Zhou, Kai, Diehl, Edward, and Tang, Jiong. Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations. Retrieved from https://par.nsf.gov/biblio/10373946. Mechanical Systems and Signal Processing 185.C Web. doi:10.1016/j.ymssp.2022.109772.

Zhou, Kai, Diehl, Edward, & Tang, Jiong. Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations. Mechanical Systems and Signal Processing, 185 (C). Retrieved from https://par.nsf.gov/biblio/10373946. https://doi.org/10.1016/j.ymssp.2022.109772

Zhou, Kai, Diehl, Edward, and Tang, Jiong. "Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations". Mechanical Systems and Signal Processing 185 (C). Country unknown/Code not available. https://doi.org/10.1016/j.ymssp.2022.109772. https://par.nsf.gov/biblio/10373946.

@article{osti_10373946,
place = {Country unknown/Code not available}, title = {Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations}, url = {https://par.nsf.gov/biblio/10373946}, DOI = {10.1016/j.ymssp.2022.109772}, abstractNote = {}, journal = {Mechanical Systems and Signal Processing}, volume = {185}, number = {C}, author = {Zhou, Kai and Diehl, Edward and Tang, Jiong}, }