Qiang Miao

Control and Systems Engineering · 2007–2020

A researcher in the Science corpus: 9 papers; trusts 17 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 41 citations into it; #1,540 of 8,257 by trust here, #664 by citations
• Automotive Engineering — 39 citations into it; #796 of 3,081 by trust here, #840 by citations
• Artificial Intelligence — 22 citations into it
• Safety, Risk, Reliability and Quality — 17 citations into it; #362 of 1,220 by trust here, #99 by citations
• Mechanical Engineering — 14 citations into it; #3,027 of 8,300 by trust here, #5,233 by citations
• Civil and Structural Engineering — 12 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Mechanics of Materials — 8 citations into it; #2,754 of 6,291 by trust here, #3,944 by citations

Whom Qiang Miao trusts overall

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

1. Jérôme Antoni
2. Stéphane Mallat
3. Jay H. Lee
4. Bhaskar Saha
5. Richard Buessow
6. Wei He
7. Hai Qiu
8. Chao Hu
9. Jianbo Yu
10. Dong Wang

Who trusts Qiang Miao overall

1. Xiaosong Hu
2. Z. Ye
3. Jing Tian
4. Dong Wang
5. Lifeng Wu
6. Nicholas Williard
7. Shengjing Tang
8. Michael G. Pecht
9. Meru A. Patil
10. Yunhong Che

Papers from Qiang Miao's perspective

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

1. Deep Learning
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me
7. APPLICATION OF DISCRETE WAVELET TRANSFORM FOR DETECTION OF BALL BEARING RACE FAULTS
8. Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods
9. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network (2019) — 461 citations
• State-of-charge estimation of lithium-ion batteries using LSTM and UKF (2020) — 398 citations
• A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery (2018) — 512 citations
• Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018) — 416 citations
• Smoothness index-guided Bayesian inference for determining joint posterior probability distributions of anti-symmetric real Laplace wavelet parameters for identification of different bearing faults (2015) — 48 citations
• Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model (2013) — 334 citations
• Remaining useful life prediction of lithium-ion battery with unscented particle filter technique (2013) — 452 citations
• Robust health evaluation of gearbox subject to tooth failure with wavelet decomposition (2009) — 142 citations
• Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models (2007) — 138 citations