参考文献/References:
[1] 郑晓红. 锅炉“四管”的失效机理研究与寿命预测[D]. 杭州: 浙江大学, 2002.
[2] 崔强. 锅炉受热面四管泄漏的原因分析及预防措施[J]. 中国设备工程, 2018(17): 58–59
[3] 李军. 激光诱导击穿光谱应用于电站锅炉受热面组织结构和性能诊断的研究[D]. 广州: 华南理工大学, 2016.
[4] YAO Shunchun, DONG Meirong, LU Jidong. Correlation between grade of pearlite spheroidization and laser induced spectra[J]. Laser physics, 2013, 23(12): 125702.
[5] 董美蓉, 韦丽萍, 陆继东, 等. 基于K-CV参数优化支持向量机的LIBS燃煤热值定量分析[J]. 光谱学与光谱分析, 2019, 39(7): 2202–2209
[6] LU Shengzi, DONG Meirong, HUANG Jianwei, et al. Estimation of the aging grade of T91 steel by laser-induced breakdown spectroscopy coupled with support vector machines[J]. Spectrochimica acta part B: atomic spectroscopy, 2018, 140: 35–43.
[7] 董璇. TP347H受热面材料的激光光谱特性分析[D]. 广州: 华南理工大学, 2015.
[8] LI Jun, LU Jidong, DAI Yuan, et al. Correlation between aging grade of T91 steel and spectral characteristics of the laser-induced plasma[J]. Applied surface science, 2015, 346: 302–310.
[9] GB/T4338-2006 金属材料高温拉伸试验方法[S].
[10] DL/T884-2004 火电厂金相检验与评定技术导则[S].
[11] HUANG Jianwei, DONG Meirong, LU Shengzi, et al. Estimation of the mechanical properties of steel via LIBS combined with canonical correlation analysis (CCA) and support vector regression (SVR)[J]. Journal of analytical atomic spectrometry, 2018, 33(5): 720–729.
[12] 屈华阳, 胡净宇, 赵雷, 等. 激光诱导击穿光谱法分析表面氧化钢铁样品中的碳硅锰磷硫铬镍铜铝[J]. 冶金分析, 2012, 32(7): 1–6
[13] 尚文利, 李琳, 万明, 等. 基于优化单类支持向量机的工业控制系统入侵检测算法[J]. 信息与控制, 2015, 44(6): 678–684
[14] 周中寒, 田雪咏, 孙兰香, 等. Fiber-LIBS技术结合SVM鉴定铝合金牌号[J]. 激光与光电子学进展, 2018, 55(6): 424–430
[15] SHU Xin, LAI Darong, XU Huanliang, et al. Learning shared subspace for multi-label dimensionality reduction via dependence maximization[J]. Neurocomputing, 2015, 168: 356–364.
[16] LIN Yaojin, HU Qinghua, LIU Jinghua, et al. Multi-label feature selection based on neighborhood mutual information[J]. Applied soft computing, 2016, 38: 244–256.
[17] 程玉胜, 宋帆, 王一宾, 等. 基于专家特征的条件互信息多标记特征选择算法[J]. 计算机应用, 2020, 40(2): 503–509
[18] PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: machine learning in python[J]. Journal of machine learning research, 2011, 12(10): 2825–2830.
[19] LU Shengzi, SHEN Shen, HUANG Jianwei, et al. Feature selection of laser-induced breakdown spectroscopy data for steel aging estimation[J]. Spectrochimica acta part B: atomic spectroscopy, 2018, 150: 49–58.
[20] KERBAA T H, MEZACHE A, OUDIRA H. Model selection of sea clutter using cross validation method[J]. Procedia computer science, 2019, 158: 394–400.
[21] 孙健, 李琪, 陈明强, 等. 基于机器学习的油气水层随钻识别模型优选[J]. 西安石油大学学报(自然科学版), 2019, 34(5): 79–85