提出将支持向量机网络应用于含不同浓度杂质气体的非线性荧光光谱的识别。
That the support vector machine network is applied to recognize the nonlinear fluorescence spectrum of impurities of different concentrations in air is proposed.
提出利用物质的荧光光谱联合人工神经网络识别大气中杂质气体成分的新方法。
A new method for recognizing gas components in air based on nonlinear fluorescence spectra combined with a neural network model was proposed.
基于杂质气体在阴极表面的吸附，理论推导并得到了阴极寿命与真空度的关系。
Based on the adsorption of harmful gases on the cathode surface, an equation about the relationship between the cathode lifetime and the pressure was deduced theoretically.
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