Neural Network Model for Assessing the Physical and Mechanical Properties of a Metal Material Based on Deep Learning

Abstract

The paper investigates the algorithmic stability of learning a deep neural network in problems of recognition of the materials microstructure. It is shown that at 8% of quantitative deviation in the basic test set the algorithm trained network loses stability. This means that with such a quantitative or qualitative deviation in the training or test sets, the results obtained with such trained network can hardly be trusted. Although the results of this study are applicable to the particular case, i.e. problems of recognition of the microstructure using ResNet-152, the authors propose a cheaper method for studying stability based on the analysis of the test, rather than the training set.

Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

“…Inspired by visual arts research in bio-agriculture, the joint neural network's ability to learn and express energy resources has been widely used in the field of natural languages, such as the distribution of text and the distribution of hearing [13]. In the native function of CNN, the word vector formed by a sentence is used as a single input, and then the rotation function is performed by multiple rotation kernels matching the size of the word vector to obtain the attributes of several consecutive words.…”