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
In net-based collaborative design environment, design resources become more and more varied and complex. Besides common information management systems, design resources can be organized in connection with design act...In net-based collaborative design environment, design resources become more and more varied and complex. Besides common information management systems, design resources can be organized in connection with design activities. A set of activities and resources linked by logic relations can form a task. A task has at least one objective and can be broken down into smaller ones. So a design project can be separated into many subtasks forming a hierarchical structure. Task Management System (TMS) is designed to break down these tasks and assign certain resources to its related task nodes. As a result of decomposition, all design resources and activities could be managed via this system. Based on this idea, we realized a TMS which manages collaborative resources in web environment.展开更多
近年来,基于电网信息模型(grid information model,GIM)的信息管理研究增多,研究人员需要在地区或场地层面有效地可视化GIM模型对象,并对模型数据进行应用和管理,但GIM模型具有跨度大、精细度高的特点,计算机在可视化和应用GIM模型时容...近年来,基于电网信息模型(grid information model,GIM)的信息管理研究增多,研究人员需要在地区或场地层面有效地可视化GIM模型对象,并对模型数据进行应用和管理,但GIM模型具有跨度大、精细度高的特点,计算机在可视化和应用GIM模型时容易遇到性能瓶颈。为此,文章基于BIMBase技术提出一种轻量化GIM数据的方法,首先对GIM文件进行解析,并对参数化几何进行网格化处理;然后通过顶点属性保持的合并算法去除网格中的冗余数据;最后设计数据存储结构,将几何、材质和属性解耦存储。GIM模型数据轻量化完成后,对顶点合并、模型加载和解析性能进行比较,均取得了较好效果。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Supported by National Hi-Tch Research and Development Program of China
文摘In net-based collaborative design environment, design resources become more and more varied and complex. Besides common information management systems, design resources can be organized in connection with design activities. A set of activities and resources linked by logic relations can form a task. A task has at least one objective and can be broken down into smaller ones. So a design project can be separated into many subtasks forming a hierarchical structure. Task Management System (TMS) is designed to break down these tasks and assign certain resources to its related task nodes. As a result of decomposition, all design resources and activities could be managed via this system. Based on this idea, we realized a TMS which manages collaborative resources in web environment.
文摘近年来,基于电网信息模型(grid information model,GIM)的信息管理研究增多,研究人员需要在地区或场地层面有效地可视化GIM模型对象,并对模型数据进行应用和管理,但GIM模型具有跨度大、精细度高的特点,计算机在可视化和应用GIM模型时容易遇到性能瓶颈。为此,文章基于BIMBase技术提出一种轻量化GIM数据的方法,首先对GIM文件进行解析,并对参数化几何进行网格化处理;然后通过顶点属性保持的合并算法去除网格中的冗余数据;最后设计数据存储结构,将几何、材质和属性解耦存储。GIM模型数据轻量化完成后,对顶点合并、模型加载和解析性能进行比较,均取得了较好效果。
