Naipeng Li
Control and Systems Engineering · 2015–2020
A researcher in the Science corpus: 9 papers; trusts 28 researchers and is trusted by 15 researchers here, as inferred from the citation graph.
Subfields
- Control and Systems Engineering — 147 citations into it; #1,866 of 8,257 by trust here, #359 by citations
- Mechanical Engineering — 80 citations into it; #8,119 of 8,300 by trust here, #3,227 by citations
- Artificial Intelligence — 51 citations into it
- Mechanics of Materials — 41 citations into it; #2,326 of 6,291 by trust here, #831 by citations
- Safety, Risk, Reliability and Quality — 35 citations into it; #966 of 1,220 by trust here, #194 by citations
- Representation & Self-Supervised Learning — 14 citations into it
- Civil and Structural Engineering — 11 citations into it
- Automotive Engineering — 10 citations into it
Whom Naipeng Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yaguo Lei
- Arnaud Doucet
- Marcos Eduardo Orchard
- Kamal Medjaher
- M. Sanjeev Arulampalam
- Noureddine Zerhouni
- Neil J. Gordon
- Vladimir Naumovich Vapnik
- Brigitte Chebel-Morello
- Tarak Benkedjouh
Who trusts Naipeng Li overall
- Yaguo Lei
- Biao Wang
- Shen Zhang
- Boyuan Yang
- Liang Guo
- Ningbo Li
- Jinyang Jiao
- Jun Zhu
- Dhiraj Neupane
- Xiang Li
Papers from Naipeng Li's perspective
Ordered by how strongly the researchers Naipeng Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- GENERATIVE ADVERSARIAL NETS
Their papers
- Applications of machine learning to machine fault diagnosis: A review and roadmap (2020) — 2,573 citations
- A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020) — 1,563 citations
- Deep separable convolutional network for remaining useful life prediction of machinery (2019) — 343 citations
- XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial (2019) — 218 citations
- Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019) — 1,048 citations
- Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018) — 2,251 citations
- A recurrent neural network based health indicator for remaining useful life prediction of bearings (2017) — 1,105 citations
- A Model-Based Method for Remaining Useful Life Prediction of Machinery (2016) — 526 citations
- An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings (2015) — 594 citations