Yinjiao Xing
Automotive Engineering · 2013–2016
A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Automotive Engineering — 33 citations into it; #427 of 3,081 by trust here, #868 by citations
- Electrical and Electronic Engineering — 25 citations into it
- Safety, Risk, Reliability and Quality — 11 citations into it; #195 of 1,220 by trust here, #112 by citations
- Control and Systems Engineering — 10 citations into it
Whom Yinjiao Xing trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Bhaskar Saha
- Shriram Santhanagopalan
- Wei He
- Trevor J. Hastie
- David D. Lewis
- Jay H. Lee
- Kai Goebel
- Ralph E. White
- Xindong Wu
- Eric R. Ziegel
Who trusts Yinjiao Xing overall
- Kailong Liu
- Z. Ye
- Lifeng Wu
- Qingqing Zhai
- Xiaosong Hu
- S. Y. S. Ng
- Yongzhi Zhang
- Nicholas Williard
- Xiaohui Fu
- Yunlong Shang
Papers from Yinjiao Xing's perspective
Ordered by how strongly the researchers Yinjiao Xing trusts endorse each paper. Same corpus, a different order for every researcher.
- A training algorithm for optimal margin classifiers
- The Elements of Statistical Learning
- An integrated approach to battery health monitoring using bayesian regression and state estimation (2007 IEEE Autotestcon)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Prognostics of lithium-ion batteries based on DempsterShafer theory and the Bayesian Monte Carlo me
- State of charge estimation using an unscented filter for high power lithium ion cells
- An ensemble model for predicting the remaining useful performance of lithium-ion batteries (Microelectronics and reliability)
- Idiot's Bayes—Not So Stupid After All?
- Fast Training of Support Vector Machines using Sequential Minimal Optimization
- On sequential Monte Carlo sampling methods for Bayesian filtering
Their papers
- Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries (2016) — 481 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