@article{oai:repo.qst.go.jp:00082838,
 author = {Fumiyoshi, Kin and Tomohide, Nakano and Naoyuki, Oyama and Akihiro, Terakado and Takuma, Wakatsuki and Emi, Narita and Fumiyoshi, Kin and Tomohide, Nakano and Naoyuki, Oyama and Akihiro, Terakado and Takuma, Wakatsuki and Emi, Narita},
 issue = {5},
 journal = {Review of Scientific Instruments},
 month = {May},
 note = {We have developed a denoising autoencoder based neural network (NN) method to determine a spectral line intensity with an uncertainty lower than the uncertainty determined by fitting the spectral line. The NN method processes the measured raw spectral line shape, providing a single Gaussian shape based on the training dataset, which consists of synthetically prepared Doppler shift and broadening free spectral lines in the present work. It is found that the uncertainty reduction level significantly depends on the training dataset. Limitations originating from the training dataset are also discussed.},
 title = {Prediction of a single Gaussian shape of spectral line measured with low-dispersion spectrometer by using machine learning},
 volume = {92},
 year = {2021}
}