Haidong Shao

Control and Systems Engineering · 2015–2023

A researcher in the Science corpus: 11 papers; trusts 43 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 67 citations into it; #1,375 of 8,257 by trust here, #508 by citations
• Mechanical Engineering — 38 citations into it; #1,134 of 8,300 by trust here, #1,911 by citations
• Artificial Intelligence — 34 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Mechanics of Materials — 23 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Model Architectures — 11 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Haidong Shao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yaguo Lei
4. Yann LeCun
5. Honglak Lee
6. Prasanna Tamilselvan
7. Alex Krizhevsky
8. Matthew D. Zeiler
9. Jianbo Yu
10. Andrew L. Maas

Who trusts Haidong Shao overall

1. Shen Zhang
2. Shibo Zhang
3. Weiting Zhang
4. Long Wen
5. Bingnan Wang
6. Dhiraj Neupane
7. Zhuyun Chen
8. Thomas G. Habetler
9. Yifei Ding
10. Te Han

Papers from Haidong Shao's perspective

Ordered by how strongly the researchers Haidong Shao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention (2023) — 343 citations
• Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework (2022) — 316 citations
• Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning (2021) — 336 citations
• A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation (2021) — 334 citations
• Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images (2020) — 342 citations
• Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing (2018) — 349 citations
• Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018) — 434 citations
• A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders (2018) — 434 citations
• An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017) — 293 citations
• A novel deep autoencoder feature learning method for rotating machinery fault diagnosis (2017) — 630 citations
• Rolling bearing fault diagnosis using an optimization deep belief network (2015) — 495 citations