Aiming at the low classification accuracy of unbalanced data sets, an improved SMOTE over-sampling algorithm ACC-SMOTE (Ant Colony Clustering Synthetic Minority Oversampling Technology) based on ant colony clustering is proposed. On the one hand, the improved ant colony clustering algorithm is used to divide a small number of samples into different sub-clusters, fully considered the imbalance between inter-cluster and intra-cluster data, and SMOTE algorithm is used to oversample the samples according to the proportion of sub-clusters, to reduce the imbalance of intra-class data. On the other hand, Tomek Links data cleaning technology is used to correct the oversampled samples in time, the quality of synthetic samples is guaranteed by eliminating noise in data sets and overlapping samples generated by sampling methods. The training data set and the test data set used in this paper are both UCI data sets. The experimental results show that this algorithm can significantly improve the classification accuracy of a few classes, thus improving the classification performance of the classifier.
Fe–25Cr–35Ni-based alloy tensile deformation behaviour was studied through tensile test at strain rates of 5 × 10−4, 10−3, 5 × 10−3 and 10−2 s−1 from 900 to 1023°C in this paper. The flow stress has a good correlation with testing temperature and strain rate. TEM and SEM were used to analyse the microstructure evolution. The results show that DRV and DRX are the important factors for reducing the flow stress during hot tensile deformation. The occurrence of DRV and DRX was confirmed by TEM micrographs in high temperature tensile deformation. The hyperbolic sine-type equation with deformation activation energy and temperature describes Fe–25Cr–35Ni-based alloy hot tensile deformation, and the deformation activation energy is 5.24 × 102 kJ mol–1.
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