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Convolutional neural network battery pack classification - ..:

, In: 7th E-Mobility Power System Integration Symposium (EMOB 2023),

Andersen, H. ; Paasch, K. M. - p. None , 2023

Link: https://doi.org/10.1049/icp.2023.2705

RT 
T1 <cTitle>7th E-Mobility Power System Integration Symposium (EMOB 2023)</cTitle>: T1 <Title>Convolutional neural network battery pack classification - Gramian angular field vs. Markov transition field</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10324700&Exemplar=1&LAN=DE
				
A1 Andersen, H.
A1 Paasch, K. M.
YR 2023
K1 convolutional neural nets
K1 deep learning (artificial intelligence)
K1 feature extraction
K1 image classification
K1 Markov processes
K1 time series
K1 10 pretrained neural networks
K1 AI
K1 battery factory test system
K1 battery pack manufacturer
K1 convolutional neural network battery pack classification - Gramian
K1 different battery pack types
K1 Gramian Angular Field
K1 important test features
K1 long test time
K1 main time-consuming aspects
K1 Markov Transition Field methods
K1 standard convolutional neural network structures
K1 test outcome
K1 test type
K1 time series data
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2023.2705
DO https://doi.org/10.1049/icp.2023.2705
SF ELIB - SuUB Bremen

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