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Computer methods for ITER-like materials LIBS diagnostics

机译:iter样材料Libs诊断的计算机方法

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摘要

Recent development of Laser-Induced Breakdown Spectroscopy (LIBS) caused that this method is considered as the most promising for future diagnostic applications for characterization of the deposited materials in the International Thermonuclear Experimental Reactor (ITER), which is currently under construction. In this article the basics of LIBS are shortly discussed and the software for spectra analyzing is presented. The main software function is to analyze measured spectra with respect to the certain element lines presence. Some program operation results are presented. Correct results for graphite and aluminum are obtained although identification of tungsten lines is a problem. The reason for this is low tungsten lines intensity, and thus low signal to noise ratio of the measured signal. In the second part artificial neural networks (ANNs) as the next step for LIBS spectra analyzing are proposed. The idea is focused on multilayer perceptron network (MLP) with backpropagation learning method. The potential of ANNs for data processing was proved through application in several LIBS-related domains, e.g. differentiating ancient Greek ceramics (discussed). The idea is to apply an ANN for determination of W, A1, C presence on ITER-like plasma-facing materials.
机译:激光诱导的击穿光谱(LIBS)的最新发展导致该方法被认为是未来诊断应用的最有希望的,用于在目前正在建设的国际热核实验反应器(浸泡)中表征沉积材料。在本文中,不久讨论了LIB的基础知识,并提出了用于光谱分析的软件。主软件功能是分析关于某些元素线的测量光谱。提供了一些程序操作结果。尽管钨线的鉴定是一个问题,但获得了石墨和铝的正确结果。其原因是低钨线强度,从而低信噪比测量信号。在第二部分中,提出了作为Libs谱分析的下一步骤的人工神经网络(ANN)。这些想法专注于多层的Perceptron网络(MLP),具有反向化学习方法。通过在几个LIBS相关的域中的应用,证明了数据处理的ANNS的潜力,例如,在几个LIBS相关的域中证明了。区分古希腊陶瓷(讨论)。该思想是应用一个ANN,用于测定艾尔样等离子体材料上的W,A1,C存在。

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