism that could distinguish the type of DATA packet was designed in order to meet the QoS requirements of different types of transm Further more,cooperative transmission mechanism and its performance were analyzed through Markov chain model.When transmitting the non-real-time(NR) packets,network coding was adopted to improve the efficiency of relay nodes; when transmitting the real-time(R) packets,randomized distributed space-time coding(R-DSTC)was used to enhance the reliability of transmission.Simulation results show that NSTCMAC protocol can meet different QoS requirements better,and solve the problem of relays’ low efficiency more effectively,when compared with legacy DCF、COOPMAC and CD-MAC protocol.…”
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Computational Procedures for a Class of GI/D/k Systems in Discrete Time by Md. Mostafizur Rahman, Attahiru Sule Alfa

“…Then the queue length is set up as a quasi-birth-death (QBD) type Markov chain. It is shown that this transformed GI/D/1 system has special structures which make the computation of the matrix R simple and efficient, thereby reducing the number of multiplications in each iteration significantly. …”
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Subtractive pixel adjacency matrix based features for steganalysis of spatial color images by Tao HAN, Xi CHEN, Yue-fei ZHU

“…Through analyzing the potential security problems of directly extending steganography based on the grayscale spatial image to the color spatial image, novel steganalysis features for detecting the steganographic methods with color spatial images as cover images were proposed. First of all, the Markov transition probability matr of the subtractive pixel adjacency matrices of three color channels were computed, and then the intra-color-channel features were extracted and merged. …”
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Spectral Expansion Method for Cloud Reliability Analysis by K. Kotteswari, A. Bharathi

“…This approach solves the Markov model of cloud service providers (CSP) to predict the reliability. …”
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Prophet: A Context-Aware Location Privacy-Preserving Scheme in Location Sharing Service by Jiaxing Qu, Guoyin Zhang, Zhou Fang

“…First, we define fingerprint identification based on Markov chain and state classification to describe the users’ behavior patterns. …”
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A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk by Lewei Duan, Duncan C. Thomas

“…The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. …”
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Bayesian Estimation and Prediction for Flexible Weibull Model under Type-II Censoring Scheme by Sanjay Kumar Singh, Umesh Singh, Vikas Kumar Sharma

“…Since the predictive posteriors are not in the closed form, we proposed to use the Monte Carlo Markov chain (MCMC) methods to approximate the posteriors of interest. …”
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