Kwok Leung Tsui
Automotive Engineering · 2001–2020
A researcher in the Science corpus: 17 papers; trusts 43 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Automotive Engineering — 64 citations into it; #428 of 3,081 by trust here, #799 by citations
- Electrical and Electronic Engineering — 61 citations into it
- Control and Systems Engineering — 49 citations into it
- Statistics and Probability — 41 citations into it; #4,945 of 12,932 by trust here, #3,330 by citations
- Safety, Risk, Reliability and Quality — 36 citations into it; #167 of 1,220 by trust here, #76 by citations
- Artificial Intelligence — 28 citations into it
- Mechanical Engineering — 23 citations into it
- Management Science and Operations Research — 18 citations into it; #7,577 of 11,946 by trust here, #4,602 by citations
Whom Kwok Leung Tsui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jérôme Antoni
- Jerome H. Friedman
- Leo Breiman
- Timothy W. Simpson
- Fred J. Hickernell
- Jianbo Yu
- Xiao Wang
- Brian S. Yandell
- George V. Moustakides
- Jay H. Lee
Who trusts Kwok Leung Tsui overall
- Z. Ye
- Kailong Liu
- Qingqing Zhai
- Lifeng Wu
- S. Shan
- William H. Woodall
- A. Ryan
- Murat Caner Testik
- Amandine Marrel
- Min Xie
Papers from Kwok Leung Tsui's perspective
Ordered by how strongly the researchers Kwok Leung Tsui trusts endorse each paper. Same corpus, a different order for every researcher.
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A review on design, modeling and applications of computer experiments (2006) — 337 citations
- On the Performance of Geometric Charts with Estimated Control Limits (2002) — 81 citations
- EARLY DETECTION OF A CHANGE IN POISSON RATE AFTER ACCOUNTING FOR POPULATION SIZE EFFECTS (2011) — 73 citations
- Run-Length Performance of Regression Control Charts with Estimated Parameters (2004) — 71 citations
- A Minimum Bias Latin Hypercube Design (2001) — 55 citations
- SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline (2020) — 52 citations
- Degradation Data Analysis Using Wiener Processes With Measurement Errors (2013) — 354 citations
- A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries (2018) — 294 citations
- Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries (2019) — 299 citations
- A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis (2019) — 159 citations
- A universal deep learning approach for modeling the flow of patients under different severities (2018) — 45 citations
- Brownian motion with adaptive drift for remaining useful life prediction: Revisited (2018) — 100 citations
- Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018) — 416 citations
- Forecasting tourist arrivals with machine learning and internet search index (2017) — 336 citations
- A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem (2016) — 312 citations
- A naive Bayes model for robust remaining useful life prediction of lithium-ion battery (2014) — 309 citations
- An ensemble model for predicting the remaining useful performance of lithium-ion batteries (2013) — 615 citations