融合海量数据库结构模式识别研究进展

Jesús Vega, G. Rattá, A. Murari, P. Castro, S. Dormido-Canto, R. Dormido, G. Farías, A. Pereira, A. Portas, E. L. Luna, I. Pastor, Jose Angel Sanchez, N. Duro, R. Castro, Matilde Santos, H. Vargas
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引用次数: 6

摘要

核聚变装置的物理研究需要对大量的放电进行统计分析。考虑到等离子体的复杂性和相关参数之间的非线性相互作用,将物理现象与其产生的信号模式联系起来是非常困难的。迄今为止,数据检索通常是通过信号名称和射击数来完成的。对要分析的时间段的搜索是用人工的方式进行的。人工搜索数据库必须被智能技术取代,以自动化的方式查找数据。结构模式识别技术已被证明是在JET和TJ-II数据库中索引和检索数据的非常有效的方法。波形和图像可以通过几个结构模式识别应用程序访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recent results on structural pattern recognition for Fusion massive databases
Physics studies in fusion devices require statistical analyses of a large number of discharges. Given the complexity of the plasma and the non-linear interactions between the relevant parameters, connecting a physical phenomenon with the signal patterns that it generates can be quite demanding Up to now, data retrieval has been typically accomplished by means of signal name and shot number. The search of the temporal segment to analyze has been carried out in a manual way. Manual searches in databases must be replaced by intelligent techniques to look for data in an automated way. Structural pattern recognition techniques have proven to be very efficient methods to index and retrieve data in JET and TJ-II databases. Waveforms and images can be accessed through several structural pattern recognition applications.
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