Robert X. Gao
Control and Systems Engineering · 2004–2020
A researcher in the Science corpus: 17 papers; trusts 64 researchers and is trusted by 30 researchers here, as inferred from the citation graph.
Subfields
- Control and Systems Engineering — 144 citations into it; #869 of 8,257 by trust here, #837 by citations
- Mechanical Engineering — 100 citations into it; #2,733 of 8,300 by trust here, #4,353 by citations
- Artificial Intelligence — 66 citations into it; #28,104 of 35,345 by trust here, #12,083 by citations
- Industrial and Manufacturing Engineering — 63 citations into it; #296 of 2,521 by trust here, #152 by citations
- Computer Vision and Pattern Recognition — 57 citations into it
- Mechanics of Materials — 51 citations into it; #2,257 of 6,291 by trust here, #1,992 by citations
- Civil and Structural Engineering — 20 citations into it; #2,472 of 6,160 by trust here, #5,397 by citations
- Electrical and Electronic Engineering — 20 citations into it
Whom Robert X. Gao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zhigang Tian
- Shunzheng Yu
- Jan M. van Noortwijk
- Geoffrey E. Hinton
- Xun Xu
- Fei Tao
- Xiangyu Wang
- Keith Worden
- Yoshua Bengio
- Hasan Ocak
Who trusts Robert X. Gao overall
- Thomas Walter Rauber
- Jianbo Yu
- Dhiraj Neupane
- Xiaoxi Ding
- Rui Zhao
- Abdenour Soualhi
- Hongtian Chen
- Francisco de Assis Boldt
- Kamal Medjaher
- Jing Tian
Papers from Robert X. Gao's perspective
Ordered by how strongly the researchers Robert X. Gao trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines (2012) — 356 citations
- A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests (2017) — 609 citations
- DCNN-Based Multi-Signal Induction Motor Fault Diagnosis (2020) — 356 citations
- Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020) — 429 citations
- Deep learning and its applications to machine health monitoring (2019) — 2,506 citations
- WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019) — 515 citations
- Machine learning-based image processing for on-line defect recognition in additive manufacturing (2019) — 379 citations
- Deep learning for smart manufacturing: Methods and applications (2018) — 1,515 citations
- Long short-term memory for machine remaining life prediction (2018) — 411 citations
- Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018) — 463 citations
- A multi-time scale approach to remaining useful life prediction in rolling bearing (2017) — 216 citations
- Cloud-enabled prognosis for manufacturing (2015) — 315 citations
- Wavelets for fault diagnosis of rotary machines: A review with applications (2014) — 1,410 citations
- Wavelets: Theory and Applications for Manufacturing (2010) — 358 citations
- Harmonic wavelet-based data filtering for enhanced machine defect identification (2010) — 68 citations
- Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring (2006) — 359 citations
- PCA-based feature selection scheme for machine defect classification (2004) — 562 citations