[1] Zhang C, Wang F. Application of photo-acoustic spectroscopy technology to dissolved gas analysis in oil of oil-immersed power transformer [J]. High Voltage Engineering, 2005, 31(2): 84-86.
[2] Tan Z H, Tang J, Sun C X, et al. Photoacoustic spectroscopy applied to the detection of SF6 decomposition components under partial discharge [J]. Journal of Chongqing University, 2013, 36(8): 68-75.
[3] Méda B, Fortun-Lamothe L, Hassouna M. Prediction of nutrient flows with potential impacts on the environment in a rabbit farm: A modelling approach [J]. Animal Production Science, 2014, 54(12): 2042-2051.
[4] Bustamante S, Manana M, Arroyo A, et al. Dissolved gas analysis equipment for online monitoring of transformer oil: A review [J]. Sensors (Basel, Switzerland), 2019, 19(19): 4057.
[5] Zhang W. Research on the Photoacoustic Spectroscopy for Trace Gas Detection and Applications [D]. Dalian: Dalian University of Technology, 2010.
[6] Du X F, Qian X M, Liu Q, et al. Effect of relative humidity on photoacoustic signal [J]. Acta Optica Sinica, 2017, 37(2): 0230003.
[7] Tang J, Fan M, Qiu Y J, et al. Temperature property of photoacoustic spectroscopy detection for SF6 decomposition components under partial discharge [J]. High Voltage Engineering, 2012, 38(11): 2919-2926.
[8] Chang C C, Lin C J. Training nu-support vector regression: Theory and algorithms [J]. Neural Computation, 2002, 14(8): 1959-1977.
[9] Hsu C W, Lin C J. A comparison of methods for multiclass support vector machines [J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.
[10] Chen P H, Lin C J, Scholkopf B. A tutorial on ν-support vector machines [J]. Applied Stochastic Models in Business and Industry, 2005, 21(2): 111-136.
[11] Fan R E, Chen P H, Lin C J. Working set selection using second order information for training SVM [J]. Journal of Machine Learning Research, 2005, 6: 1889-1918.
[12] Chang C C, Lin C J. LIBSVM: A library for support vector machines [J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-27.
