Stochastic variational inference is an efficient Bayesian inference technology for massive datasets,which approximates posteriors by using noisy gradient estimates.Traditional stochastic variational inference can only...Stochastic variational inference is an efficient Bayesian inference technology for massive datasets,which approximates posteriors by using noisy gradient estimates.Traditional stochastic variational inference can only be performed in a centralized manner,which limits its applications in a wide range of situations where data is possessed by multiple nodes.Therefore,this paper develops a novel trust-region based stochastic variational inference algorithm for a general class of conjugate-exponential models over distributed and asynchronous networks,where the global parameters are diffused over the network by using the Metropolis rule and the local parameters are updated by using the trust-region method.Besides,a simple rule is introduced to balance the transmission frequencies between neighboring nodes such that the proposed distributed algorithm can be performed in an asynchronous manner.The utility of the proposed algorithm is tested by fitting the Bernoulli model and the Gaussian model to different datasets on a synthetic network,and experimental results demonstrate its effectiveness and advantages over existing works.展开更多
To enhance the fidelity and accuracy of the simulation of communication networks,hardware-in-the-loop（HITL） simulation was employed.HITL simulation methods was classified into three categories,of which the merits an...To enhance the fidelity and accuracy of the simulation of communication networks,hardware-in-the-loop（HITL） simulation was employed.HITL simulation methods was classified into three categories,of which the merits and shortages were compared.Combing system-in-the-loop（SITL） simulation principle with high level architecture（HLA）,an HITL simulation model of asynchronous transfer mode（ATM） network was constructed.The throughput and end-to-end delay of all-digital simulation and HITL simulation was analyzed,which showed that HITL simulation was more reliable and effectively improved the simulation credibility of communication network.Meanwhile,HLA-SITL method was fast and easy to achieve and low-cost during design lifecycle.Thus,it was a feasible way to research and analyze the large-scale network.展开更多
Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them co...Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.展开更多
Recent efforts to add new services to the wide-band code division multiple accesses （WCDMA） system have increased interest in network processor （NP）-based routers that are easy to extend and evolve. In this paper,...Recent efforts to add new services to the wide-band code division multiple accesses （WCDMA） system have increased interest in network processor （NP）-based routers that are easy to extend and evolve. In this paper, an application of NPs in routing engine module （REM） of radio network controller （RNC） in WCDMA system is proposed. The measuring results show that NPs have good performance and efficiency in routing traffic of the communication network and the simulation verifies the fast forwarding function of NPs.展开更多
基金the National Natural Science Foundation of China under Grant Nos.61922076,61873252,61725304,and 61973324in part by Guangdong Basic and Applied Basic Research Foundation under Grant No.2021B1515020094in part by the Guangdong Provincial Key Laboratory of Computational Science under Grant No.2020B1212060032。
文摘Stochastic variational inference is an efficient Bayesian inference technology for massive datasets,which approximates posteriors by using noisy gradient estimates.Traditional stochastic variational inference can only be performed in a centralized manner,which limits its applications in a wide range of situations where data is possessed by multiple nodes.Therefore,this paper develops a novel trust-region based stochastic variational inference algorithm for a general class of conjugate-exponential models over distributed and asynchronous networks,where the global parameters are diffused over the network by using the Metropolis rule and the local parameters are updated by using the trust-region method.Besides,a simple rule is introduced to balance the transmission frequencies between neighboring nodes such that the proposed distributed algorithm can be performed in an asynchronous manner.The utility of the proposed algorithm is tested by fitting the Bernoulli model and the Gaussian model to different datasets on a synthetic network,and experimental results demonstrate its effectiveness and advantages over existing works.
基金Supported by the National Natural Science Foundation of China (61101129)Specialized Research Fund for the Doctoral Program of Higher Education(20091101110019)
文摘To enhance the fidelity and accuracy of the simulation of communication networks,hardware-in-the-loop（HITL） simulation was employed.HITL simulation methods was classified into three categories,of which the merits and shortages were compared.Combing system-in-the-loop（SITL） simulation principle with high level architecture（HLA）,an HITL simulation model of asynchronous transfer mode（ATM） network was constructed.The throughput and end-to-end delay of all-digital simulation and HITL simulation was analyzed,which showed that HITL simulation was more reliable and effectively improved the simulation credibility of communication network.Meanwhile,HLA-SITL method was fast and easy to achieve and low-cost during design lifecycle.Thus,it was a feasible way to research and analyze the large-scale network.
基金supported in part by the Program of the National Natural Science Foundation of China under Grant No.61673399in part by the Program of National Natural Science Foundation of Hunan Province under Grant No.2017JJ2329in part by Fundamental Research Funds for Central Universities of Central South University under Grant No.2018zzts550。
文摘Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.
文摘Recent efforts to add new services to the wide-band code division multiple accesses （WCDMA） system have increased interest in network processor （NP）-based routers that are easy to extend and evolve. In this paper, an application of NPs in routing engine module （REM） of radio network controller （RNC） in WCDMA system is proposed. The measuring results show that NPs have good performance and efficiency in routing traffic of the communication network and the simulation verifies the fast forwarding function of NPs.
