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Multi-source dynamic time series data processing method bas..:

, In: 12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022),

Geng, H. ; Yuan, F. ; Suo, B. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.2988

RT 
T1 <cTitle>12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)</cTitle>: T1 <Title>Multi-source dynamic time series data processing method based on comprehensive weighted fusion</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10110530&Exemplar=1&LAN=DE
				
A1 Geng, H.
A1 Yuan, F.
A1 Suo, B.
YR 2022
K1 regression analysis
K1 sensor fusion
K1 time series
K1 AHP method
K1 comprehensive weight
K1 comprehensive weighted fusion method
K1 cyclic scoring method
K1 data source
K1 different dynamic data samples
K1 discrete static data
K1 dynamic data standard deviation expectations
K1 dynamic response
K1 fusion problem
K1 multisource dynamic data fusion algorithm
K1 multisource dynamic time series data processing method
K1 multisource heterogeneous dynamic data sources
K1 objective comprehensive weighting idea
K1 outlier detection method
K1 posterior weight
K1 prior weight
K1 similarity measure
K1 subjective weighting idea
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.2988
DO https://doi.org/10.1049/icp.2022.2988
SF ELIB - SuUB Bremen

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