随着电力系统数据采集手段的不断完善,基于数据的分析方法在电力系统运行分析中扮演着日益重要的角色。现有的数据分析方法主要分析数据之间的相关关系。事实上,两个强相关变量间通常呈现出不对称的因果关系。若能揭示电力系统运行变量...随着电力系统数据采集手段的不断完善,基于数据的分析方法在电力系统运行分析中扮演着日益重要的角色。现有的数据分析方法主要分析数据之间的相关关系。事实上,两个强相关变量间通常呈现出不对称的因果关系。若能揭示电力系统运行变量间的因果关系,必将有助于深刻地洞察电力系统运行的内在规律性。近年来,因果推断的研究取得很大进展,使得基于数据的因果分析成为可能。该文从物理机制上揭示电力系统中强相关变量之间因果关系的不对称属性;提出一种逆信息熵因果推理(reciprocal information entropy causal inference,RIECI)方法,所构建的指标不仅可以有效判别相关变量间的因果方向,还能正确反映因果强度。在电力系统算例中的验证表明,RIECI方法能有效揭示电力系统运行数据中的因果关系。对电力系统运行数据中因果关系的分析对于认知电力系统运行机理和正确调控电力系统运行状态有重要意义。展开更多
免疫接种不良事件(Adverse Events Following Immunization AEFI)因果评价方法是疫苗不良反应监测工作的重要内容。目前国际没有一种通用的关于疫苗与不良反应因果关系的评价方法。本文对世界卫生组织(WHO)安全顾问委员会、加拿大临床...免疫接种不良事件(Adverse Events Following Immunization AEFI)因果评价方法是疫苗不良反应监测工作的重要内容。目前国际没有一种通用的关于疫苗与不良反应因果关系的评价方法。本文对世界卫生组织(WHO)安全顾问委员会、加拿大临床流行病学研究中心以及美国临床免疫评估协作网3个组织的免疫接种不良事件因果关系评价方法进行了介绍与分析,并对我国免疫接种后不良反应因果关系评估提出相关建议。展开更多
For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used ...For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.展开更多
文摘随着电力系统数据采集手段的不断完善,基于数据的分析方法在电力系统运行分析中扮演着日益重要的角色。现有的数据分析方法主要分析数据之间的相关关系。事实上,两个强相关变量间通常呈现出不对称的因果关系。若能揭示电力系统运行变量间的因果关系,必将有助于深刻地洞察电力系统运行的内在规律性。近年来,因果推断的研究取得很大进展,使得基于数据的因果分析成为可能。该文从物理机制上揭示电力系统中强相关变量之间因果关系的不对称属性;提出一种逆信息熵因果推理(reciprocal information entropy causal inference,RIECI)方法,所构建的指标不仅可以有效判别相关变量间的因果方向,还能正确反映因果强度。在电力系统算例中的验证表明,RIECI方法能有效揭示电力系统运行数据中的因果关系。对电力系统运行数据中因果关系的分析对于认知电力系统运行机理和正确调控电力系统运行状态有重要意义。
文摘免疫接种不良事件(Adverse Events Following Immunization AEFI)因果评价方法是疫苗不良反应监测工作的重要内容。目前国际没有一种通用的关于疫苗与不良反应因果关系的评价方法。本文对世界卫生组织(WHO)安全顾问委员会、加拿大临床流行病学研究中心以及美国临床免疫评估协作网3个组织的免疫接种不良事件因果关系评价方法进行了介绍与分析,并对我国免疫接种后不良反应因果关系评估提出相关建议。
基金Supported by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)+1 种基金the Natural Science Foundation of Zhejiang Province(LQ15F030006)the Educational Commission Research Program of Zhejiang Province(Y201431412)
文摘For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.
