In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it ...In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it isshown that both the existence and their mutual arrangement of faults could obviously influence the overallcharacters of earthquake process. Then the characters of each stage of model evolution are explained withself-organized critical state theory. Finally, earthquake sequences produced by the models are analysed interms pf algorithmic complexity and the result shows that AC-values of algorithmic complexity could be usedto study earthquake process and evolution.展开更多
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
文摘In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it isshown that both the existence and their mutual arrangement of faults could obviously influence the overallcharacters of earthquake process. Then the characters of each stage of model evolution are explained withself-organized critical state theory. Finally, earthquake sequences produced by the models are analysed interms pf algorithmic complexity and the result shows that AC-values of algorithmic complexity could be usedto study earthquake process and evolution.
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.
