K
Kezhi Mao
Researcher at Nanyang Technological University
Publications - 116
Citations - 6736
Kezhi Mao is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Feature extraction & Feature selection. The author has an hindex of 28, co-authored 106 publications receiving 5079 citations. Previous affiliations of Kezhi Mao include DSO National Laboratories.
Papers
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Journal ArticleDOI
Deep learning and its applications to machine health monitoring
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
Book
Extreme Learning Machine
Erik Cambria,Guang-Bin Huang,Liyanaarachchi Lekamalage Chamara Kasun,Hongming Zhou,Chi-Man Vong,Jiarun Lin,Jianping Yin,Zhiping Cai,Qiang Liu,Kuan Li,Victor C. M. Leung,Liang Feng,Yew-Soon Ong,Meng-Hiot Lim,Anton Akusok,Amaury Lendasse,Francesco Corona,Rui Nian,Yoan Miche,Paolo Gastaldo,Rodolfo Zunino,Sergio Decherchi,Xuefeng Yang,Kezhi Mao,Beom-Seok Oh,Jehyoung Jeon,Kar-Ann Toh,Andrew Beng Jin Teoh,Jaihie Kim,Hanchao Yu,Yiqiang Chen,Junfa Liu +31 more
TL;DR: This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation.
Journal ArticleDOI
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
TL;DR: Inspired by the success of deep learning methods that redefine representation learning from raw data, this work proposes local feature-based gated recurrent unit (LFGRU) networks, a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring.
Journal ArticleDOI
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.
TL;DR: A deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data and is able to outperform several state-of-the-art baseline methods.
Journal ArticleDOI
Probabilistic neural-network structure determination for pattern classification
Kezhi Mao,K.-C. Tan,Wee Ser +2 more
TL;DR: A supervised network structure determination algorithm that identifies an appropriate smoothing parameter using a genetic algorithm and determines suitable pattern layer neurons using a forward regression orthogonal algorithm is proposed.