Daeil Kwon
Sungkyunkwan University
52 Papers
137 Citations
Daeil Kwon is an academic researcher from Sungkyunkwan University. The author has contributed to research in topics: Computer science & Prognostics. The author has an hindex of 13, co-authored 50 publications. Previous affiliations of Daeil Kwon include Konkuk University & Intel.
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Papers
Mitigation strategies for Li-ion battery thermal runaway: A review
TL;DR: A review of safety strategies for Li-ion batteries, including positive temperature coefficient thermistors, current interrupt devices, safety vents, protection circuitry, shutdown separators, electrolyte additives, safe electrolytes, passive protection designs in battery packages, and battery management systems is presented in this paper.
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Early identification of emerging technologies: A machine learning approach using multiple patent indicators
TL;DR: The case of pharmaceutical technology shows that the proposed machine learning approach to identifying emerging technologies at early stages using multiple patent indicators that can be defined immediately after the relevant patents are issued can facilitate responsive technology forecasting and planning.
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Accelerated degradation model for C-rate loading of lithium-ion batteries
TL;DR: In this paper, the authors developed an accelerated capacity fade model for Li-ion batteries under multiple C-rate loading conditions, to translate the performance and degradation of a battery population at accelerated Crate conditions to normal C-Rate conditions.
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Early Detection of Interconnect Degradation by Continuous Monitoring of RF Impedance
TL;DR: In this article, the authors demonstrate the value of RF impedance measurements as an early indicator of physical degradation of solder joints as compared to dc-resistance measurements, and compare their respective sensitivities in detecting interconnect degradation.
A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability
TL;DR: Li et al. as mentioned in this paper proposed a convolutional neural network model to estimate the future state-of-health (SOH) value of Li-ion batteries in the early phases of qualification tests.
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