The relation between deformation inhomogeneity and low-cycle-fatigue failure of T2 pure copper and the nickel-based superalloy GH4169 under symmetric tension-compression cyclic strain loading is investigated by using a polycrystal representative volume element (RVE) as the material model. The anisotropic behavior of grains and the strain fields are calculated by crystal plasticity, taking the Bauschinger effect into account to track the process of strain cycles of metals, and the Shannon’s differential entropies of both distributions of the strain in the loading direction and the first principal strain are employed at the tension peak of the cycles as measuring parameters of strain inhomogeneity. Both parameters are found to increase in value with increments in the number of cycles and they have critical values for predicting the material’s fatigue failure. Compared to the fatigue test data, it is verified that both parameters measured by Shannon’s differential entropies can be used as fatigue indicating parameters (FIPs) to predict the low cycle fatigue life of metal.

Feature selection is an essential process in the identification task because the irrelevant and redundant features contained in the unselected feature set can reduce both the performance and efficiency of recognition. However, when identifying the underwater targets based on their radiated noise, the diversity of targets, and the complexity of underwater acoustic channels introduce various complex relationships among the extracted acoustic features. For this problem, this paper employs the normalized maximum information coefficient (NMIC) to measure the correlations between features and categories and the redundancy among different features and further proposes an NMIC based feature selection method (NMIC-FS). Then, on the real-world dataset, the average classification accuracy estimated by models such as random forest and support vector machine is used to evaluate the performance of the NMIC-FS. The analysis results show that the feature subset obtained by NMIC-FS can achieve higher classification accuracy in a shorter time than that without selection. Compared with correlation-based feature selection, laplacian score, and lasso methods, the NMIC-FS improves the classification accuracy faster in the process of feature selection and requires the least acoustic features to obtain classification accuracy comparable to that of the full feature set.

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