基于GO法的Nd：YAG侧面泵浦激光器可靠性分析

本文报道了基于GO（Goad Oriented）法的Nd∶YAG侧面泵浦激光器可靠性分析，建立了Nd∶YAG侧面泵浦激光器的GO图模型，定性分析了Nd∶YAG侧面泵浦激光器的薄弱环节，定量计算了激光头中各薄弱环节的可靠性结果，包括等效故障率λ_R、等效维修率μ_R、平均无故障工作时间（MTBF）等；并定量计算了KTP晶体故障率的改善对Nd∶YAG侧面泵浦激光器可靠性提高的影响。本文中建立的GO图模型可为Nd∶YAG侧面泵浦激光的优化及可靠性评价提供理论依据。     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.