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An evaluation model based on multiple ultrasonic parameters was developed to control mean absolute error between an actual measured value and a model calculation value. Superalloy GH4169 was used to validate the presented model. A multi-dimensional ultrasonic parameter set was built for each sample by ultrasonic and nonlinear ultrasonic test and parameter calculation. The feature scale of different parameters was restricted to the same range using normalization. The dimensionality of the parameter set was descended by a correlation metric with grain size. A new one-dimensional ultrasonic characteristic parameter was comprised of the select normalized set of ultrasonic parameters by quadratic polynomial, exponential, Gauss kernel and linear mapping functions with undetermined coefficients. The relationship between the new ultrasonic multiple eigenvalues mapping parameter and estimated grain size was expressed using a linear fitting function. An optimization problem aimed at minimizing the mean absolute error between the estimated and quantitative measured grain size was solved using evolutionary algorithms to obtain coefficients for the mapping and fitting functions. These calculations were used to generate four full multi-parameter ultrasonic evaluation models, which were benchmarked against three single parameter evaluation models (i.e. velocity, attenuation coefficient, and backscattering). The performance analysis and verification using leave-one-out cross-validation demonstrate the utility of the MUE model through its high precision, good stabilization, super anti-interference and practicality.

This paper investigates magnetic field and stress-induced magnetic domain reorientation and its correlation with Magnetic Barkhausen Noise (MBN). The magnetic domain dynamics and MBN of grain-oriented transverse and longitudinal electrical steels are studied by using time-resolved magneto-optical imaging. Time and frequency-domain characteristics of MBN signal are investigated for evaluation of stress state and magnetocrystalline anisotropy. Transitions between magnetic domain states with differing tensile stress and magnetic anisotropy directly influence the MBN signal. Its shown that 180° domain wall (DW) reorganization and DW-type conversion generate peaks in the MBN measurement for longitudinal and transverse electrical steel, respectively. The magnetic domain imaging reveals complicated domain arrangement processes in transverse electrical steel. The results provide a physical insight into bulk micromagnetic phenomena.

Land surface temperature is an important indicator for characterizing the habitat conditions and ecological disturbances of ion-adsorbed rare earth mining areas. Because the ions in an ion-adsorbed type rare earth mining area are scattered and the area of a single mining site is small, it is important to obtain practical surface temperature data for monitoring the ecological environment of the area. In this study, Landsat-8 and Gaofen-1(GF-1) satellite images were used as data sources. In view of the characteristics of rare earth mining areas, and the thermal infrared data were used to retrieve the land surface temperature (LST), a land surface temperature downscaling model TDFSU based on remote sensing data fusion and mixed pixel decomposition is proposed. ultimately verifying the quantitative and qualitative accuracy of the downscaled results. The results show that the model can effectively improve the spatial resolution of surface temperature and reflect the spatial differences of surface temperature. This model has high feasibility and applicability in ionic rare earth mining areas.