Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments

Long Short-Term Memory Networks for the Automated Identification of the Stationary Phase in Tribological Experiments

This study outlines the development and optimization of a Long Short-Term Memory (LSTM) network designed to analyze and classify time-series data from tribological experiments, with a particular emphasis on identifying stationary phases. The process of fine-tuning key hyperparameters was systematica...

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Bibliographic Details
Main Authors:	Yuxiao Zhao, Leyu Lin, Alois K. Schlarb
Format:	Article
Language:	English
Published:	MDPI AG 2024-11-01
Series:	Lubricants
Subjects:	artificial neural network (ANN) long short-term memory (LSTM) tribology polymer-based composites
Online Access:	https://www.mdpi.com/2075-4442/12/12/423
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