Various flow phenomena of black particles （b-particles） and grey particles （g-particles） produced in magnesium-emulsion （Mg-Em） collisions at 4.5 A GeV/c are reported. These flows are directed and elliptic trans...Various flow phenomena of black particles （b-particles） and grey particles （g-particles） produced in magnesium-emulsion （Mg-Em） collisions at 4.5 A GeV/c are reported. These flows are directed and elliptic transverse flows （v1 and v2） related by the azimuthal angle （φ）, directed and elliptic reaction plane flows （vR1 and vR2） related by the projected angle （φ） on the reaction plane, and directed and elliptic polar direction flows （vp1 and vp2） related by the polar angle （ ）. We extract absolute flows as the direct experimental values minus the isotropic theoretical values. The dependence of the various flows on the target particle multiplicity and on the angles （ ,φ,φ） is investigated. Our results show that the dependence of b-particle flows on the target size is obvious and for heavy targets the dependence on target particle multiplicity is slight. Compared with b-particles, g-particles have a slight dependence on the target size and target particle multiplicity.展开更多
软件测试中收集的累积出错数据,往往是不完全的,使用它们对软件的可靠性进行分析,将影响到分析结果的精度.解决这一问题的途径有很多,本文试图应用 EM 算法于 NHPP(非齐次泊松过程)类模型的参数估计,以提高估计精度,从而提高软件可靠性...软件测试中收集的累积出错数据,往往是不完全的,使用它们对软件的可靠性进行分析,将影响到分析结果的精度.解决这一问题的途径有很多,本文试图应用 EM 算法于 NHPP(非齐次泊松过程)类模型的参数估计,以提高估计精度,从而提高软件可靠性分析的精确程度.展开更多
特征提取和健康状态的辨识是复杂系统健康状态评估中的关键问题。提出一种新的健康状态评估方法,该方法分为3个步骤:首先,采用经验模态分解(empirical model decomposition,EMD)和奇异值分解(singular value decomposition,SVD)来提取...特征提取和健康状态的辨识是复杂系统健康状态评估中的关键问题。提出一种新的健康状态评估方法,该方法分为3个步骤:首先,采用经验模态分解(empirical model decomposition,EMD)和奇异值分解(singular value decomposition,SVD)来提取振动信号的特征变量。然后,运用马田系统(Mahalanobis-Taguchi system,MTS)构造马氏空间,并对其进行优化,从而降低特征变量的维度。最后,提出了一种健康度(health index,HI)的概念,并且用来对复杂系统健康问题进行评估。该方法成功地应用在轴承的健康状态评估中。展开更多
基金Supported by National Natural Science Foundation of China (10975095,10675077)Natural Science Foundation of Shanxi Province (2007011005)
文摘Various flow phenomena of black particles （b-particles） and grey particles （g-particles） produced in magnesium-emulsion （Mg-Em） collisions at 4.5 A GeV/c are reported. These flows are directed and elliptic transverse flows （v1 and v2） related by the azimuthal angle （φ）, directed and elliptic reaction plane flows （vR1 and vR2） related by the projected angle （φ） on the reaction plane, and directed and elliptic polar direction flows （vp1 and vp2） related by the polar angle （ ）. We extract absolute flows as the direct experimental values minus the isotropic theoretical values. The dependence of the various flows on the target particle multiplicity and on the angles （ ,φ,φ） is investigated. Our results show that the dependence of b-particle flows on the target size is obvious and for heavy targets the dependence on target particle multiplicity is slight. Compared with b-particles, g-particles have a slight dependence on the target size and target particle multiplicity.
文摘在基于模型重用的敏捷虚拟企业(Agile Virtual Enterprise,AVE)建模过程研究中,提出基于模型重用的AVE建模过程框架,并在该框架下对两个特殊的AVE建模过程实例即G-EM和V-EM进行研究。同时,为了支持AVE建模过程中的模型重用,提出面向AVE建模的基于实例推理(Case Based Reasoning,CBR)的模型重用方法,使AVE建模过程的效率更高,AVE模型的语义一致性更强。
文摘特征提取和健康状态的辨识是复杂系统健康状态评估中的关键问题。提出一种新的健康状态评估方法,该方法分为3个步骤:首先,采用经验模态分解(empirical model decomposition,EMD)和奇异值分解(singular value decomposition,SVD)来提取振动信号的特征变量。然后,运用马田系统(Mahalanobis-Taguchi system,MTS)构造马氏空间,并对其进行优化,从而降低特征变量的维度。最后,提出了一种健康度(health index,HI)的概念,并且用来对复杂系统健康问题进行评估。该方法成功地应用在轴承的健康状态评估中。
