探讨如何将“AI for Science”化为“Decision Intelligence for Decision Science(DI4DS)”,使人工智能和智能科技成为变革传统指挥控制科学与技术的新动力并成为确保军事力量和国防安全的新科技。主要围绕决策智能理念、方法、技术的...探讨如何将“AI for Science”化为“Decision Intelligence for Decision Science(DI4DS)”,使人工智能和智能科技成为变革传统指挥控制科学与技术的新动力并成为确保军事力量和国防安全的新科技。主要围绕决策智能理念、方法、技术的历史演化以及ISDOS、EMS、计算机会议、决策剧场、平行剧场的历史进程,讨论决策5.0和平行决策智能及相关交互数字剧场等新人工智能技术在未来C++ISR的作用与意义。展开更多
Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play...Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.展开更多
文摘探讨如何将“AI for Science”化为“Decision Intelligence for Decision Science(DI4DS)”,使人工智能和智能科技成为变革传统指挥控制科学与技术的新动力并成为确保军事力量和国防安全的新科技。主要围绕决策智能理念、方法、技术的历史演化以及ISDOS、EMS、计算机会议、决策剧场、平行剧场的历史进程,讨论决策5.0和平行决策智能及相关交互数字剧场等新人工智能技术在未来C++ISR的作用与意义。
基金sponsored by the National Science and Technology Major Project(No.2011ZX05023-005-006)
文摘Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.
