Paul R Shearing
researcher
Facts
6 properties- Instance of
- human
- Occupation
- researcher
- Award received
- Fellow of the Royal Academy of Engineering[2]
🏷️ Also known as
en Paul R Shearing
en Paul Shearing
🔖 Identifiers · 4
- ORCID iD
- 0000-0002-1387-9531
- Image (Wikimedia Commons)
- Office of Naval Research Global Visit to University College London (5455298).jpg ↗
- Loop ID
- 1001535
- Web of Science ResearcherID
- K-3114-2013
🔗 Connections
Author of 19
4D nano-tomography of electrochemical energy devices using lab-based X-ray imaging,
Algorithmic structural segmentation of defective particle systems: a lithium‐ion battery study,
Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures,
Comparative study of energy management systems for a hybrid fuel cell electric vehicle - A novel mutative fuzzy logic controller to prolong fuel cell lifetime,
Comparison of three‐dimensional analysis and stereological techniques for quantifying lithium‐ion battery electrode microstructures,
Computed Tomography Techniques Help Understand Wear Patterns in Retrieved Total Knee Arthroplasty,
Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes,
Crack detection in lithium-ion cells using machine learning
+11 more
← Authored by 19
4D nano-tomography of electrochemical energy devices using lab-based X-ray imaging,
Algorithmic structural segmentation of defective particle systems: a lithium‐ion battery study,
Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures,
Comparative study of energy management systems for a hybrid fuel cell electric vehicle - A novel mutative fuzzy logic controller to prolong fuel cell lifetime,
Comparison of three‐dimensional analysis and stereological techniques for quantifying lithium‐ion battery electrode microstructures,
Computed Tomography Techniques Help Understand Wear Patterns in Retrieved Total Knee Arthroplasty,
Computer‐Vision‐Based Approach to Classify and Quantify Flaws in Li‐Ion Electrodes,
Crack detection in lithium-ion cells using machine learning
+11 more
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