...., as the constant-current discharge capacity) either using the measurements directly [ 25 – 28 ] or via predefined features extracted from measurements [ 29 – 34 ]. To obtain these mappings, supervised machine-learning techniques such as neural networks [ 25 , 26 ], Gaussian process regression [ 29 , 35 , 36...

... author. e-mail:  tofigh@ualberta.ca machine learning modeling system identification fuel cell deep neural network Cummins Incorporated 10.13039/100008298 RES0055264 Natural Sciences and Engineering Research Council of Canada 10.13039/501100000038 ALLRP 587193 - 23...

... and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine-learning (ML) model to predict...

...-mail:  y.p.zhao@163.com e-mail:  1767363682@qq.com e-mail:  bhhaoz@126.com e-mail:  909818346@qq.com e-mail:  yangzhe422@126.com e-mail:  792802475@qq.com 04 06 2018 04 02 2020 01 06 2020 machine learning extreme learning machine class imbalance learning fault detection...