In this paper, a large number of AE signals in the process of a three-point bending test were studied and the pattern recognition system of refractory materials ...
Abstract. The determination of the damage mode and the quantitative description of the damage of the clustered acoustic emission (AE) signal of the.
Bibliographic details on Damage Pattern Recognition of Refractory Materials Based on BP Neural Network.
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This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method ...
Damage Pattern Recognition of Refractory Materials Based on BP Neural Network · Damage Pattern Recognition and Feature Extraction of MgO–C Refractory · Acoustic ...
Jun 19, 2024 · Spatial-Temporal Pattern Analysis on Journal Paper Submissions. ... Damage Pattern Recognition of Refractory Materials Based on BP Neural Network.
Damage pattern recognition of refractory materials based on BP neural network ... Damage Pattern Recognition and Feature Extraction of MgO–C Refractory.
Dec 13, 2012 · The k-means algorithm was used to divide the acoustic emission signals collected during the three-point bending test into two types. Combining ...
Missing: BP Neural
Jul 12, 2024 · The results show that the BP neural network model is more accurate than the traditional fitting model in assessing the compressive strength.
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