We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algori...We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.展开更多
The Chinese Government and citizens face enormous challenges of disaster management as widespread devastation,economic damages,and loss of human lives caused by increasing natural disasters.Disaster management require...The Chinese Government and citizens face enormous challenges of disaster management as widespread devastation,economic damages,and loss of human lives caused by increasing natural disasters.Disaster management requires a complicated iterative process that includes disaster monitoring,early detection,forecasting,loss assessment,and efficient analysis of disaster reduction.Each task typically involves the use of technologists and multiple geospatial information resources,including sensors,data sources,models,geo-tools,software packages,and computing resources.However,most existing disaster management systems operate in a typical passive data-centric mode,where resources cannot be fully utilized.This impediment is partially being addressed by the increasingly complex application requirements and the growing availability of diverse resources.In this paper,we summarize and analyze the practical problems experienced by the National Disaster Reduction Application System of China.To address the issues of data-centric,centralized,isolated solutions,we propose a novel Focusing Service Mechanism,which is capable of scheduling and allocating for optimum utilization of multiple resources,to dynamically generate collaborative and on-demand disaster information services.We also demonstrate the design and implementation of the Integrated Disaster Information Service System(IDISS).Through the service strategies of Virtualizing,Wrapping,and Integrating,disasterrelated resources are constructed into services in the IDISS.These services are dynamically aggregated into focusing service chains,for diverse disaster management tasks.Actual applications illustrate that the proposed service system can significantly improve the capability of disaster management in China.展开更多
The load demand and distributed generation(DG)integration capacity in distribution networks(DNs)increase constantly,and it means that the violation of security constraints may occur in the future.This can be further w...The load demand and distributed generation(DG)integration capacity in distribution networks(DNs)increase constantly,and it means that the violation of security constraints may occur in the future.This can be further worsened by short-term power fluctuations.In this paper,a scheduling method based on a multi-objective chance-constrained information-gap decision(IGD)model is proposed to obtain the active management schemes for distribution system operators(DSOs)to address these problems.The maximum robust adaptability of multiple uncertainties,including the deviations of growth prediction and their relevant power fluctuations,can be obtained based on the limited budget of active management.The systematic solution of the proposed model is developed.The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term.Considering the stochastic characteristics and correlations of power fluctuations,the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution.The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network.The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties,which corresponds to an optional active management strategy set for future selection.展开更多
公立医院高质量发展,疾病诊断相关分组(diagnosis related groups,DRG)支付背景下,对成本绩效规范化管理提出了更高要求。绩效管理是医院发展的重要抓手,成本管控是提升运营效率的关键;成功的成本绩效管理方案,有助于促进公立医院持续...公立医院高质量发展,疾病诊断相关分组(diagnosis related groups,DRG)支付背景下,对成本绩效规范化管理提出了更高要求。绩效管理是医院发展的重要抓手,成本管控是提升运营效率的关键;成功的成本绩效管理方案,有助于促进公立医院持续健康运营,提高医务人员积极性。通过分析公立医院成本绩效管理现状,建立成本绩效管理体系、信息集成串联系统,对成本核算对象进行细分和下沉,运用作业成本法实现间接成本的分摊归集等方法,设计和探索成本绩效管理模式,旨在为新的绩效管理体系与实施策略提供思路和借鉴,实现医院增效、患者降费的共赢局面。展开更多
通过分析用户执行多任务的交互场景,基于活动理论,将任务、任务相关的信息以及任务问的关系统一纳入活动研究的框架,提出了以活动为中心的个人信息管理方式;从活动的静态结构、动态演变过程以及活动间的关系3个方面对活动进行建模:针对...通过分析用户执行多任务的交互场景,基于活动理论,将任务、任务相关的信息以及任务问的关系统一纳入活动研究的框架,提出了以活动为中心的个人信息管理方式;从活动的静态结构、动态演变过程以及活动间的关系3个方面对活动进行建模:针对多活动场景中的用户交互和活动对象的内容提出了计算活动相关性的方法;并在此基础上,实现了以活动为中心的个人信息管理工具——ACPIM(activity-centered personal information management),评估结果显示:以活动为中心的个人信息管理有助于用户减轻认知和记忆负担,降低交互努力,从而提高工作效率.展开更多
文摘We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.
基金This study was supported by the National Science and Technology Support Program of China[863 Program,grant number 03-Y30B06-9001-13/15,grant number 2012AA121305]the National Natural Science Foundation[grant number 41171311,grant number 41101354,grant number 41201440].
文摘The Chinese Government and citizens face enormous challenges of disaster management as widespread devastation,economic damages,and loss of human lives caused by increasing natural disasters.Disaster management requires a complicated iterative process that includes disaster monitoring,early detection,forecasting,loss assessment,and efficient analysis of disaster reduction.Each task typically involves the use of technologists and multiple geospatial information resources,including sensors,data sources,models,geo-tools,software packages,and computing resources.However,most existing disaster management systems operate in a typical passive data-centric mode,where resources cannot be fully utilized.This impediment is partially being addressed by the increasingly complex application requirements and the growing availability of diverse resources.In this paper,we summarize and analyze the practical problems experienced by the National Disaster Reduction Application System of China.To address the issues of data-centric,centralized,isolated solutions,we propose a novel Focusing Service Mechanism,which is capable of scheduling and allocating for optimum utilization of multiple resources,to dynamically generate collaborative and on-demand disaster information services.We also demonstrate the design and implementation of the Integrated Disaster Information Service System(IDISS).Through the service strategies of Virtualizing,Wrapping,and Integrating,disasterrelated resources are constructed into services in the IDISS.These services are dynamically aggregated into focusing service chains,for diverse disaster management tasks.Actual applications illustrate that the proposed service system can significantly improve the capability of disaster management in China.
基金supported by the National Natural Science Foundation of China(No.U1866207)。
文摘The load demand and distributed generation(DG)integration capacity in distribution networks(DNs)increase constantly,and it means that the violation of security constraints may occur in the future.This can be further worsened by short-term power fluctuations.In this paper,a scheduling method based on a multi-objective chance-constrained information-gap decision(IGD)model is proposed to obtain the active management schemes for distribution system operators(DSOs)to address these problems.The maximum robust adaptability of multiple uncertainties,including the deviations of growth prediction and their relevant power fluctuations,can be obtained based on the limited budget of active management.The systematic solution of the proposed model is developed.The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term.Considering the stochastic characteristics and correlations of power fluctuations,the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution.The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network.The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties,which corresponds to an optional active management strategy set for future selection.
文摘公立医院高质量发展,疾病诊断相关分组(diagnosis related groups,DRG)支付背景下,对成本绩效规范化管理提出了更高要求。绩效管理是医院发展的重要抓手,成本管控是提升运营效率的关键;成功的成本绩效管理方案,有助于促进公立医院持续健康运营,提高医务人员积极性。通过分析公立医院成本绩效管理现状,建立成本绩效管理体系、信息集成串联系统,对成本核算对象进行细分和下沉,运用作业成本法实现间接成本的分摊归集等方法,设计和探索成本绩效管理模式,旨在为新的绩效管理体系与实施策略提供思路和借鉴,实现医院增效、患者降费的共赢局面。
基金Supported by the National Basic Research Program of China under Grant No.2002CB312103(国家重点基础研究发展计划(973))the National Natural Science Foundation of China under Grant No+2 种基金60503054(国家自然科学基金)the Key Innovation Project from the Institute of Softwarethe Chinese Academy of Sciences(中国科学院软件研究所创新基金重大项目)
文摘通过分析用户执行多任务的交互场景,基于活动理论,将任务、任务相关的信息以及任务问的关系统一纳入活动研究的框架,提出了以活动为中心的个人信息管理方式;从活动的静态结构、动态演变过程以及活动间的关系3个方面对活动进行建模:针对多活动场景中的用户交互和活动对象的内容提出了计算活动相关性的方法;并在此基础上,实现了以活动为中心的个人信息管理工具——ACPIM(activity-centered personal information management),评估结果显示:以活动为中心的个人信息管理有助于用户减轻认知和记忆负担,降低交互努力,从而提高工作效率.
