In this paper, we study the compound binomial model in Markovian environment, which is proposed by Cossette, et al. （2003）. We obtain the recursive formula of the joint distributions of T, X（T - 1） and ｜X（T）｜...In this paper, we study the compound binomial model in Markovian environment, which is proposed by Cossette, et al. （2003）. We obtain the recursive formula of the joint distributions of T, X（T - 1） and ｜X（T）｜（i.e., the time of ruin, the surplus before ruin and the deficit at ruin） by the method of mass function of up-crossing zero points, as given by Liu and Zhao （2007）. By using the same method, the recursive formula of supremum distribution is obtained. An example is included to illustrate the results of the model.展开更多
New models of safety-critical systems are built here. In these systems, when components fail, different defect states have different effects, hence need different ways to measure. In the models, there are two kinds of...New models of safety-critical systems are built here. In these systems, when components fail, different defect states have different effects, hence need different ways to measure. In the models, there are two kinds of failure modes of the components： one could be called failed-safe, and the other may be named failed- dangerous In practice, the so-called failed-dangerous components may lead a system to peril. However, failed-safe components will not. Reliability and safety issues are analyzed using Ion-Channel modeling theory to get count of repairs and time duration before the system becomes dangerous. In the closing section a numerical example is presented to illustrate the results obtained in the paper.展开更多
Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an I...Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an Industry 4.0 network is very complex and challenging.To manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network services.It follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response time.An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time.After implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time model.It should also be noted that the average response time increases when the arrival rate increases.展开更多
We study the non-Markovianity of the single qubit system coupled with an isotropic Lipkin-Meshkov-Glick（LMG）model by an effective method proposed by Breuer et al.（Breuer H P and Piilo J 2009 Europhys. Lett. 85 5004...We study the non-Markovianity of the single qubit system coupled with an isotropic Lipkin-Meshkov-Glick（LMG）model by an effective method proposed by Breuer et al.（Breuer H P and Piilo J 2009 Europhys. Lett. 85 5004）. It is discovered that the non-Markovianity is concerned with the quantum phase transitions（QPTs）. In the open system, we present that the strong coupling inside the bath and the strong interaction between the system and bath can enhance the degree of non-Markovianity. Moreover, the non-Markovianity is stronger and more sensitive for the bath in the symmetric phase than the symmetry broken phase.展开更多
实际工业过程中,量测数据除了在线仪表采集的快速率数据,还有离线化验等慢速率辅助量测数据.为了更好地利用离线化验数据,增加在线估计的精度,针对随机跳变系统,引入迁移学习思想,提出迁移交互多模型估计(Transfer interacting multiple...实际工业过程中,量测数据除了在线仪表采集的快速率数据,还有离线化验等慢速率辅助量测数据.为了更好地利用离线化验数据,增加在线估计的精度,针对随机跳变系统,引入迁移学习思想,提出迁移交互多模型估计(Transfer interacting multiple model state estimator,IMM-TF)新策略.首先,将离线化验数据的边缘分布作为可以迁移的知识,迁移到贝叶斯后验分布,实现辅助量测数据的充分利用.其次,利用KL(Kullback-Leibler)散度度量知识迁移前后任务间的差异性,求解最优的贝叶斯迁移估计器.同时,结合慢速率量测,利用平滑策略获取待迁移的估计值,解决多率量测下的迁移估计难题.然后,利用影响力函数构建辅助量测数据与估计性能之间的解析关系,从而对迁移效果进行定量评价.最后,通过在目标跟踪实例中的应用,表明所提方法的有效性及优越性.展开更多
基金Supported by the National Natural Science Foundation of China (10671176, 10771192, 70871103)
文摘In this paper, we study the compound binomial model in Markovian environment, which is proposed by Cossette, et al. （2003）. We obtain the recursive formula of the joint distributions of T, X（T - 1） and ｜X（T）｜（i.e., the time of ruin, the surplus before ruin and the deficit at ruin） by the method of mass function of up-crossing zero points, as given by Liu and Zhao （2007）. By using the same method, the recursive formula of supremum distribution is obtained. An example is included to illustrate the results of the model.
基金supported by National Natural Science Foundation of China(61403254,61374039,61203143)Shanghai Pujiang Program(13PJ1406300)+2 种基金Natural Science Foundation of Shanghai City(13ZR1428500)Innovation Program of Shanghai Municipal Education Commission(14YZ083)Hujiang Foundation of China(C14002,B1402/D1402)
基金Sponsored by 211 Project of Minzu University of China(021211030312)
文摘New models of safety-critical systems are built here. In these systems, when components fail, different defect states have different effects, hence need different ways to measure. In the models, there are two kinds of failure modes of the components： one could be called failed-safe, and the other may be named failed- dangerous In practice, the so-called failed-dangerous components may lead a system to peril. However, failed-safe components will not. Reliability and safety issues are analyzed using Ion-Channel modeling theory to get count of repairs and time duration before the system becomes dangerous. In the closing section a numerical example is presented to illustrate the results obtained in the paper.
基金This work was supported by the National Research Foundation of Korea under Grant 2019R1A2C1085388.
文摘Industry 4.0 refers to the fourth evolution of technology development,which strives to connect people to various industries in terms of achieving their expected outcomes efficiently.However,resource management in an Industry 4.0 network is very complex and challenging.To manage and provide suitable resources to each service,we propose a FogQSYM(Fog—Queuing system)model;it is an analytical model for Fog Applications that helps divide the application into several layers,then enables the sharing of the resources in an effective way according to the availability of memory,bandwidth,and network services.It follows theMarkovian queuing model that helps identify the service rates of the devices,the availability of the system,and the number of jobs in the Industry 4.0 systems,which helps applications process data with a reasonable response time.An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application,which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time.After implementing the proposed model with different sizes of fog services in Industry 4.0 applications,FogQSYM provides a lower response time than the existing optimized response time model.It should also be noted that the average response time increases when the arrival rate increases.
基金Project supported by the National Natural Science Foundation of China(Grant No.11075101)
文摘We study the non-Markovianity of the single qubit system coupled with an isotropic Lipkin-Meshkov-Glick（LMG）model by an effective method proposed by Breuer et al.（Breuer H P and Piilo J 2009 Europhys. Lett. 85 5004）. It is discovered that the non-Markovianity is concerned with the quantum phase transitions（QPTs）. In the open system, we present that the strong coupling inside the bath and the strong interaction between the system and bath can enhance the degree of non-Markovianity. Moreover, the non-Markovianity is stronger and more sensitive for the bath in the symmetric phase than the symmetry broken phase.
基金Supported by the National Natural Science Foundation of China (61911530398)Special Projects of the Central Government Guiding Local Science and Technology Development(2021L3018)the Natural Science Foundation of Fujian Province of China (2021J01621)。
文摘实际工业过程中,量测数据除了在线仪表采集的快速率数据,还有离线化验等慢速率辅助量测数据.为了更好地利用离线化验数据,增加在线估计的精度,针对随机跳变系统,引入迁移学习思想,提出迁移交互多模型估计(Transfer interacting multiple model state estimator,IMM-TF)新策略.首先,将离线化验数据的边缘分布作为可以迁移的知识,迁移到贝叶斯后验分布,实现辅助量测数据的充分利用.其次,利用KL(Kullback-Leibler)散度度量知识迁移前后任务间的差异性,求解最优的贝叶斯迁移估计器.同时,结合慢速率量测,利用平滑策略获取待迁移的估计值,解决多率量测下的迁移估计难题.然后,利用影响力函数构建辅助量测数据与估计性能之间的解析关系,从而对迁移效果进行定量评价.最后,通过在目标跟踪实例中的应用,表明所提方法的有效性及优越性.
