David A. Howey
Electrical and Electronic Engineering · Journal of the Electrochemical Society · 2012–2022
A researcher in the Science corpus: 13 papers; trusts 25 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 304 citations into it; #3,165 of 16,539 by trust here, #3,037 by citations
- Automotive Engineering — 300 citations into it; #406 of 3,081 by trust here, #493 by citations
- Control and Systems Engineering — 32 citations into it; #7,987 of 8,257 by trust here, #2,078 by citations
- Mechanical Engineering — 31 citations into it
- Safety, Risk, Reliability and Quality — 19 citations into it
- Artificial Intelligence — 18 citations into it
- Statistical and Nonlinear Physics — 10 citations into it
- Probabilistic & Bayesian Methods — 9 citations into it
Whom David A. Howey trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sébastien Poncet
- Carl E. Rasmussen
- Michael A. Osborne
- Robert R. Richardson
- Kevin P. Murphy
- Christoph R. Birkl
- Xiaosong Hu
- David A. Staton
- Caihao Weng
- Hannes Nickisch
Who trusts David A. Howey overall
Papers from David A. Howey's perspective
Ordered by how strongly the researchers David A. Howey trusts endorse each paper. Same corpus, a different order for every researcher.
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Learning Multiple Layers of Features from Tiny Images
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Machine learning - a probabilistic perspective
- On the difficulty of training recurrent neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Their papers
- Degradation diagnostics for lithium ion cells (2017) — 1,527 citations
- Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries (2017) — 315 citations
- Gaussian process regression for forecasting battery state of health (2017) — 627 citations
- Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries (2019) — 385 citations
- Battery internal temperature estimation by combined impedance and surface temperature measurement (2014) — 257 citations
- Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021) — 309 citations
- Module design and fault diagnosis in electric vehicle batteries (2012) — 194 citations
- Review—"Knees" in Lithium-Ion Battery Aging Trajectories (2022) — 373 citations
- The challenge and opportunity of battery lifetime prediction from field data (2021) — 335 citations
- Battery warm-up methodologies at subzero temperatures for automotive applications: Recent advances and perspectives (2020) — 381 citations
- A Parametric Open Circuit Voltage Model for Lithium Ion Batteries (2015) — 160 citations
- Time-domain fitting of battery electrochemical impedance models (2015) — 144 citations
- Air-Gap Convection in Rotating Electrical Machines (2012) — 313 citations