核聚变海量数据库信息检索与知识提取新技术

A. Murari, J. Vega, J. Alonso, E. De LaLuna, J. Farthing, C. Hidalgo, G. Rattá, J. Svensson, G. Vagliasindi
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引用次数: 1

摘要

与磁约束聚变相关的反应堆实验,如JET,每次发射已经产生数十gb的数据,而下一步的设备,ITER,预计需要更多的数量级。有效地管理如此大量的数据需要新的技术,从信号存储和信息检索到物理解释的数据分析。JET正在为所有主要问题作出重大努力。目前正在开发用于单维和二维信号的无损数据压缩,以及用于图像处理的新技术(定向小波和细胞非线性网络)。结构模式识别在信息检索方面显示出巨大的潜力。统计方法,如贝叶斯推理和回归树,正在系统地研究,从所有可用的测量中提取所需的知识。其他软计算技术,如模糊逻辑和人工神经网络,是处理当前和不久将来实验的巨大复杂性和不确定性的非常强大的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Techniques and Technologies for Information Retrieval and Knowledge Extraction from Nuclear Fusion Massive Databases
Reactor relevant experiments for Magnetic Confinement Fusion, like JET, produce already tens of GBytes of data per shot and the next step device, ITER, is expected to require orders of magnitude more. Managing such vast quantities of data in an efficient way needs new techniques, ranging from signal storage and information retrieval to data analysis for physical interpretation. At JET significant efforts are being devoted to all the main issues. Lossless data compression is under development for both mono and bi-dimensional signals, together with new techniques and technologies for image processing (directional wavelets and Cellular Non-linear Networks). Structural pattern recognition has shown great potential for information retrieval. Statistical methods, like Bayesian inference and regression trees, are being systematically investigated, to extract the required knowledge from all the available measurements. Other Soft Computing techniques, like Fuzzy Logic and Artificial Neural Networks, are very powerful tools to handle the great complexity and uncertainties of present day and near future experiments.
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