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These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers.
The simplified crack model may also be used to generate training data for pattern recognition approaches to health monitoring.
The resulting one-to-many mapping between patterns and their classes cannot be adequately handled by traditional pattern recognition approaches.
Over the last two decades pattern recognition approaches have attracted engineers to solve real world problems more accurately through the development of computational technology.
When combined with powerful pattern recognition approaches, a PC-PTF can be trained to learn the shape of WRC from a discrete set of measured points, unlike traditional parametric PTFs which follow a predefined WRC shape dictated by the selected soil hydraulic equations.
Pattern recognition approaches divide into further two categories.
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We use a pattern recognition approach in terms of feature generation and classifier design.
We report a pattern recognition approach to detect the atomic structure in high-resolution transmission electron microscopy images of graphene.
An unsupervised pattern recognition approach was employed to separate the data into signal subsets which were then classified and assigned to differing mechanisms.
This paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR).
This paper presents a new approach for fault analysis of advanced series compensated (thyristor controlled series compensated) line using pattern recognition approach.
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